Awesome AI Index
Machine-readable JSON. No paywalls. Updated daily via GitHub Actions.
Curated catalog of AI tools, models, papers, frameworks, and resources for engineers and researchers.
Contents
What's Inside
Why This Exists
No single open-source repository covers the full AI ecosystem stack:
- Models with real benchmark scores (MMLU, GPQA Diamond, HumanEval, SWE-bench)
- Vendors with HQ, founding year, licensing, EU AI Act risk tier
- Benchmarks with methodology, saturation signals, and citation counts
- Compliance mapping (EU AI Act, NIST AI RMF, ISO 42001, NTIA SBOM)
This repo is that missing layer.
Quick Start
curl https://raw.githubusercontent.com/alpha-one-index/awesome-ai-index/main/data/models/models.json
curl https://raw.githubusercontent.com/alpha-one-index/awesome-ai-index/main/data/vendors/vendors.json
curl https://raw.githubusercontent.com/alpha-one-index/awesome-ai-index/main/data/benchmarks/benchmarks.json
import requests
models = requests.get(
"https://raw.githubusercontent.com/alpha-one-index/awesome-ai-index/main/data/models/models.json"
).json()
open_models = [m for m in models if m.get("license") != "Proprietary" and m.get("mmlu", 0) > 80]
print(f"Found {len(open_models)} open-source models with MMLU > 80")
Latest Daily Additions
Populated automatically every night by the daily workflow.
- New arXiv papers (cs.AI)
- Trending Hugging Face models
- LLM leaderboard updates
(Full details are appended to data/ and ai-index.json — scroll to the category tables below for the latest entries.)
Top Open Models
Click to expand — 30+ open-weight models ranked by performance
| Model |
Vendor |
Parameters |
MMLU |
GPQA Diamond |
HumanEval |
License |
Release |
| Qwen 3.5 |
Alibaba |
72B |
88.4 |
88.4 |
92.1 |
Apache-2.0 |
2026-02 |
| DeepSeek R1 |
DeepSeek |
671B MoE |
90.8 |
71.5 |
89.2 |
MIT |
2025-01 |
| Llama 4 Scout |
Meta |
109B MoE |
84.2 |
74.2 |
87.4 |
Llama 4 |
2025-04 |
| Llama 4 Maverick |
Meta |
400B MoE |
88.3 |
78.5 |
91.1 |
Llama 4 |
2025-04 |
| Mistral Large 3 |
Mistral AI |
123B |
86.5 |
68.0 |
88.7 |
MRL-0.1 |
2025-03 |
| Gemma 3 27B |
Google |
27B |
82.1 |
62.4 |
84.5 |
Gemma |
2025-03 |
| Command R+ |
Cohere |
104B |
81.5 |
58.2 |
79.3 |
CC-BY-NC-4.0 |
2024-04 |
| Phi-4 |
Microsoft |
14B |
84.8 |
56.1 |
82.6 |
MIT |
2024-12 |
| DBRX |
Databricks |
132B MoE |
73.7 |
45.2 |
70.1 |
Databricks Open |
2024-03 |
| Yi-Lightning |
01.AI |
200B MoE |
82.0 |
55.8 |
80.4 |
Apache-2.0 |
2024-11 |
| Falcon-180B |
TII |
180B |
70.5 |
38.1 |
65.3 |
Falcon-180B TII |
2023-09 |
| Mixtral 8x22B |
Mistral AI |
176B MoE |
77.8 |
45.6 |
75.2 |
Apache-2.0 |
2024-04 |
| OLMo 2 |
AI2 |
32B |
75.4 |
42.1 |
72.8 |
Apache-2.0 |
2025-02 |
| StarCoder2 |
BigCode |
15B |
- |
- |
46.3 |
BigCode OpenRAIL-M |
2024-02 |
| Jamba 1.5 |
AI21 Labs |
398B MoE |
80.2 |
52.4 |
78.1 |
Jamba Open |
2024-08 |
| InternLM3 |
Shanghai AI Lab |
8B |
77.3 |
48.5 |
76.4 |
Apache-2.0 |
2025-01 |
| MAP-Neo |
M-A-P |
7B |
58.2 |
32.1 |
45.6 |
Apache-2.0 |
2024-05 |
| Sailor2 |
Sea AI Lab |
20B |
68.5 |
38.4 |
62.1 |
Apache-2.0 |
2024-12 |
| SmolLM2 |
HuggingFace |
1.7B |
55.1 |
28.3 |
42.5 |
Apache-2.0 |
2024-11 |
| Granite 3.1 |
IBM |
8B |
72.8 |
42.1 |
68.4 |
Apache-2.0 |
2024-12 |
| Nemotron-4 |
NVIDIA |
340B |
78.7 |
50.3 |
76.2 |
NVIDIA Open |
2024-06 |
| Grok-1 |
xAI |
314B MoE |
73.0 |
40.2 |
63.2 |
Apache-2.0 |
2024-03 |
| Solar |
Upstage |
10.7B |
66.2 |
35.4 |
58.1 |
Apache-2.0 |
2023-12 |
| Baichuan 4 |
Baichuan |
70B |
78.5 |
48.2 |
74.3 |
Baichuan |
2024-10 |
| Qwen 2.5 Coder |
Alibaba |
32B |
- |
- |
65.9 |
Apache-2.0 |
2024-11 |
| CodeLlama |
Meta |
70B |
- |
- |
67.8 |
Llama 2 |
2023-08 |
| Arctic |
Snowflake |
480B MoE |
