All models
36 of 40 open-source models (filtered).
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
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
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
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
Cohere's flagship 104B model. RAG-focused with native multilingual support across ~10 high-resource languages. CC-BY-NC weights; commercial use via Cohere's hosted API.
- Context
- 128K
- License
- mrl
- VRAM Q4
- 62.4 GB
Larger vision-language Llama variant, competitive with the proprietary multimodal frontier on standard image-understanding benchmarks. Drops in as a vision upgrade where 11B isn't sharp enough. Requires substantial GPU memory in fp16; most teams will run it quantized or on multi-GPU. A natural pairing with retrieval pipelines that fetch image-rich chunks alongside text.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 54 GB
The flagship Qwen 2.5 release. Competes with Llama 3.1 405B on many benchmarks at one-fifth the parameter count. Note the 72B specifically uses the Qwen License (commercial use up to 100M MAU) — the smaller Qwen2.5 sizes are Apache 2.0.
- Context
- 128K
- License
- qwen
- VRAM Q4
- 43.2 GB
Meta's December 2024 refresh of Llama 3 70B that closes most of the gap with Llama 3.1 405B for chat workloads while remaining tractable on a single H100. Strong instruction following, robust tool-use behaviour, and a 128K context window make it the default choice for production chat at 70B scale. The 3.3 release was trained on a refreshed instruction-tuning data mix and benefits from Meta's most recent alignment work. It outperforms the much larger 3.1 405B on several reasoning benchmarks at a fraction of inference cost. The licence is the Llama 3 Community License, which permits commercial use unless your service exceeds 700M monthly active users. Good pick for: production chat at scale, RAG over long documents, agentic workflows where tool use matters, and any 70B-tier replacement for closed proprietary models.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
R1 reasoning capabilities distilled into a Llama 3.3 70B base. The most accessible way to run R1-class reasoning locally — fits on a single H100 in fp16 or on a 4090 at Q4. Inherits Llama 3's community licence (commercial use under 700M MAU). Great pick for production reasoning workloads where the full R1 is too expensive to host but o1/R1-style quality is required.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
NVIDIA's RLHF-tuned Llama 3.1 70B. Tops several Arena-style human-preference leaderboards and shipped with NVIDIA's reward-model research. Inherits the Llama 3 community licence.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
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
Cohere's 35B model tuned for RAG and tool use. The open weights are released under CC-BY-NC (commercial use requires the Cohere API). Strong multilingual coverage and a fine-grained RAG-mode output format that makes downstream citation easier.
- Context
- 128K
- License
- mrl
- VRAM Q4
- 21 GB
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
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
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
Flagship Gemma 2 release. Uses logit-distillation from a larger teacher model, which is how Google delivers near-70B quality from a 27B student. A solid choice when the Llama community licence doesn't fit and you need quality at the 27B–40B size range.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 16.2 GB
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
14B model trained primarily on synthetic data. Punches above its weight on reasoning, especially MATH and GPQA. MIT licensed. A standout choice when you want strong reasoning quality without paying 70B-tier hardware costs. Phi-4 in particular demonstrated that careful synthetic-data curation can extract frontier-class reasoning from a relatively small dense model.
- Context
- 16K
- License
- mit
- VRAM Q4
- 8.4 GB
Phi-3's mid-tier model with extended 128K context. MIT licence. Strong reasoning relative to its parameter count thanks to Microsoft's heavy investment in synthetic training data.
- Context
- 128K
- License
- mit
- VRAM Q4
- 8.4 GB
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
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
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
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
Llama 3's first vision-language model. Image understanding via a separately-trained ViT adapter bolted onto Llama 3 weights. Useful for OCR-adjacent workloads, document understanding, and image captioning at a permissive licence. The 11B size makes it cheap to host. Combined with the 128K text context, it handles long PDF-with-images workflows comfortably on a single 4090.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 6.6 GB
Mid-tier Gemma. Strong general-purpose chat model at small scale. The Gemma Terms of Use permit commercial use subject to Google's prohibited-use policy.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 5.4 GB
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
TII's latest dense 7B from December 2024. Strong scores on commonsense reasoning benchmarks. TII's Falcon licence permits royalty-free commercial use with attribution.
- Context
- 33K
- License
- falcon-2
- VRAM Q4
- 4.2 GB
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
The first major open-weights state-space model. Linear-time decoding, no KV cache — memory usage stays flat as context grows, which makes it interesting for very long-context workloads. Falcon licence.
- Context
- 16K
- License
- falcon-2
- VRAM Q4
- 4.2 GB
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
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
Microsoft's flagship small-model demonstration: GPT-3.5-class on academic benchmarks at <4B parameters. The 4K context-window variant is the lightest; a 128K variant ships separately. MIT licensed, well-suited to on-device assistants and structured-extraction workloads where compactness matters more than absolute quality.
- Context
- 4K
- License
- mit
- VRAM Q4
- 2.3 GB
Pocket-sized Llama 3 variant for edge deployment. Surprising chat quality after instruction tuning makes it competitive with much larger models from a previous generation. At Q4 it fits in ~2 GB of VRAM and runs on consumer GPUs and recent Apple Silicon. A strong default for on-device chat, summarisation, and structured extraction tasks where the workload doesn't need frontier reasoning quality.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 1.8 GB
Compact Gemma variant designed for on-device inference. Trained with knowledge distillation from larger Gemma 2 teachers. Runs comfortably on a phone at Q4.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 1.6 GB
The smallest Llama 3 release, designed for on-device inference on phones and laptops. The 1B model runs comfortably in <2 GB of RAM at Q4 quantization and is fast enough for real-time chat on a modern smartphone. Useful for edge inference, on-device assistants where round-tripping to a server is undesirable, and as a draft model for speculative decoding in front of a larger Llama 3 variant.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 0.6 GB