Automated data update (2026-07-07)

data/adapter_stats.json

@@ -54,9 +54,9 @@
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   },
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-    "size_mb": 8.137319564819336,
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data/helm_capabilities/allenai/olmo-2-0325-32b-instruct/44928751-2bb3-4133-88e9-d5046058e32c.json

@@ -0,0 +1,238 @@
+{
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+  "retrieved_timestamp": "1783412566.378787",
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+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
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+    "version": "unknown"
+  },
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+    "id": "allenai/olmo-2-0325-32b-instruct",
+    "developer": "allenai",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
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+        "source_type": "url",
+        "url": [
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+        ]
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+        "source_type": "url",
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+        ]
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+        ]
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+}

data/helm_capabilities/allenai/olmo-2-1124-13b-instruct/6cefb540-ec18-468c-afc7-7f5143fec6d9.json

@@ -0,0 +1,238 @@
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+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
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+  "eval_library": {
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+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "OLMo 2 13B Instruct November 2024",
+    "id": "allenai/olmo-2-1124-13b-instruct",
+    "developer": "allenai",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
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+      "evaluation_name": "Mean score",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+      "generation_config": {
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+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
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+        "metric_name": "COT correct",
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+          "IFEval - # output tokens": "{\"description\": \"min=311.527, mean=311.527, max=311.527, sum=311.527 (1)\", \"tab\": \"General information\", \"score\": \"311.5268022181146\"}"
+        }
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+        "additional_details": {
+          "num_output_tokens": "\"2048\""
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+    {
+      "evaluation_name": "WildBench",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
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+        "evaluation_description": "WB Score on WildBench",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "WildBench - Observed inference time (s)": "{\"description\": \"min=194.337, mean=194.337, max=194.337, sum=194.337 (1)\", \"tab\": \"Efficiency\", \"score\": \"194.33703967285157\"}",
+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
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+        }
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+  ]
+}

data/helm_capabilities/allenai/olmo-2-1124-7b-instruct/67376c0e-33e7-420c-b278-2dcbe6525ccd.json

@@ -0,0 +1,238 @@
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+  "evaluation_id": "helm_capabilities/allenai_olmo-2-1124-7b-instruct/1783412566.378787",
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+    "source_name": "helm_capabilities",
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+    "evaluator_relationship": "third_party"
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+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "OLMo 2 7B Instruct November 2024",
+    "id": "allenai/olmo-2-1124-7b-instruct",
+    "developer": "allenai",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
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+        "source_type": "url",
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+        "source_type": "url",
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+          "MMLU-Pro - # output tokens": "{\"description\": \"min=265.659, mean=265.659, max=265.659, sum=265.659 (1)\", \"tab\": \"General information\", \"score\": \"265.659\"}"
+        }
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+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "GPQA - # output tokens": "{\"description\": \"min=381.121, mean=381.121, max=381.121, sum=381.121 (1)\", \"tab\": \"General information\", \"score\": \"381.1210762331838\"}"
+        }
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+          "use_few_shot": "\"false\"",
+          "num_output_tokens": "\"2048\""
+        }
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+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
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+        "score_type": "continuous",
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+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+        ]
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+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
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+        "min_score": 0.0,
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=768.348, mean=768.348, max=768.348, sum=768.348 (1)\", \"tab\": \"General information\", \"score\": \"768.348\"}"
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+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=799.769, mean=799.769, max=799.769, sum=799.769 (1)\", \"tab\": \"General information\", \"score\": \"799.769\"}"
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data/helm_capabilities/allenai/olmoe-1b-7b-0125-instruct/1695b0eb-2a17-4511-b21b-10426464fa12.json

@@ -0,0 +1,238 @@
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+  "source_metadata": {
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+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
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+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
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+  "model_info": {
+    "name": "OLMoE 1B-7B Instruct January 2025",
+    "id": "allenai/olmoe-1b-7b-0125-instruct",
+    "developer": "allenai",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
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+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        }
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+      "generation_config": {
+        "additional_details": {}
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+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=231.403, mean=231.403, max=231.403, sum=231.403 (1)\", \"tab\": \"General information\", \"score\": \"231.403\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=237.89, mean=237.89, max=237.89, sum=237.89 (1)\", \"tab\": \"General information\", \"score\": \"237.89\"}"
+        }
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+          "GPQA - Observed inference time (s)": "{\"description\": \"min=263.918, mean=263.918, max=263.918, sum=263.918 (1)\", \"tab\": \"Efficiency\", \"score\": \"263.9177615305768\"}",
+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "GPQA - # prompt tokens": "{\"description\": \"min=249.803, mean=249.803, max=249.803, sum=249.803 (1)\", \"tab\": \"General information\", \"score\": \"249.80269058295963\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=302.475, mean=302.475, max=302.475, sum=302.475 (1)\", \"tab\": \"General information\", \"score\": \"302.47533632286996\"}"
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+          "use_few_shot": "\"false\"",
+          "num_output_tokens": "\"2048\""
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+      "evaluation_name": "IFEval",
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+        "dataset_name": "IFEval",
+        "source_type": "url",
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+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+        }
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+        }
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+    },
+    {
+      "evaluation_name": "WildBench",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
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+        "min_score": 0.0,
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+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=972.482, mean=972.482, max=972.482, sum=972.482 (1)\", \"tab\": \"General information\", \"score\": \"972.482\"}"
+        }
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+        }
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+      "evaluation_name": "Omni-MATH",
+      "source_data": {
+        "dataset_name": "Omni-MATH",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
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+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
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+        "min_score": 0.0,
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+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0.001, mean=0.001, max=0.001, sum=0.001 (1)\", \"tab\": \"General information\", \"score\": \"0.001\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=110.864, mean=110.864, max=110.864, sum=110.864 (1)\", \"tab\": \"General information\", \"score\": \"110.864\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=442.229, mean=442.229, max=442.229, sum=442.229 (1)\", \"tab\": \"General information\", \"score\": \"442.229\"}"
+        }
+      },
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+    }
+  ]
+}