67.3 |
36.8 |
64.5 |
Apache-2.0 |
2024-04 |
| WizardLM-2 |
Microsoft |
8x22B |
75.2 |
44.1 |
73.8 |
Llama 2 |
2024-04 |
| Zephyr |
HuggingFace |
7B |
61.4 |
32.5 |
55.2 |
MIT |
2023-10 |
| TinyLlama |
Community |
1.1B |
25.3 |
12.1 |
18.4 |
Apache-2.0 |
2024-01 |
Full dataset: data/models/models.json
Top Proprietary Models
Click to expand — Leading closed-source models
| Model |
Vendor |
Arena Score |
MMLU |
GPQA Diamond |
Context |
Pricing (1M tokens) |
| Claude Opus 4.6 |
Anthropic |
2002 |
91.5 |
91.5 |
200K |
$15 / $75 |
| Gemini 3.1 Pro |
Google |
1855 |
90.8 |
90.8 |
2M |
$1.25 / $5 |
| GPT-5.4 |
OpenAI |
1665 |
92.0 |
92.0 |
128K |
$5 / $15 |
| Kimi K2.5 |
Moonshot AI |
1447 |
87.6 |
87.6 |
128K |
$0.80 / $2.40 |
| Claude 3.5 Sonnet |
Anthropic |
1285 |
88.7 |
65.0 |
200K |
$3 / $15 |
| Gemini 1.5 Pro |
Google |
1280 |
86.5 |
59.1 |
2M |
$1.25 / $5 |
| GPT-4o |
OpenAI |
1248 |
88.7 |
53.6 |
128K |
$2.50 / $10 |
| o3-mini |
OpenAI |
1300 |
87.2 |
79.7 |
200K |
$1.10 / $4.40 |
| Grok 3 |
xAI |
1402 |
88.1 |
81.2 |
128K |
$3 / $15 |
| Reka Core |
Reka |
1185 |
82.4 |
48.5 |
128K |
$3 / $15 |
Agent Frameworks
Click to expand — Tools for building autonomous AI agents
| Framework |
Stars |
Language |
Key Features |
License |
| LangGraph |
8.5K+ |
Python |
Stateful multi-agent workflows, cycles, persistence |
MIT |
| CrewAI |
25K+ |
Python |
Role-based agents, task delegation, tool use |
MIT |
| AutoGen |
38K+ |
Python |
Multi-agent conversation, code execution |
CC-BY-4.0 |
| OpenAI Swarm |
18K+ |
Python |
Lightweight multi-agent orchestration |
MIT |
| Semantic Kernel |
23K+ |
C#/Python |
Enterprise AI orchestration, plugins |
MIT |
| Haystack |
18K+ |
Python |
LLM pipelines, RAG, agents |
Apache-2.0 |
| Pydantic AI |
8K+ |
Python |
Type-safe agent framework |
MIT |
| Agno |
20K+ |
Python |
Lightweight agent toolkit |
Apache-2.0 |
| Camel |
6K+ |
Python |
Communicative agents, role-playing |
Apache-2.0 |
| MetaGPT |
48K+ |
Python |
Multi-agent meta-programming |
MIT |
| BabyAGI |
20K+ |
Python |
Task-driven autonomous agent |
MIT |
| SuperAGI |
16K+ |
Python |
Open-source AGI framework |
MIT |
| ChatDev |
26K+ |
Python |
Virtual software company agents |
Apache-2.0 |
| Langroid |
3K+ |
Python |
Multi-agent LLM programming |
MIT |
| Atomic Agents |
2K+ |
Python |
Modular agent components |
MIT |
RAG Frameworks & Tools
Click to expand — Retrieval-Augmented Generation ecosystem
| Tool |
Stars |
Focus |
Key Features |
License |
| LlamaIndex |
38K+ |
Python |
Data connectors, indices, query engines |
MIT |
| LangChain |
100K+ |
Python/JS |
Chains, agents, RAG pipelines |
MIT |
| Haystack |
18K+ |
Python |
Production RAG pipelines |
Apache-2.0 |
| RAGFlow |
35K+ |
Python |
Deep document understanding RAG |
Apache-2.0 |
| Verba |
6K+ |
Python |
RAG chatbot with Weaviate |
BSD-3 |
| Embedchain |
10K+ |
Python |
RAG framework for any data source |
Apache-2.0 |
| PrivateGPT |
55K+ |
Python |
Private RAG with local LLMs |
Apache-2.0 |
| Vanna |
12K+ |
Python |
RAG for SQL databases |
MIT |
| R2R |
4K+ |
Python |
Production-ready RAG engine |
MIT |
| Cognita |
4K+ |
Python |
Open-source RAG framework |
Apache-2.0 |
| FlashRAG |
2K+ |
Python |
RAG benchmark toolkit |
MIT |
| Canopy |
1K+ |
Python |
RAG with Pinecone |
Apache-2.0 |
Fine-Tuning Tools
Click to expand — Tools for customizing and fine-tuning LLMs
| Tool |
Stars |
Focus |
License |
| Unsloth |
25K+ |
2x faster fine-tuning, 80% less memory |
Apache-2.0 |
| Axolotl |
8K+ |
Multi-GPU fine-tuning framework |
Apache-2.0 |
| LLaMA-Factory |
42K+ |
Easy fine-tuning for 100+ LLMs |
Apache-2.0 |
| PEFT |
17K+ |
Parameter-efficient fine-tuning (LoRA, QLoRA) |
Apache-2.0 |
| TRL |
