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4 of 40 open-source models (filtered).

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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

Phi-4 14B

14B

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 Medium 14B

14B

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

Phi-3 Mini 4K Instruct

3.8B

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