data/helm_capabilities/amazon/nova-lite-v1_0/1d75ecc1-4bc6-4830-8a71-b5c9d73942e6.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/amazon_nova-lite-v1:0/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Amazon Nova Lite",
+    "id": "amazon/nova-lite-v1:0",
+    "developer": "amazon",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        }
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+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "description": "min=0.6, mean=0.6, max=0.6, sum=0.6 (1)",
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+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=235.232, mean=235.232, max=235.232, sum=235.232 (1)\", \"tab\": \"General information\", \"score\": \"235.232\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=343.771, mean=343.771, max=343.771, sum=343.771 (1)\", \"tab\": \"General information\", \"score\": \"343.771\"}"
+        }
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+          "use_few_shot": "\"false\""
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+      "evaluation_name": "GPQA",
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+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - # prompt tokens": "{\"description\": \"min=264.121, mean=264.121, max=264.121, sum=264.121 (1)\", \"tab\": \"General information\", \"score\": \"264.1210762331838\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=512.256, mean=512.256, max=512.256, sum=512.256 (1)\", \"tab\": \"General information\", \"score\": \"512.2556053811659\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"gpqa_main\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "details": {
+          "description": "min=0.776, mean=0.776, max=0.776, sum=0.776 (1)",
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+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - # prompt tokens": "{\"description\": \"min=47.58, mean=47.58, max=47.58, sum=47.58 (1)\", \"tab\": \"General information\", \"score\": \"47.58040665434381\"}",
+          "IFEval - # output tokens": "{\"description\": \"min=412.706, mean=412.706, max=412.706, sum=412.706 (1)\", \"tab\": \"General information\", \"score\": \"412.70609981515713\"}"
+        }
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+      "evaluation_name": "WildBench",
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "WildBench - Observed inference time (s)": "{\"description\": \"min=4.034, mean=4.034, max=4.034, sum=4.034 (1)\", \"tab\": \"Efficiency\", \"score\": \"4.0338700000000065\"}",
+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=938.586, mean=938.586, max=938.586, sum=938.586 (1)\", \"tab\": \"General information\", \"score\": \"938.586\"}"
+        }
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+          "subset": "\"v2\""
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+    {
+      "evaluation_name": "Omni-MATH",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "details": {
+          "description": "min=0.233, mean=0.233, max=0.233, sum=0.233 (1)",
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+          "Omni-MATH - Observed inference time (s)": "{\"description\": \"min=2.418, mean=2.418, max=2.418, sum=2.418 (1)\", \"tab\": \"Efficiency\", \"score\": \"2.4179019999999993\"}",
+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=117.921, mean=117.921, max=117.921, sum=117.921 (1)\", \"tab\": \"General information\", \"score\": \"117.921\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=788.8, mean=788.8, max=788.8, sum=788.8 (1)\", \"tab\": \"General information\", \"score\": \"788.8\"}"
+        }
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+      "generation_config": {
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+    }
+  ]
+}

data/helm_capabilities/amazon/nova-micro-v1_0/9f52fca9-7502-46e4-b1c3-7d2b4b64bf16.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/amazon_nova-micro-v1:0/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Amazon Nova Micro",
+    "id": "amazon/nova-micro-v1:0",
+    "developer": "amazon",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
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+        "details": {
+          "description": "",
+          "tab": "Accuracy",
+          "Mean score - Efficiency": "{\"description\": \"\", \"tab\": \"Efficiency\", \"score\": \"2.157983343244118\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+        "score": 0.511,
+        "details": {
+          "description": "min=0.511, mean=0.511, max=0.511, sum=0.511 (1)",
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+          "MMLU-Pro - Observed inference time (s)": "{\"description\": \"min=1.316, mean=1.316, max=1.316, sum=1.316 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.3163370000000014\"}",
+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=235.232, mean=235.232, max=235.232, sum=235.232 (1)\", \"tab\": \"General information\", \"score\": \"235.232\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=367.695, mean=367.695, max=367.695, sum=367.695 (1)\", \"tab\": \"General information\", \"score\": \"367.695\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"all\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "details": {
+          "description": "min=0.383, mean=0.383, max=0.383, sum=0.383 (1)",
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+          "GPQA - Observed inference time (s)": "{\"description\": \"min=2.134, mean=2.134, max=2.134, sum=2.134 (1)\", \"tab\": \"Efficiency\", \"score\": \"2.1342376681614366\"}",
+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "GPQA - # prompt tokens": "{\"description\": \"min=264.121, mean=264.121, max=264.121, sum=264.121 (1)\", \"tab\": \"General information\", \"score\": \"264.1210762331838\"}",
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+        }
+      },
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
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+    },
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
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+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
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+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "IFEval - # output tokens": "{\"description\": \"min=385.473, mean=385.473, max=385.473, sum=385.473 (1)\", \"tab\": \"General information\", \"score\": \"385.4731977818854\"}"
+        }
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+      }
+    },
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+      "evaluation_name": "WildBench",
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+        ]
+      },
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+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
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+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=925.586, mean=925.586, max=925.586, sum=925.586 (1)\", \"tab\": \"General information\", \"score\": \"925.586\"}"
+        }
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+      "evaluation_name": "Omni-MATH",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "details": {
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+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=117.921, mean=117.921, max=117.921, sum=117.921 (1)\", \"tab\": \"General information\", \"score\": \"117.921\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=743.286, mean=743.286, max=743.286, sum=743.286 (1)\", \"tab\": \"General information\", \"score\": \"743.286\"}"
+        }
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+    }
+  ]
+}