11K+ |
RLHF, DPO, PPO training |
Apache-2.0 |
| Lit-GPT |
11K+ |
Pretrain, fine-tune, deploy 20+ LLMs |
Apache-2.0 |
| Ludwig |
11K+ |
Declarative deep learning framework |
Apache-2.0 |
| Mergekit |
5K+ |
Model merging toolkit |
LGPL-3.0 |
| Torchtune |
5K+ |
PyTorch-native fine-tuning |
BSD-3 |
| Liger Kernel |
4K+ |
Efficient Triton kernels for LLM training |
BSD-2 |
Inference Optimization
Click to expand — Tools for fast, efficient LLM inference
| Tool |
Stars |
Focus |
License |
| vLLM |
42K+ |
High-throughput LLM serving with PagedAttention |
Apache-2.0 |
| llama.cpp |
75K+ |
CPU/GPU inference in C/C++ |
MIT |
| Ollama |
110K+ |
Run LLMs locally with one command |
MIT |
| TensorRT-LLM |
10K+ |
NVIDIA-optimized inference |
Apache-2.0 |
| SGLang |
8K+ |
Structured generation language for LLMs |
Apache-2.0 |
| ExLlamaV2 |
4K+ |
Fast GPTQ/EXL2 inference |
MIT |
| MLC LLM |
20K+ |
Universal LLM deployment on any device |
Apache-2.0 |
| Text Generation Inference |
10K+ |
Production LLM serving by HuggingFace |
HFOIL-1.0 |
| LMDeploy |
5K+ |
Efficient LLM deployment toolkit |
Apache-2.0 |
| DeepSpeed-MII |
2K+ |
Low-latency model inference |
Apache-2.0 |
| PowerInfer |
8K+ |
Fast LLM serving on consumer GPUs |
Apache-2.0 |
| GGML |
11K+ |
Tensor library for ML |
MIT |
Vector Databases
Click to expand — Databases optimized for embedding storage and similarity search
| Database |
Stars |
Type |
Key Features |
License |
| Milvus |
32K+ |
Distributed |
GPU-accelerated, hybrid search |
Apache-2.0 |
| Qdrant |
22K+ |
Cloud-native |
Rust-based, filtering, payload |
Apache-2.0 |
| Weaviate |
12K+ |
Cloud-native |
GraphQL API, modules |
BSD-3 |
| ChromaDB |
16K+ |
Embedded |
Simple API, Python-first |
Apache-2.0 |
| Pinecone |
SaaS |
Managed |
Serverless, hybrid search |
Proprietary |
| pgvector |
13K+ |
Extension |
PostgreSQL vector search |
PostgreSQL |
| LanceDB |
5K+ |
Embedded |
Serverless, multimodal |
Apache-2.0 |
| Vespa |
6K+ |
Distributed |
Real-time serving, ranking |
Apache-2.0 |
| Marqo |
5K+ |
Cloud-native |
Tensor search, multimodal |
Apache-2.0 |
| FAISS |
32K+ |
Library |
GPU-optimized similarity search |
MIT |
LLM Orchestration
Click to expand — Frameworks for building LLM applications
| Tool |
Stars |
Focus |
License |
| LangChain |
100K+ |
Full-stack LLM application framework |
MIT |
| LlamaIndex |
38K+ |
Data-aware LLM applications |
MIT |
| DSPy |
22K+ |
Programming (not prompting) LMs |
MIT |
| Guidance |
19K+ |
Structured output generation |
MIT |
| Instructor |
9K+ |
Structured data extraction from LLMs |
MIT |
| Outlines |
10K+ |
Structured generation for LLMs |
Apache-2.0 |
| Mastra |
10K+ |
TypeScript AI framework |
MIT |
| Mirascope |
2K+ |
Pythonic LLM toolkit |
MIT |
| LiteLLM |
16K+ |
Unified API for 100+ LLM providers |
MIT |
| Portkey |
6K+ |
AI gateway for LLM routing |
MIT |
Prompt Engineering
Click to expand — Resources and tools for effective prompting
AI Code Assistants
Click to expand — AI-powered coding tools
| Tool |
Type |
Model |
Pricing |
Key Features |
| GitHub Copilot |
IDE Extension |
GPT-4o/Claude |
$10-39/mo |
Inline completion, chat, workspace |
| Cursor |
IDE |
Multi-model |
$20/mo |
Fork of VS Code with AI-native editing |
| Windsurf |
IDE |
Cascade |
$10/mo |
Agentic IDE with Flows |
| Cline |
Extension |
Multi-model |
Free (OSS) |
Autonomous coding agent in VS Code |
| Aider |
CLI |
Multi-model |
Free (OSS) |
AI pair programming in terminal |
| Continue |
Extension |
Multi-model |
Free (OSS) |
Open-source Copilot alternative |
| Tabnine |
Extension |
Custom |
$12/mo |
Privacy-focused, on-prem option |
| Amazon Q Developer |
IDE/CLI |
Amazon |
Free tier |
AWS-integrated code assistant |
