All models

16 of 40 open-source models (filtered).

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xAI's first open-weights release: a 314B-parameter mixture-of-experts model. Apache 2.0 licensed. Largely a research artefact at this size — most users will run smaller models for production — but useful as a permissively-licensed reference for MoE research.

Context: 8K
License: apache-2-0
VRAM Q4: 188.4 GB

Mixtral 8×22B Instruct

141B

Scaled-up Mixtral with 22B-parameter experts. ~39B active parameters out of 141B total. Strong long-context performance and competitive coding scores. Apache 2.0 makes it attractive for self-hosting where the licence terms of Llama 3 are a non-starter.

Context: 66K
License: apache-2-0
VRAM Q4: 84.6 GB

Mixtral 8×7B Instruct

46.7B

The mixture-of-experts release that introduced 8 experts of 7B each, 2 active per token. ~13B active parameters with 47B total, which makes per-token inference roughly as fast as a 13B dense model while approaching 70B dense quality. Apache 2.0 weights mean it's still a popular self-hosting choice. Memory footprint is the main constraint — the full 47B parameters must be loaded even though only a quarter are active per token.

Context: 33K
License: apache-2-0
VRAM Q4: 28 GB

Yi VL 34B

34B

Vision-language variant of Yi 34B. Image-text reasoning via an MLP adapter on a CLIP encoder. Useful for bilingual EN/中 multimodal workloads where the major Western vision-language models underperform on Chinese text in images.

Context: 4K
License: apache-2-0
VRAM Q4: 20.4 GB

Yi 1.5 34B Chat

34B

Bilingual EN/中 34B chat model. Apache 2.0 licensed with strong Chinese-language performance and competitive English chat quality. Good default for bilingual production workloads.

Context: 33K
License: apache-2-0
VRAM Q4: 20.4 GB

QwQ 32B Preview

32B

Qwen's reasoning-focused 'thinking' model. Generates long chains-of-thought before answering, similar to OpenAI's o1 and DeepSeek R1 lineage. Optimised for math and competition-style problem solving. The Preview tag means Qwen is iterating quickly; later versions may obsolete this one. Useful today for math-heavy workloads where a slow, careful answer is preferred to a fast wrong one.

Context: 33K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 32B Instruct

32B

32B sweet-spot model: strong reasoning, fits on one H100 in fp16, on a 4090 at Q4. The 32B size in particular hits a quality/cost knee — quality scales with parameters faster than cost up to ~32B, and slower afterwards. Favoured for production chat where 7B isn't sharp enough and where 70B+ would over-spec the hardware budget. Apache 2.0 licence.

Context: 128K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 Coder 32B

32B

Coding-specialised Qwen2.5 32B fine-tune. GPT-4o-class on HumanEval and BigCodeBench at the time of release. Trained on additional code-heavy data with extended pre-training. Apache 2.0. Natural pick for self-hosted coding assistants, code-review automation, and any agent loop that primarily writes code.

Context: 128K
License: apache-2-0
VRAM Q4: 19.2 GB

Mistral Small 3

24B

24B dense model from Mistral's January 2025 release that competes with Llama 3.3 70B on many tasks at a third of the parameter count. Apache 2.0 licensed and small enough to run on a single 4090 at Q4. Good pick when you want Llama-3.3-70B-class chat quality but at a friendlier hardware budget, or when the licence matters and Llama's community terms don't fit.

Context: 33K
License: apache-2-0
VRAM Q4: 14.4 GB

Qwen2.5 14B Instruct

14B

Mid-size Qwen2.5 with broad task coverage. The sweet spot for users who want noticeably better quality than 7B but can't justify the hardware footprint of 32B or 72B.

Context: 128K
License: apache-2-0
VRAM Q4: 8.4 GB

OLMo 2 13B

13B

Larger OLMo 2 release. Same fully-open philosophy as the 7B variant. The 13B size makes it more competitive with mainstream production-grade chat models.

Context: 4K
License: apache-2-0
VRAM Q4: 7.8 GB

Mistral Nemo 12B

12B

Joint Mistral × NVIDIA model with 128K context, designed as a drop-in upgrade to Mistral 7B. Trained with NVIDIA's Megatron stack and released under Apache 2.0. Strong multilingual coverage thanks to the Tekken tokenizer.

Context: 128K
License: apache-2-0
VRAM Q4: 7.2 GB

Stable LM 2 12B

12B

Stability AI's general-purpose 12B model. Apache 2.0. Useful default when you need a permissively-licensed 12B-scale model.

Context: 4K
License: apache-2-0
VRAM Q4: 7.2 GB

OLMo 2 7B

Fully-open 7B model: weights, training data and code all released under permissive licences. Useful as a reference for reproducibility research and for teams that need full transparency on training data provenance.

Context: 4K
License: apache-2-0
VRAM Q4: 4.2 GB

Qwen2.5 7B Instruct

Apache-2.0-licensed 7B model with surprisingly strong reasoning and multilingual chops. Qwen 2.5 trains on a larger and more carefully filtered corpus than the original Qwen series, and the 7B variant punches well above its weight on coding and math benchmarks. A strong default for cost-sensitive chat workloads and for fine-tuning experiments where the Apache licence simplifies downstream redistribution.

Context: 128K
License: apache-2-0
VRAM Q4: 4.2 GB

Mistral 7B v0.3

The original Mistral 7B refresh with 32K context and extended vocabulary. Permissive Apache 2.0 weights and the first widely-deployed sliding-window-attention model. Still useful in 2026 for very-low-cost inference and as a baseline for fine-tuning experiments.

Context: 33K
License: apache-2-0
VRAM Q4: 4.2 GB