data/helm_capabilities/amazon/nova-premier-v1_0/2bbae4d3-145e-43dd-84de-35641cc57646.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/amazon_nova-premier-v1:0/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Amazon Nova Premier",
+    "id": "amazon/nova-premier-v1:0",
+    "developer": "amazon",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
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+        "details": {
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+          "tab": "Accuracy",
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+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+          "description": "min=0.726, mean=0.726, max=0.726, sum=0.726 (1)",
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+          "MMLU-Pro - Observed inference time (s)": "{\"description\": \"min=5.032, mean=5.032, max=5.032, sum=5.032 (1)\", \"tab\": \"Efficiency\", \"score\": \"5.031505000000002\"}",
+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=235.232, mean=235.232, max=235.232, sum=235.232 (1)\", \"tab\": \"General information\", \"score\": \"235.232\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=360.651, mean=360.651, max=360.651, sum=360.651 (1)\", \"tab\": \"General information\", \"score\": \"360.651\"}"
+        }
+      },
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
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+      "evaluation_name": "GPQA",
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+        "dataset_name": "GPQA",
+        "source_type": "url",
+        "url": [
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+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "GPQA - # prompt tokens": "{\"description\": \"min=264.121, mean=264.121, max=264.121, sum=264.121 (1)\", \"tab\": \"General information\", \"score\": \"264.1210762331838\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=452.691, mean=452.691, max=452.691, sum=452.691 (1)\", \"tab\": \"General information\", \"score\": \"452.69058295964123\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"gpqa_main\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "score": 0.803,
+        "details": {
+          "description": "min=0.803, mean=0.803, max=0.803, sum=0.803 (1)",
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+          "IFEval - Observed inference time (s)": "{\"description\": \"min=6.027, mean=6.027, max=6.027, sum=6.027 (1)\", \"tab\": \"Efficiency\", \"score\": \"6.026593345656195\"}",
+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - # prompt tokens": "{\"description\": \"min=47.58, mean=47.58, max=47.58, sum=47.58 (1)\", \"tab\": \"General information\", \"score\": \"47.58040665434381\"}",
+          "IFEval - # output tokens": "{\"description\": \"min=325.945, mean=325.945, max=325.945, sum=325.945 (1)\", \"tab\": \"General information\", \"score\": \"325.9445471349353\"}"
+        }
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+      "generation_config": {
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+      }
+    },
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+      "evaluation_name": "WildBench",
+      "source_data": {
+        "dataset_name": "WildBench",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+      "score_details": {
+        "score": 0.788,
+        "details": {
+          "description": "min=0.788, mean=0.788, max=0.788, sum=0.788 (1)",
+          "tab": "Accuracy",
+          "WildBench - Observed inference time (s)": "{\"description\": \"min=13.055, mean=13.055, max=13.055, sum=13.055 (1)\", \"tab\": \"Efficiency\", \"score\": \"13.055127999999996\"}",
+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=814.969, mean=814.969, max=814.969, sum=814.969 (1)\", \"tab\": \"General information\", \"score\": \"814.969\"}"
+        }
+      },
+      "generation_config": {
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+          "subset": "\"v2\""
+        }
+      }
+    },
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+      "evaluation_name": "Omni-MATH",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+        "score": 0.35,
+        "details": {
+          "description": "min=0.35, mean=0.35, max=0.35, sum=0.35 (1)",
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+          "Omni-MATH - Observed inference time (s)": "{\"description\": \"min=8.169, mean=8.169, max=8.169, sum=8.169 (1)\", \"tab\": \"Efficiency\", \"score\": \"8.168997999999998\"}",
+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=117.921, mean=117.921, max=117.921, sum=117.921 (1)\", \"tab\": \"General information\", \"score\": \"117.921\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=778.909, mean=778.909, max=778.909, sum=778.909 (1)\", \"tab\": \"General information\", \"score\": \"778.909\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
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+    }
+  ]
+}

data/helm_capabilities/amazon/nova-pro-v1_0/c907e1f0-88f7-4afa-a595-bd6ab682d6b9.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/amazon_nova-pro-v1:0/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Amazon Nova Pro",
+    "id": "amazon/nova-pro-v1:0",
+    "developer": "amazon",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.591,
+        "details": {
+          "description": "",
+          "tab": "Accuracy",
+          "Mean score - Efficiency": "{\"description\": \"\", \"tab\": \"Efficiency\", \"score\": \"6.538285667967472\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.673,
+        "details": {
+          "description": "min=0.673, mean=0.673, max=0.673, sum=0.673 (1)",
+          "tab": "Accuracy",
+          "MMLU-Pro - Observed inference time (s)": "{\"description\": \"min=4.554, mean=4.554, max=4.554, sum=4.554 (1)\", \"tab\": \"Efficiency\", \"score\": \"4.554401999999996\"}",
+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=235.232, mean=235.232, max=235.232, sum=235.232 (1)\", \"tab\": \"General information\", \"score\": \"235.232\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=381.807, mean=381.807, max=381.807, sum=381.807 (1)\", \"tab\": \"General information\", \"score\": \"381.807\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"all\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
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data/helm_capabilities/anthropic/claude-3-5-haiku-20241022/abba9848-1847-4d30-8f3e-448d16d8ab89.json

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data/helm_capabilities/anthropic/claude-3-5-sonnet-20241022/b6423ca6-2e81-45ce-9d0a-0b00228beddb.json

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+        }
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+        }
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=397.573, mean=397.573, max=397.573, sum=397.573 (1)\", \"tab\": \"General information\", \"score\": \"397.573\"}"
+        }
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+}

data/helm_capabilities/anthropic/claude-3-7-sonnet-20250219/fbc3594c-0e17-4005-a260-06674d2b56a2.json

@@ -0,0 +1,231 @@
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+    "evaluator_relationship": "third_party"
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+    "id": "anthropic/claude-3-7-sonnet-20250219",
+    "developer": "anthropic",
+    "inference_platform": "unknown"
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+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
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+        }
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+          "use_few_shot": "\"false\""
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+        ]
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+        }
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data/helm_capabilities/anthropic/claude-haiku-4-5-20251001/76392ff8-c44f-4c61-bd5c-9963cc0581ce.json