| Devin |
Agent |
Custom |
$500/mo |
Autonomous software engineer |
| OpenHands |
Agent |
Multi-model |
Free (OSS) |
Open-source Devin alternative |
| SWE-agent |
Agent |
Multi-model |
Free (OSS) |
Autonomous bug fixing |
| Bolt.new |
Web |
Multi-model |
Freemium |
Full-stack app generation |
AI Image Generation
Click to expand — Image generation models and tools
AI Video Generation
Click to expand — Video generation and editing models
| Model/Tool |
Vendor |
Type |
Key Features |
| Sora |
OpenAI |
API |
Text-to-video, editing |
| Veo 2 |
Google |
API |
4K video generation |
| Kling |
Kuaishou |
SaaS |
Motion brush, lip sync |
| Runway Gen-3 |
Runway |
SaaS |
Multi-modal video gen |
| Pika 2.0 |
Pika |
SaaS |
Cinematic video gen |
| Luma Dream Machine |
Luma AI |
SaaS |
Fast video generation |
| CogVideo |
Tsinghua |
Open |
Open-source text-to-video |
| AnimateDiff |
Community |
Open |
Animation from images |
| Wan |
Alibaba |
Open |
Open-source video model |
AI Audio & Speech
Click to expand — Speech, music, and audio AI tools
| Tool |
Type |
Focus |
License |
| Whisper |
Model |
Speech-to-text |
MIT |
| Bark |
Model |
Text-to-speech, multilingual |
MIT |
| Coqui TTS |
Model |
Text-to-speech |
MPL-2.0 |
| Eleven Labs |
SaaS |
Voice cloning, TTS |
Proprietary |
| Suno |
SaaS |
Music generation |
Proprietary |
| Udio |
SaaS |
Music generation |
Proprietary |
| MusicGen |
Model |
Music generation |
MIT |
| Faster Whisper |
Tool |
Fast speech recognition |
MIT |
| WhisperX |
Tool |
Whisper with word alignment |
BSD-4 |
| Parler TTS |
Model |
Controllable TTS |
Apache-2.0 |
| Fish Speech |
Model |
Multilingual TTS |
CC-BY-NC-SA-4.0 |
AI Search Engines
Click to expand — AI-powered search and answer engines
| Engine |
Type |
Key Features |
| Perplexity |
SaaS |
Citation-backed AI answers, Pro Search |
| You.com |
SaaS |
AI search with apps and agents |
| Phind |
SaaS |
Developer-focused AI search |
| Tavily |
API |
Search API optimized for AI agents |
| Exa |
API |
Neural search API for embeddings |
| SearXNG |
Self-hosted |
Privacy-respecting metasearch |
| Kagi |
SaaS |
Premium ad-free search with AI |
| Brave Search |
SaaS |
Independent index with AI answers |
Evaluation & Benchmarks
Click to expand — LLM evaluation tools and benchmark suites
| Benchmark/Tool |
Focus |
Metrics |
Source |
| MMLU |
Knowledge |
57 subjects, 15K questions |
Hendrycks et al. |
| GPQA Diamond |
Expert reasoning |
PhD-level science questions |
NYU |
| HumanEval |
Code generation |
Pass@k on 164 problems |
OpenAI |
| SWE-bench |
Real software engineering |
GitHub issue resolution |
Princeton |
| Chatbot Arena |
Human preference |
Elo ratings from blind comparisons |
LMSYS |
| MATH |
Mathematics |
12.5K competition math problems |
Hendrycks et al. |
| BigBench |
Diverse tasks |
200+ language tasks |
Google |
| MT-Bench |
Multi-turn chat |
GPT-4 judged conversations |
LMSYS |
| AlpacaEval |
Instruction following |
Win rate vs reference model |
Stanford |
| IFEval |
Instruction following |
Verifiable instruction adherence |
Google |
| Open LLM Leaderboard |
Aggregate |
Multiple benchmarks combined |
HuggingFace |
| LM Evaluation Harness |
Framework |
200+ tasks, unified eval |
EleutherAI |
| HELM |
Holistic |
42 scenarios, 7 metrics |
Stanford |
| Agentic Benchmarks |
Agent capability |
Real-world task completion |
Various |
Datasets for Training
Click to expand — Key datasets for LLM pre-training and fine-tuning
| Dataset |
Size |
Focus |
License |
| FineWeb |
15T tokens |
Web text, deduplicated |
ODC-By |
| RedPajama v2 |
30T tokens |
Web crawl + curated |
Apache-2.0 |
| The Stack v2 |
67.5TB |
Source code, 600+ languages |
Various |
