55 open-source models indexed
The open-source AI model directory.
Licenses, parameters, benchmarks, VRAM and inference pricing for Llama, Mistral, Qwen, DeepSeek, Gemma, Phi and more. Pick the right open-weights model for your use case.
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Best models for…
All use cases →Curated picks for the six workloads we get asked about most.
Open-source models that excel at code generation, completion, and review. Picks span on-device 7B models through frontier-class 30B+ specialists.
Open-source models that run well on consumer hardware (RTX 4090, Apple Silicon, even laptop iGPUs). Picks balance quality with VRAM footprint.
Models tuned for retrieval-augmented generation: long context, strong instruction following, native citation behaviour where possible.
Open-weights multimodal models that can read images, do OCR-adjacent tasks, and reason over diagrams and screenshots.
Models that generate long internal chains-of-thought before answering — designed for math, code, and multi-step problem solving.
Sub-4B models that run on phones, laptops, and embedded devices. Optimised for memory footprint and tokens-per-second on integrated GPUs.
Featured models
See all →Moonshot AI's 1-trillion-parameter mixture-of-experts (32B active per token). Trained on 15.5T tokens with a heavy emphasis on tool-use and agentic behaviour. Modified-MIT licence with an attribution clause for very-large deployments. Exceptional at long-horizon agent tasks; benchmarked well against Claude Sonnet on SWE-bench Verified.
- Context
- 128K
- License
- kimi
- VRAM Q4
- 600 GB
671B-parameter MoE model with 37B active per token. Trained for roughly $5.6M of compute — a landmark in cost-efficient frontier training. Frontier-class quality at a fraction of the cost of the closed proprietary frontier. The DeepSeek licence permits commercial use with limited restrictions on military and unlawful applications. Running V3 yourself requires serious hardware (8× H100 at fp8); most teams will use it via the DeepSeek API or providers like Together.
- Context
- 128K
- License
- deepseek
- VRAM Q4
- 402.6 GB
Reasoning model trained with reinforcement learning on top of DeepSeek V3-Base. MIT licence — even the weights are unrestricted, making R1 the most permissively-licensed frontier reasoning model. Generates long internal chains-of-thought before answering, trading latency for accuracy on math, code, and reasoning benchmarks. Distilled variants (e.g. R1 Distill Llama 70B) recover most of the quality at much smaller scales.
- Context
- 128K
- License
- mit
- VRAM Q4
- 402.6 GB
Meta's July 2024 flagship — the first open-weights model at 405B parameters. Trained on 15T tokens with 128K context. Rivals GPT-4o on many academic benchmarks and set the ceiling for open-weights quality for most of 2024. Running it self-hosted requires serious hardware (8× H100 at fp8 or multi-node at fp16); most users will run it via a hosted provider (Together, Groq, Fireworks). Llama 3.3 70B closed most of the practical gap at a fraction of the cost, so 405B is now most useful when 70B specifically hits its ceiling.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 243 GB
Hybrid Mamba-Transformer-MoE model with native 256K context (effective beyond 140K). 94B active parameters out of 398B total. The state-space-model layers give it linear-time scaling with sequence length, making it interesting for very long contexts. Licensed under AI21's open model licence, which permits most commercial use.
- Context
- 256K
- License
- jamba-open
- VRAM Q4
- 238.8 GB
NVIDIA's reward-modelling research vehicle. Trained primarily to be a synthetic-data-generation specialist rather than a chat-first model. Useful for teams building instruction-tuning datasets at scale.
- Context
- 4K
- License
- llama-3
- VRAM Q4
- 204 GB
Browse by family
Cohere's enterprise-oriented model family. Command R+ targets retrieval-augmented generation workflows.
Databricks' 132B mixture-of-experts (12 of 16 experts active per token) built on the MegaBlocks stack. Released under the Databricks Open Model Licence.
Hangzhou-based lab known for highly efficient MoE training. DeepSeek V3 and R1 set new bars for open reasoning and coding.
Technology Innovation Institute (UAE). Falcon Mamba pioneered state-space-model open releases.
Google's open-weights model family derived from the same research as Gemini. Strong performance at small scales.
xAI's open-weights releases. Grok 1 and Grok 2 weights have been published under Apache 2.0.
Nous Research's community-driven fine-tune series. Hermes 3 sits on top of Llama 3.1 base weights, tuned for very strong tool use and steerable roleplay behaviour.
Hybrid Mamba/Transformer architecture with very long native context windows.
Beijing-based lab founded by ex-Tsinghua researchers. Kimi K1.5 and K2 push very long context and reasoning; K2 is a trillion-parameter MoE with strong tool-use behaviour.
Meta's open-weights LLM family. Llama 3 introduced 128K context and strong instruction tuning across 1B–405B parameter scales.
Paris-based lab whose dense and mixture-of-experts open-weights models popularised sliding-window attention and high efficiency.
NVIDIA's research and instruction-tuning effort built on top of Llama base models, with strong RLHF and reward-modelling work.
AI2's fully-open initiative: weights, data and training code all under permissive licences.
Microsoft's small-language-model line, focused on data-quality-driven training. Phi-4 punches well above its weight class.
Alibaba's Qwen series. Qwen2.5 delivers competitive frontier-class performance across sizes from 0.5B to 72B with permissive licensing.
Stability AI's general-purpose text models.
01.AI's bilingual (Chinese/English) open-weights series including vision variants.
MMLU leaderboard — top 5
Full leaderboards →- 90.80
- 89.50
- 88.50
- 87.30
- 87.10
Why open-source models matter
Open-weights models let you run inference locally, fine-tune on private data, and avoid vendor lock-in. Open Source AI Models tracks every model with public weights so you can pick on facts, not marketing.