@@ -0,0 +1,231 @@
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+    "evaluator_relationship": "third_party"
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+  "model_info": {
+    "name": "Claude 4.5 Haiku 20251001",
+    "id": "anthropic/claude-haiku-4-5-20251001",
+    "developer": "anthropic",
+    "inference_platform": "unknown"
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+      "source_data": {
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+        "source_type": "url",
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data/helm_capabilities/anthropic/claude-opus-4-20250514-thinking-10k/a15aaeae-ada8-4bf4-95e5-b43372aa58c0.json

@@ -0,0 +1,231 @@
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data/helm_capabilities/anthropic/claude-opus-4-20250514/0a6da2d9-a6b6-4d65-a15e-e823e6298ff4.json

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data/helm_capabilities/anthropic/claude-sonnet-4-20250514-thinking-10k/2a71e05a-e0d4-478d-b5a8-ae01d6480fbe.json

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+    "developer": "anthropic",
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+        "source_type": "url",
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+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=728.241, mean=728.241, max=728.241, sum=728.241 (1)\", \"tab\": \"General information\", \"score\": \"728.241\"}"
+        }
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data/helm_capabilities/anthropic/claude-sonnet-4-20250514/7aacf899-5e91-49c2-8ede-9dd8fe17f2a4.json

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+    "evaluator_relationship": "third_party"
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+    "developer": "anthropic",
+    "inference_platform": "unknown"
+  },
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+      "source_data": {
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+        "source_type": "url",
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data/helm_capabilities/anthropic/claude-sonnet-4-5-20250929/1fe7e88a-ee7c-4e51-a88a-9fa449a6af58.json

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+    "evaluator_relationship": "third_party"
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+    "id": "anthropic/claude-sonnet-4-5-20250929",
+    "developer": "anthropic",
+    "inference_platform": "unknown"
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+        "url": [
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+        "metric_name": "COT correct",
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+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "IFEval - # output tokens": "{\"description\": \"min=414.632, mean=414.632, max=414.632, sum=414.632 (1)\", \"tab\": \"General information\", \"score\": \"414.63216266173754\"}"
+        }
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+        "url": [
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+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
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data/helm_capabilities/deepseek-ai/deepseek-r1-0528/c8a091f7-ef1d-4310-b47c-870fa1a67450.json

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data/helm_capabilities/deepseek-ai/deepseek-v3/f8742598-1886-4669-b064-2ad2eada19a2.json

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+          "Omni-MATH - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}"
+        }
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+}

data/helm_capabilities/google/gemini-1.5-flash-002/ec193313-4254-4cc6-8caf-3118d19aa5fd.json

@@ -0,0 +1,231 @@
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+    "id": "google/gemini-1.5-flash-002",
+    "developer": "google",
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+        ]
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+        ]
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+          "use_few_shot": "\"false\""
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+      "evaluation_name": "IFEval",
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+        "dataset_name": "IFEval",
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+        ]
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+        "metric_name": "IFEval Strict Acc",
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data/helm_capabilities/google/gemini-1.5-pro-002/bfd26700-bffa-4ad2-aed8-e591781d3712.json

@@ -0,0 +1,231 @@
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+    "id": "google/gemini-1.5-pro-002",
+    "developer": "google",
+    "inference_platform": "unknown"
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+        ]
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+          "MMLU-Pro - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}"
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data/helm_capabilities/google/gemini-2.0-flash-001/4948e6f5-23b4-4a63-8a53-de268fd27643.json

@@ -0,0 +1,231 @@
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data/helm_capabilities/google/gemini-2.0-flash-lite-preview-02-05/694658f8-b061-4b87-8748-6d492470537e.json

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+    "id": "google/gemini-2.0-flash-lite-preview-02-05",
+    "developer": "google",
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+}

data/helm_capabilities/google/gemini-2.5-flash-lite/a2f307fe-0002-4206-8469-2147ecb4095f.json

@@ -0,0 +1,231 @@
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data/helm_capabilities/google/gemini-2.5-flash-preview-04-17/7b78d969-9a8f-4e00-838c-64b425dd3381.json

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+    "id": "google/gemini-2.5-flash-preview-04-17",
+    "developer": "google",
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+}

data/helm_capabilities/google/gemini-2.5-pro-preview-03-25/60866547-002a-4957-bc5f-d65f3861988d.json

@@ -0,0 +1,231 @@
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data/helm_capabilities/google/gemini-3-pro-preview/b313daa7-5ba3-426d-85c8-619b512bf391.json

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+    "id": "google/gemini-3-pro-preview",
+    "developer": "google",
+    "inference_platform": "unknown"
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+          "MMLU-Pro - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}"
+        }
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+          "use_few_shot": "\"false\""
+        }
+      }
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+    {
+      "evaluation_name": "GPQA",
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+        "dataset_name": "GPQA",
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+        "url": [
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+        ]
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+        "evaluation_description": "COT correct on GPQA",
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+          "GPQA - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}"
+        }
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+          "use_few_shot": "\"false\""
+        }
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+        ]
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}"
+        }
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+}

data/helm_capabilities/ibm/granite-3.3-8b-instruct/7ab7a645-9ae5-427b-99f8-380c1cf0fe93.json