| OASST2 |
91K convos |
Human feedback dialogues |
Apache-2.0 |
| UltraChat |
1.5M convos |
Synthetic multi-turn chat |
MIT |
| SlimPajama |
627B tokens |
Deduplicated RedPajama |
Apache-2.0 |
| Dolma |
3T tokens |
Multi-source pretraining |
AI2 ImpACT |
| LMSYS-Chat-1M |
1M convos |
Real user LLM conversations |
CC-BY-NC-4.0 |
| OpenHermes 2.5 |
1M samples |
Curated instruction data |
CC-BY-4.0 |
| WildChat |
1M convos |
Real ChatGPT conversations |
AI2 ImpACT |
AI Safety & Alignment
Click to expand — Safety research, red-teaming, and alignment tools
| Resource |
Type |
Focus |
| Anthropic Research |
Lab |
Constitutional AI, interpretability |
| ARC Evals |
Evaluations |
Dangerous capability assessments |
| METR |
Organization |
Model evaluation and threat research |
| Guardrails AI |
Tool |
Input/output validation for LLMs |
| NeMo Guardrails |
Tool |
Programmable safety rails |
| LLM Guard |
Tool |
Security scanning for LLM I/O |
| Garak |
Tool |
LLM vulnerability scanner |
| Rebuff |
Tool |
Prompt injection detection |
| HarmBench |
Benchmark |
Red-teaming evaluation framework |
| Alignment Forum |
Community |
AI alignment research discussion |
AI Ethics & Governance
Click to expand — Ethical AI frameworks and governance resources
Compliance Frameworks
Click to expand — Regulatory and compliance frameworks for AI
| Framework |
Jurisdiction |
Status |
Focus |
| EU AI Act |
European Union |
Enforced (2025+) |
Risk-based AI regulation |
| NIST AI RMF |
United States |
Published |
AI risk management |
| ISO 42001 |
International |
Published |
AI management systems |
| ISO 23894 |
International |
Published |
AI risk management |
| NTIA SBOM |
United States |
Published |
Software bill of materials |
| OWASP Top 10 for LLMs |
International |
Published |
LLM security risks |
| CycloneDX ML-BOM |
International |
Published |
ML bill of materials |
Full framework data: data/frameworks/
MLOps & Model Serving
Click to expand — Tools for deploying and monitoring ML in production
Cloud AI Platforms
Click to expand — Managed AI/ML cloud services
| Platform |
Provider |
Key Services |
Model Access |
| AWS SageMaker |
Amazon |
Training, deployment, pipelines |
Bedrock models |
| Google Vertex AI |
Google |
AutoML, training, serving |
Gemini, PaLM |
| Azure AI Studio |
Microsoft |
Fine-tuning, prompt flow |
OpenAI, Llama |
| Hugging Face Inference |
HuggingFace |
Serverless API, Endpoints |
All HF models |
| Together AI |
Together |
Fine-tuning, inference |
Open models |
| Fireworks AI |
Fireworks |
Fast inference API |
Open models |
| Groq |
Groq |
Ultra-fast LPU inference |
Open models |
| Cerebras |
Cerebras |
Wafer-scale chip inference |
Open models |
| Replicate |
Replicate |
Run models via API |
100K+ models |
| Modal |
Modal |
Serverless GPU compute |
Any model |
| Lambda Labs |
Lambda |
GPU cloud for ML |
Any model |
Edge AI & On-Device
Click to expand — Running AI models on edge devices
| Tool/Framework |
Focus |
Platforms |
License |
| llama.cpp |
Local LLM inference |
CPU/GPU/Metal |
MIT |
| Ollama |
One-command local models |
Mac/Linux/Windows |
MIT |
| LM Studio |
Local LLM desktop app |
Mac/Windows/Linux |
Proprietary |
| Jan |
Open-source local AI |
Mac/Windows/Linux |
AGPL-3.0 |
| TensorFlow Lite |
Mobile/edge inference |
iOS/Android/Embedded |
Apache-2.0 |
| ONNX Runtime |
Cross-platform inference |
All platforms |
MIT |
| Core ML |
Apple silicon inference |
iOS/macOS |
Proprietary |
| MediaPipe |
On-device ML pipelines |
Mobile/Web/Desktop |
Apache-2.0 |
| MLC LLM |
Universal device deployment |
iOS/Android/Web |
Apache-2.0 |
| Executorch |
PyTorch mobile deployment |
Mobile/embedded |
BSD-3 |
AI Hardware
Click to expand — Chips and hardware for AI training and inference
| Hardware |
Vendor |
Type |
FLOPS (FP16) |
Use Case |
| H200 SXM |
NVIDIA |
GPU |
989 TFLOPS |
LLM training |
| H100 SXM |
NVIDIA |
GPU |
989 TFLOPS |
LLM training/inference |
| A100 80GB |
NVIDIA |
GPU |
312 TFLOPS |
LLM training |
| MI300X |
AMD |
GPU |
1307 TFLOPS |
LLM training |
| Gaudi 3 |
Intel |
Accelerator |
1835 TFLOPS |
LLM training |
| TPU v5p |
Google |
TPU |
459 TFLOPS |
LLM training |
| Trainium 2 |
AWS |
Accelerator |
N/A |
AWS LLM training |
| LPU |
Groq |
LPU |
N/A |
Ultra-low latency inference |
| WT-1 |
Cerebras |
WSE |
N/A |
Single-chip neural net |
| M3 Ultra |
Apple |
SoC |
800 GFLOPS |
Local inference |
| RTX 4090 |
NVIDIA |
GPU |
165.2 TFLOPS |
Consumer fine-tuning |
Free AI Courses
Click to expand — Free and high-quality AI learning resources
AI Research Labs
Click to expand — Leading AI research organizations
| Lab |
Type |
Focus Areas |
Notable Work |
| OpenAI |
Private |
AGI, safety, multimodal |
GPT-4, DALL-E, Sora |
| DeepMind |
Google |
Scientific AI, gaming |
AlphaFold, Gemini |
| Anthropic |
Private |
AI safety, interpretability |
Claude, Constitutional AI |
| Meta AI |
Corporate |
Open models, translation |
Llama, SEAMLESS |
| Microsoft Research |
Corporate |
AGI, safety, applications |
Phi, Orca |
| EleutherAI |
Nonprofit |
Open LLMs, transparency |
GPT-NeoX, Pythia |
| AI2 |
Nonprofit |
Scientific AI, commonsense |
OLMo, SPDX |
| Hugging Face |
Company |
Open AI ecosystem |
Transformers, datasets |
| Mistral AI |
Private |
Efficient open models |
Mistral, Mixtral |
| Cohere |
Private |
Enterprise NLP |
Command, Embed |
| Stability AI |
Private |
Open generative models |
Stable Diffusion |
| BigScience |
Research |
Open, multilingual LLMs |
BLOOM |
| LAION |
Nonprofit |
Open datasets |
LAION-5B, OpenCLIP |
AI Conferences & Events
Click to expand — Key AI conferences and community events
| Conference |
Focus |
Frequency |
Location |
| NeurIPS |
ML theory, applications |
Annual (Dec) |
Rotating |
| ICML |
Machine learning |
Annual (Jul) |
Rotating |
| ICLR |
Deep learning |
Annual (May) |
Rotating |
| CVPR |
Computer vision |
Annual (Jun) |
Rotating |
| ACL/EMNLP/NAACL |
NLP |
Annual |
Rotating |
| AAAI |
AI breadth |
Annual (Feb) |
Rotating |
| AI Engineer Summit |
LLM engineering |
Annual |
San Francisco |
| AI for Good |
Social impact AI |
Annual |
Geneva |
| GTC (NVIDIA) |
AI infrastructure |
Annual |
San Jose |
| Google I/O |
Google AI |
Annual (May) |
Mountain View |
| Microsoft Build |
Azure/OpenAI |
Annual (May) |
Seattle |
| AWS re:Invent |
AWS AI services |
Annual (Dec) |
Las Vegas |
Latest Papers (Daily Updated)
Click to expand — Notable recent arXiv papers (auto-updated daily)
March 2026
| Paper |
Authors |
Key Contribution |
arXiv |
| Qwen 3.5 Technical Report |
Alibaba |
72B model achieving 88.4 GPQA |
2503.xxxxx |
| DeepSeek-V3 |
DeepSeek |
MoE scaling, 671B with 37B active |
2412.19437 |
| Llama 4: Open Foundation Models |
Meta |
Multi-scale MoE, Scout & Maverick |
2504.xxxxx |
| Scaling LLM Test-Time Compute |
Google |
Test-time scaling improves reasoning |
2408.03314 |
| Constitutional AI |
Anthropic |
RLHF with AI feedback |
2212.08073 |
| Attention Is All You Need |
Google |
Original transformer paper |
1706.03762 |
| LoRA: Low-Rank Adaptation |
Microsoft |
Parameter-efficient fine-tuning |
2106.09685 |
| RLHF: Training LMs from Human Feedback |
OpenAI |
RLHF methodology |
2203.02155 |
| Chain-of-Thought Prompting |
Google |
CoT reasoning in LLMs |
2201.11903 |
| Retrieval-Augmented Generation |
Meta |
RAG for knowledge-intensive tasks |
2005.11401 |
This section is auto-updated daily via GitHub Actions.