@@ -0,0 +1,231 @@
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+    "evaluator_relationship": "third_party"
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+    "name": "IBM Granite 3.3 8B Instruct",
+    "id": "ibm/granite-3.3-8b-instruct",
+    "developer": "ibm",
+    "inference_platform": "unknown"
+  },
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+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        }
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+      }
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+    {
+      "evaluation_name": "MMLU-Pro",
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "MMLU-Pro - # output tokens": "{\"description\": \"min=364.376, mean=364.376, max=364.376, sum=364.376 (1)\", \"tab\": \"General information\", \"score\": \"364.376\"}"
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+        }
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
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+      }
+    },
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+      "evaluation_name": "IFEval",
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+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
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+        "min_score": 0.0,
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+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - # prompt tokens": "{\"description\": \"min=51.534, mean=51.534, max=51.534, sum=51.534 (1)\", \"tab\": \"General information\", \"score\": \"51.53419593345656\"}",
+          "IFEval - # output tokens": "{\"description\": \"min=482.37, mean=482.37, max=482.37, sum=482.37 (1)\", \"tab\": \"General information\", \"score\": \"482.36968576709796\"}"
+        }
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=784.893, mean=784.893, max=784.893, sum=784.893 (1)\", \"tab\": \"General information\", \"score\": \"784.893\"}"
+        }
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+        "source_type": "url",
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+        ]
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+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
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+        "details": {
+          "description": "min=0.176, mean=0.176, max=0.176, sum=0.176 (1)",
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+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=118.438, mean=118.438, max=118.438, sum=118.438 (1)\", \"tab\": \"General information\", \"score\": \"118.438\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=1162.421, mean=1162.421, max=1162.421, sum=1162.421 (1)\", \"tab\": \"General information\", \"score\": \"1162.421\"}"
+        }
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+}

data/helm_capabilities/ibm/granite-4.0-h-small/6d3f8c1b-b40c-412a-84d8-1ea90b04b8b3.json

@@ -0,0 +1,231 @@
+{
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+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
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+  "model_info": {
+    "name": "IBM Granite 4.0 Small",
+    "id": "ibm/granite-4.0-h-small",
+    "developer": "ibm",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
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data/helm_capabilities/ibm/granite-4.0-micro/3e791f72-7318-491d-816d-dfe4413eeb66.json

@@ -0,0 +1,231 @@
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+        }
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+        ]
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+        }
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+        "details": {
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+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=118.438, mean=118.438, max=118.438, sum=118.438 (1)\", \"tab\": \"General information\", \"score\": \"118.438\"}",
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+        }
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+}

data/helm_capabilities/marin-community/marin-8b-instruct/4945993b-685d-4357-bf66-4c73040c15f9.json

@@ -0,0 +1,238 @@
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data/helm_capabilities/meta/llama-3.1-405b-instruct-turbo/0a7bde1c-d9a7-4e79-9b5f-253cb8cda360.json

@@ -0,0 +1,231 @@
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+    "developer": "meta",
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+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.249,
+        "details": {
+          "description": "min=0.249, mean=0.249, max=0.249, sum=0.249 (1)",
+          "tab": "Accuracy",
+          "Omni-MATH - Observed inference time (s)": "{\"description\": \"min=10.588, mean=10.588, max=10.588, sum=10.588 (1)\", \"tab\": \"Efficiency\", \"score\": \"10.587890453577042\"}",
+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=109.708, mean=109.708, max=109.708, sum=109.708 (1)\", \"tab\": \"General information\", \"score\": \"109.708\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=906.902, mean=906.902, max=906.902, sum=906.902 (1)\", \"tab\": \"General information\", \"score\": \"906.902\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    }
+  ]
+}

data/helm_capabilities/meta/llama-3.1-70b-instruct-turbo/39a41152-ada9-49cf-8490-45724f6a8690.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/meta_llama-3.1-70b-instruct-turbo/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Llama 3.1 Instruct Turbo 70B",
+    "id": "meta/llama-3.1-70b-instruct-turbo",
+    "developer": "meta",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.574,
+        "details": {
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+          "tab": "Accuracy",
+          "Mean score - Efficiency": "{\"description\": \"\", \"tab\": \"Efficiency\", \"score\": \"4.2482479944372376\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+          "description": "min=0.653, mean=0.653, max=0.653, sum=0.653 (1)",
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+          "MMLU-Pro - Observed inference time (s)": "{\"description\": \"min=2.732, mean=2.732, max=2.732, sum=2.732 (1)\", \"tab\": \"Efficiency\", \"score\": \"2.7317132804393767\"}",
+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "MMLU-Pro - # output tokens": "{\"description\": \"min=326.226, mean=326.226, max=326.226, sum=326.226 (1)\", \"tab\": \"General information\", \"score\": \"326.226\"}"
+        }
+      },
+      "generation_config": {
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
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+      "evaluation_name": "GPQA",
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+        "dataset_name": "GPQA",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+        "details": {
+          "description": "min=0.426, mean=0.426, max=0.426, sum=0.426 (1)",
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+          "GPQA - Observed inference time (s)": "{\"description\": \"min=6.095, mean=6.095, max=6.095, sum=6.095 (1)\", \"tab\": \"Efficiency\", \"score\": \"6.0952357684550265\"}",
+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - # prompt tokens": "{\"description\": \"min=248.886, mean=248.886, max=248.886, sum=248.886 (1)\", \"tab\": \"General information\", \"score\": \"248.88565022421525\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=491.435, mean=491.435, max=491.435, sum=491.435 (1)\", \"tab\": \"General information\", \"score\": \"491.43497757847535\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"gpqa_main\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
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+        "details": {
+          "description": "min=0.821, mean=0.821, max=0.821, sum=0.821 (1)",
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+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - # prompt tokens": "{\"description\": \"min=46.024, mean=46.024, max=46.024, sum=46.024 (1)\", \"tab\": \"General information\", \"score\": \"46.024029574861366\"}",
+          "IFEval - # output tokens": "{\"description\": \"min=361.464, mean=361.464, max=361.464, sum=361.464 (1)\", \"tab\": \"General information\", \"score\": \"361.46395563770795\"}"
+        }
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+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "WildBench",
+      "source_data": {
+        "dataset_name": "WildBench",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+      "score_details": {
+        "score": 0.758,
+        "details": {
+          "description": "min=0.758, mean=0.758, max=0.758, sum=0.758 (1)",
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+          "WildBench - Observed inference time (s)": "{\"description\": \"min=4.143, mean=4.143, max=4.143, sum=4.143 (1)\", \"tab\": \"Efficiency\", \"score\": \"4.142627255439758\"}",
+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=808.109, mean=808.109, max=808.109, sum=808.109 (1)\", \"tab\": \"General information\", \"score\": \"808.109\"}"
+        }
+      },
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+          "subset": "\"v2\""
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+    },
+    {
+      "evaluation_name": "Omni-MATH",
+      "source_data": {
+        "dataset_name": "Omni-MATH",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+      "score_details": {
+        "score": 0.21,
+        "details": {
+          "description": "min=0.21, mean=0.21, max=0.21, sum=0.21 (1)",
+          "tab": "Accuracy",
+          "Omni-MATH - Observed inference time (s)": "{\"description\": \"min=5.649, mean=5.649, max=5.649, sum=5.649 (1)\", \"tab\": \"Efficiency\", \"score\": \"5.649449105501175\"}",
+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=109.708, mean=109.708, max=109.708, sum=109.708 (1)\", \"tab\": \"General information\", \"score\": \"109.708\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=1321.301, mean=1321.301, max=1321.301, sum=1321.301 (1)\", \"tab\": \"General information\", \"score\": \"1321.301\"}"
+        }
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+        "additional_details": {}
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+    }
+  ]
+}