Production Tools & APIs
Click to expand — APIs and platforms for AI in production
Multimodal AI
Click to expand — Models and tools handling multiple modalities
| Model |
Modalities |
Vendor |
License |
| GPT-4V / GPT-4o |
Text, Image, Audio |
OpenAI |
Proprietary |
| Gemini 3.1 Ultra |
Text, Image, Audio, Video, Code |
Google |
Proprietary |
| Claude 3 Opus |
Text, Image |
Anthropic |
Proprietary |
| LLaVA-1.6 |
Text, Image |
Community |
Apache-2.0 |
| Qwen-VL |
Text, Image, Video |
Alibaba |
Apache-2.0 |
| Phi-3 Vision |
Text, Image |
Microsoft |
MIT |
| InternVL2 |
Text, Image, Video |
Shanghai AI Lab |
MIT |
| PaliGemma 2 |
Text, Image |
Google |
Gemma |
| CogVLM2 |
Text, Image |
Tsinghua |
Apache-2.0 |
| Idefics3 |
Text, Image |
HuggingFace |
Apache-2.0 |
| Pixtral |
Text, Image |
Mistral |
Apache-2.0 |
AI for Science
Click to expand — AI models and tools for scientific research
| Tool |
Field |
Description |
License |
| AlphaFold 3 |
Biology |
Protein & molecular structure prediction |
CC-BY-NC-SA-4.0 |
| ESMFold |
Biology |
Meta's protein structure prediction |
MIT |
| OpenFold |
Biology |
Open-source AlphaFold |
Apache-2.0 |
| NVIDIA BioNeMo |
Biology |
Drug discovery foundation models |
Proprietary |
| MatterSim |
Materials |
Universal ML potential for materials |
MIT |
| ClimaX |
Climate |
Foundation model for weather/climate |
MIT |
| FourCastNet |
Climate |
Fast AI weather forecasting |
BSD-3 |
| GNoME |
Materials |
DeepMind materials discovery |
Research |
| ChemBERTa |
Chemistry |
SMILES-based molecular transformers |
MIT |
AI for Healthcare
Click to expand — Medical and clinical AI applications
| Tool |
Focus |
Organization |
Notes |
| Med-PaLM 2 |
Medical QA |
Google |
Passes USMLE |
| BioMedGPT |
Biomedical NLP |
Community |
Apache-2.0 |
| ClinicalBERT |
Clinical notes |
Research |
Apache-2.0 |
| PathAI |
Pathology |
PathAI |
Proprietary |
| Paige AI |
Oncology pathology |
Paige |
FDA-cleared |
| Tempus |
Precision oncology |
Tempus |
Proprietary |
| Insilico Medicine |
Drug discovery |
Insilico |
Proprietary |
AI for Finance
Click to expand — AI tools for financial services
| Tool |
Focus |
Notes |
| FinBERT |
Financial sentiment |
Fine-tuned BERT for finance |
| BloombergGPT |
Finance NLP |
50B finance-trained LLM |
| FinGPT |
Finance agent |
Open-source financial LLMs |
| NLP4Finance |
Various |
AI for finance research org |
| Numerai |
Stock prediction |
Tournament-based ML hedge fund |
AI for Robotics
Click to expand — Foundation models and tools for robotics
| Tool |
Focus |
Organization |
License |
| RT-2 |
Vision-language-action |
Google DeepMind |
Research |
| OpenVLA |
Open vision-language-action |
Stanford |
MIT |
| Octo |
Generalist robot policy |
Berkeley |
Apache-2.0 |
| Isaac ROS |
ROS2 GPU acceleration |
NVIDIA |
NVIDIA Isaac |
| LeRobot |
Learning for robots |
HuggingFace |
Apache-2.0 |
| Genesis |
Physics simulation |
Community |
Apache-2.0 |
Vendor Profiles
Click to expand — AI vendor ecosystem overview (40+ vendors tracked)
| Vendor |
HQ |
Founded |
EU AI Act Tier |
Key Models |
Licensing |
| OpenAI |
San Francisco, CA |
2015 |
High |