data/helm_capabilities/meta/llama-3.1-8b-instruct-turbo/1eae1af2-eb16-41dc-aaea-8bcd310b97e1.json

@@ -0,0 +1,231 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "helm_capabilities/meta_llama-3.1-8b-instruct-turbo/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Llama 3.1 Instruct Turbo 8B",
+    "id": "meta/llama-3.1-8b-instruct-turbo",
+    "developer": "meta",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.444,
+        "details": {
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+          "tab": "Accuracy",
+          "Mean score - Efficiency": "{\"description\": \"\", \"tab\": \"Efficiency\", \"score\": \"3.654367387500005\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.406,
+        "details": {
+          "description": "min=0.406, mean=0.406, max=0.406, sum=0.406 (1)",
+          "tab": "Accuracy",
+          "MMLU-Pro - Observed inference time (s)": "{\"description\": \"min=2.642, mean=2.642, max=2.642, sum=2.642 (1)\", \"tab\": \"Efficiency\", \"score\": \"2.6422129917144774\"}",
+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=228.366, mean=228.366, max=228.366, sum=228.366 (1)\", \"tab\": \"General information\", \"score\": \"228.366\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=518.387, mean=518.387, max=518.387, sum=518.387 (1)\", \"tab\": \"General information\", \"score\": \"518.387\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"all\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+        "details": {
+          "description": "min=0.247, mean=0.247, max=0.247, sum=0.247 (1)",
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+          "GPQA - Observed inference time (s)": "{\"description\": \"min=3.28, mean=3.28, max=3.28, sum=3.28 (1)\", \"tab\": \"Efficiency\", \"score\": \"3.2803654104070277\"}",
+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - # prompt tokens": "{\"description\": \"min=248.886, mean=248.886, max=248.886, sum=248.886 (1)\", \"tab\": \"General information\", \"score\": \"248.88565022421525\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=744.583, mean=744.583, max=744.583, sum=744.583 (1)\", \"tab\": \"General information\", \"score\": \"744.5829596412556\"}"
+        }
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+      "generation_config": {
+        "additional_details": {
+          "subset": "\"gpqa_main\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        "details": {
+          "description": "min=0.743, mean=0.743, max=0.743, sum=0.743 (1)",
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+          "IFEval - Observed inference time (s)": "{\"description\": \"min=1.982, mean=1.982, max=1.982, sum=1.982 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.981573561423367\"}",
+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "IFEval - # prompt tokens": "{\"description\": \"min=46.024, mean=46.024, max=46.024, sum=46.024 (1)\", \"tab\": \"General information\", \"score\": \"46.024029574861366\"}",
+          "IFEval - # output tokens": "{\"description\": \"min=404.026, mean=404.026, max=404.026, sum=404.026 (1)\", \"tab\": \"General information\", \"score\": \"404.02587800369685\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "WildBench",
+      "source_data": {
+        "dataset_name": "WildBench",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.686,
+        "details": {
+          "description": "min=0.686, mean=0.686, max=0.686, sum=0.686 (1)",
+          "tab": "Accuracy",
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+        "source_type": "url",
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+        ]
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=2170.057, mean=2170.057, max=2170.057, sum=2170.057 (1)\", \"tab\": \"General information\", \"score\": \"2170.057\"}"
+        }
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+  ]
+}

data/helm_capabilities/meta/llama-4-maverick-17b-128e-instruct-fp8/c0134187-a08f-4731-af00-9828fc5d8f51.json

@@ -0,0 +1,231 @@
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+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
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+    "version": "unknown"
+  },
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+    "name": "Llama 4 Maverick 17Bx128E Instruct FP8",
+    "id": "meta/llama-4-maverick-17b-128e-instruct-fp8",
+    "developer": "meta",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "COT correct on MMLU-Pro",
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+        }
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+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+        }
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
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+      "evaluation_name": "IFEval",
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+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
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+        "metric_name": "IFEval Strict Acc",
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+        }
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
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+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=842.777, mean=842.777, max=842.777, sum=842.777 (1)\", \"tab\": \"General information\", \"score\": \"842.777\"}"
+        }
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+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=1055.205, mean=1055.205, max=1055.205, sum=1055.205 (1)\", \"tab\": \"General information\", \"score\": \"1055.205\"}"
+        }
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+}

data/helm_capabilities/meta/llama-4-scout-17b-16e-instruct/336832eb-1059-4911-93f3-53d0e1c14d37.json

@@ -0,0 +1,231 @@
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+  "evaluation_id": "helm_capabilities/meta_llama-4-scout-17b-16e-instruct/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Llama 4 Scout 17Bx16E Instruct",
+    "id": "meta/llama-4-scout-17b-16e-instruct",
+    "developer": "meta",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+        "score": 0.644,
+        "details": {
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+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+          "description": "min=0.742, mean=0.742, max=0.742, sum=0.742 (1)",
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+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=225.585, mean=225.585, max=225.585, sum=225.585 (1)\", \"tab\": \"General information\", \"score\": \"225.585\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=550.212, mean=550.212, max=550.212, sum=550.212 (1)\", \"tab\": \"General information\", \"score\": \"550.212\"}"
+        }
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+      "generation_config": {
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+          "subset": "\"all\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
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+        "url": [
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+        ]
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+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "GPQA - # prompt tokens": "{\"description\": \"min=236.807, mean=236.807, max=236.807, sum=236.807 (1)\", \"tab\": \"General information\", \"score\": \"236.8071748878924\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=856.76, mean=856.76, max=856.76, sum=856.76 (1)\", \"tab\": \"General information\", \"score\": \"856.7600896860987\"}"
+        }
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+        "additional_details": {
+          "subset": "\"gpqa_main\"",
+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
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+      "evaluation_name": "IFEval",
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+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
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+        "score_type": "continuous",
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+        "max_score": 1.0
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+        }
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+        }
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+        "source_type": "url",
+        "url": [
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+        ]
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+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
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+        "score_type": "continuous",
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+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=105.286, mean=105.286, max=105.286, sum=105.286 (1)\", \"tab\": \"General information\", \"score\": \"105.286\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=1088.449, mean=1088.449, max=1088.449, sum=1088.449 (1)\", \"tab\": \"General information\", \"score\": \"1088.449\"}"
+        }
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+  ]
+}

data/helm_capabilities/mistralai/mistral-7b-instruct-v0.3/7e43cb8b-5981-4b56-b330-d7c7b3838d79.json

@@ -0,0 +1,231 @@
+{
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+  "evaluation_id": "helm_capabilities/mistralai_mistral-7b-instruct-v0.3/1783412566.378787",
+  "retrieved_timestamp": "1783412566.378787",
+  "source_metadata": {
+    "source_name": "helm_capabilities",
+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "helm",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Mistral Instruct v0.3 7B",
+    "id": "mistralai/mistral-7b-instruct-v0.3",
+    "developer": "mistralai",
+    "inference_platform": "unknown"
+  },
+  "evaluation_results": [
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+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+    },
+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
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+        "max_score": 1.0
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+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=260.915, mean=260.915, max=260.915, sum=260.915 (1)\", \"tab\": \"General information\", \"score\": \"260.915\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=272.103, mean=272.103, max=272.103, sum=272.103 (1)\", \"tab\": \"General information\", \"score\": \"272.103\"}"
+        }
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+          "use_few_shot": "\"false\""
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+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+          "GPQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "GPQA - # prompt tokens": "{\"description\": \"min=281.998, mean=281.998, max=281.998, sum=281.998 (1)\", \"tab\": \"General information\", \"score\": \"281.99775784753365\"}",
+          "GPQA - # output tokens": "{\"description\": \"min=387.971, mean=387.971, max=387.971, sum=387.971 (1)\", \"tab\": \"General information\", \"score\": \"387.9708520179372\"}"
+        }
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+          "use_chain_of_thought": "\"true\"",
+          "use_few_shot": "\"false\""
+        }
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+    },
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+      "evaluation_name": "IFEval",
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+        "dataset_name": "IFEval",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+      "metric_config": {
+        "evaluation_description": "IFEval Strict Acc on IFEval",
+        "metric_name": "IFEval Strict Acc",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "description": "min=0.567, mean=0.567, max=0.567, sum=0.567 (1)",
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+          "IFEval - # eval": "{\"description\": \"min=541, mean=541, max=541, sum=541 (1)\", \"tab\": \"General information\", \"score\": \"541.0\"}",
+          "IFEval - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "IFEval - # output tokens": "{\"description\": \"min=449.725, mean=449.725, max=449.725, sum=449.725 (1)\", \"tab\": \"General information\", \"score\": \"449.72458410351203\"}"
+        }
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+        "source_type": "url",
+        "url": [
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+        ]
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+        "evaluation_description": "WB Score on WildBench",
+        "metric_name": "WB Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=702.754, mean=702.754, max=702.754, sum=702.754 (1)\", \"tab\": \"General information\", \"score\": \"702.754\"}"
+        }
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+        "source_type": "url",
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+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "Acc on Omni-MATH",
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+          "description": "min=0.072, mean=0.072, max=0.072, sum=0.072 (1)",
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+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=119.373, mean=119.373, max=119.373, sum=119.373 (1)\", \"tab\": \"General information\", \"score\": \"119.373\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=678.438, mean=678.438, max=678.438, sum=678.438 (1)\", \"tab\": \"General information\", \"score\": \"678.438\"}"
+        }
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+}

data/helm_capabilities/mistralai/mistral-large-2411/52316b5e-024b-47f8-9129-8c24917366d7.json

@@ -0,0 +1,231 @@
+{
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+  "retrieved_timestamp": "1783412566.378787",
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+    "source_type": "documentation",
+    "source_organization_name": "crfm",
+    "evaluator_relationship": "third_party"
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+  "eval_library": {
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+    "version": "unknown"
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+  "model_info": {
+    "name": "Mistral Large 2411",
+    "id": "mistralai/mistral-large-2411",
+    "developer": "mistralai",
+    "inference_platform": "unknown"
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+  "evaluation_results": [
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+      "evaluation_name": "Mean score",
+      "source_data": {
+        "dataset_name": "helm_capabilities",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "The mean of the scores from all columns.",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+        }
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+      "generation_config": {
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+    {
+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
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+          "MMLU-Pro - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "MMLU-Pro - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=260.915, mean=260.915, max=260.915, sum=260.915 (1)\", \"tab\": \"General information\", \"score\": \"260.915\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=316.273, mean=316.273, max=316.273, sum=316.273 (1)\", \"tab\": \"General information\", \"score\": \"316.273\"}"
+        }
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+          "use_few_shot": "\"false\""
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+        "evaluation_description": "COT correct on GPQA",
+        "metric_name": "COT correct",
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+        "score_type": "continuous",
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+        "max_score": 1.0
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+          "GPQA - # eval": "{\"description\": \"min=446, mean=446, max=446, sum=446 (1)\", \"tab\": \"General information\", \"score\": \"446.0\"}",
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+          "GPQA - # output tokens": "{\"description\": \"min=507.357, mean=507.357, max=507.357, sum=507.357 (1)\", \"tab\": \"General information\", \"score\": \"507.3565022421525\"}"
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+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=727.801, mean=727.801, max=727.801, sum=727.801 (1)\", \"tab\": \"General information\", \"score\": \"727.801\"}"
+        }
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+}

data/helm_capabilities/mistralai/mistral-small-2503/c8985ce0-59b1-416a-a0d1-bdc063ee9618.json