GPT-4, o3, DALL-E, Sora |
Proprietary |
| Anthropic |
San Francisco, CA |
2021 |
High |
Claude 3.5/4 |
Proprietary |
| Google DeepMind |
London, UK |
1988/2014 |
High |
Gemini, Veo, AlphaFold |
Proprietary/Open |
| Meta AI |
Menlo Park, CA |
2003 |
High |
Llama 4, SeamlessM4T |
Llama License |
| Microsoft |
Redmond, WA |
1975 |
High |
Phi, Copilot (OpenAI) |
Mixed |
| Mistral AI |
Paris, France |
2023 |
Limited |
Mistral, Mixtral, Codestral |
Apache-2.0/MRL |
| Cohere |
Toronto, Canada |
2019 |
Limited |
Command, Embed, Rerank |
Proprietary |
| AI21 Labs |
Tel Aviv, Israel |
2017 |
Limited |
Jamba, Jurassic |
Jamba Open |
| xAI |
San Francisco, CA |
2023 |
High |
Grok 3 |
Proprietary |
| DeepSeek |
Hangzhou, China |
2023 |
High |
DeepSeek-V3, R1 |
MIT |
| Alibaba |
Hangzhou, China |
1999 |
High |
Qwen 3.5 |
Apache-2.0 |
| Moonshot AI |
Beijing, China |
2023 |
Limited |
Kimi K2.5 |
Proprietary |
| Hugging Face |
New York, NY |
2016 |
N/A |
Hub, Transformers |
Apache-2.0 |
| Stability AI |
London, UK |
2019 |
High |
Stable Diffusion |
Various |
| Midjourney |
San Francisco, CA |
2021 |
High |
Midjourney v6 |
Proprietary |
Full vendor database: data/vendors/vendors.json
Use as an API
All data files are accessible as raw GitHub URLs. Use them as live endpoints:
import requests
BASE = "https://raw.githubusercontent.com/alpha-one-index/awesome-ai-index/main/data"
models = requests.get(f"{BASE}/models/models.json").json()
vendors = requests.get(f"{BASE}/vendors/vendors.json").json()
benchmarks = requests.get(f"{BASE}/benchmarks/benchmarks.json").json()
open_models = [
m for m in models
if m.get("license") != "Proprietary" and m.get("mmlu", 0) > 80
]
print(f"Found {len(open_models)} qualifying models")
Dataset Highlights
Top Models by Chatbot Arena (March 2026)
| Rank |
Model |
Vendor |
Arena Score |
GPQA Diamond |
License |
| 1 |
Claude Opus 4.6 |
Anthropic |
2002 |
91.5 |
Proprietary |
| 2 |
Gemini 3.1 Pro |
Google |
1855 |
90.8 |
Proprietary |
| 3 |
GPT-5.4 |
OpenAI |
1665 |
92.0 |
Proprietary |
| 4 |
Kimi K2.5 |
Moonshot AI |
1447 |
87.6 |
Proprietary |
| 5 |
Qwen 3.5 |
Alibaba |
1443 |
88.4 |
Apache-2.0 |
| 6 |
DeepSeek R1 |
DeepSeek |
1398 |
71.5 |
MIT |
| 7 |
Llama 4 Scout |
Meta |
1320 |
74.2 |
Llama 4 |
| 8 |
Mistral Large 3 |
Mistral AI |
1414 |
68.0 |
MRL-0.1 |
Full dataset with 130+ models: data/models/models.json
Academic Citation
@dataset{awesome_ai_index_2026,
title = {awesome-ai-index: The Definitive Open-Source AI Ecosystem Database},
author = {Alpha One Index},
year = 2026,
publisher = {GitHub},
url = {https://github.com/alpha-one-index/awesome-ai-index},
license = {CC-BY-SA-4.0}
}
See also: CITATION.cff
Schema & Methodology
Contributing
All contributions welcome! Especially:
Read CONTRIBUTING.md for the full guide.
Footnotes
| Project |
Description |
 |
Security & compliance ratings for 56+ AI vendors |
 |
Infrastructure benchmarking for AI systems |
 |
Full reports, premium tier, and API access |
 |
Dataset mirror for ML workflows |
 |
Dataset mirror on Kaggle |
Star History

Maintained by Alpha One Index | Data updated daily | Submit corrections via Issues | Discussions