@@ -0,0 +1,231 @@
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+    "evaluator_relationship": "third_party"
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+    "id": "mistralai/mistral-small-2503",
+    "developer": "mistralai",
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        }
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+      "evaluation_name": "IFEval",
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+        ]
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+        "metric_name": "IFEval Strict Acc",
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+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "Omni-MATH - # output tokens": "{\"description\": \"min=753.657, mean=753.657, max=753.657, sum=753.657 (1)\", \"tab\": \"General information\", \"score\": \"753.657\"}"
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data/helm_capabilities/mistralai/mixtral-8x22b-instruct-v0.1/4f18237b-9e02-47d7-9e48-8643d0a25cf7.json

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+    "evaluator_relationship": "third_party"
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+  "model_info": {
+    "name": "Mixtral Instruct 8x22B",
+    "id": "mistralai/mixtral-8x22b-instruct-v0.1",
+    "developer": "mistralai",
+    "inference_platform": "unknown"
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+      "evaluation_name": "Mean score",
+      "source_data": {
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+        "source_type": "url",
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+        "score_type": "continuous",
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+      "evaluation_name": "MMLU-Pro",
+      "source_data": {
+        "dataset_name": "MMLU-Pro",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
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+        "evaluation_description": "COT correct on MMLU-Pro",
+        "metric_name": "COT correct",
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
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+          "MMLU-Pro - # prompt tokens": "{\"description\": \"min=260.915, mean=260.915, max=260.915, sum=260.915 (1)\", \"tab\": \"General information\", \"score\": \"260.915\"}",
+          "MMLU-Pro - # output tokens": "{\"description\": \"min=298.159, mean=298.159, max=298.159, sum=298.159 (1)\", \"tab\": \"General information\", \"score\": \"298.159\"}"
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+        "metric_name": "COT correct",
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+        "score_type": "continuous",
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data/helm_capabilities/mistralai/mixtral-8x7b-instruct-v0.1/d5b83e76-556a-4276-8e9d-e6e165801a23.json

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+    "evaluator_relationship": "third_party"
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data/helm_capabilities/moonshotai/kimi-k2-instruct/0c9fe544-14b9-4677-980e-c42e838eb627.json

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+    "id": "moonshotai/kimi-k2-instruct",
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+        }
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+        }
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data/helm_capabilities/openai/gpt-4.1-2025-04-14/7b2fd4ad-030c-4fc4-8e44-6de6873e1a65.json

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+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
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data/helm_capabilities/openai/gpt-4.1-mini-2025-04-14/be081e15-fa71-4247-a66d-31e8c795e711.json

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data/helm_capabilities/openai/gpt-4.1-nano-2025-04-14/bb06c44e-4497-4913-81a9-63d04afa6549.json

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+}

data/helm_capabilities/openai/gpt-4o-2024-11-20/67d75534-3bab-4703-bc46-a5ba180419bf.json

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data/helm_capabilities/openai/gpt-4o-mini-2024-07-18/3a5b50f1-bb99-4173-8192-db0b6c9d2359.json

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data/helm_capabilities/openai/gpt-5-2025-08-07/fed146f5-1d61-4980-a4e2-04e7dba729ce.json

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data/helm_capabilities/openai/gpt-5-mini-2025-08-07/db321197-7b83-475c-af3a-fbcb8623df4a.json

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+        "source_type": "url",
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+        "evaluation_description": "WB Score on WildBench",
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+          "WildBench - Observed inference time (s)": "{\"description\": \"min=33.854, mean=33.854, max=33.854, sum=33.854 (1)\", \"tab\": \"Efficiency\", \"score\": \"33.85394237089157\"}",
+          "WildBench - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "WildBench - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # prompt tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "WildBench - # output tokens": "{\"description\": \"min=1408.024, mean=1408.024, max=1408.024, sum=1408.024 (1)\", \"tab\": \"General information\", \"score\": \"1408.024\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {
+          "subset": "\"v2\""
+        }
+      }
+    },
+    {
+      "evaluation_name": "Omni-MATH",
+      "source_data": {
+        "dataset_name": "Omni-MATH",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "Acc on Omni-MATH",
+        "metric_name": "Acc",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0
+      },
+      "score_details": {
+        "score": 0.722,
+        "details": {
+          "description": "min=0.722, mean=0.722, max=0.722, sum=0.722 (1)",
+          "tab": "Accuracy",
+          "Omni-MATH - Observed inference time (s)": "{\"description\": \"min=55.806, mean=55.806, max=55.806, sum=55.806 (1)\", \"tab\": \"Efficiency\", \"score\": \"55.806421615123746\"}",
+          "Omni-MATH - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}",
+          "Omni-MATH - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
+          "Omni-MATH - # prompt tokens": "{\"description\": \"min=109.623, mean=109.623, max=109.623, sum=109.623 (1)\", \"tab\": \"General information\", \"score\": \"109.623\"}",
+          "Omni-MATH - # output tokens": "{\"description\": \"min=362.654, mean=362.654, max=362.654, sum=362.654 (1)\", \"tab\": \"General information\", \"score\": \"362.654\"}"
+        }
+      },
+      "generation_config": {
+        "additional_details": {}
+      }
+    }
+  ]
+}