Microsoft's "small but mighty" family — packs reasoning quality of much larger models into a 14B body. Available variants: mini (4B) 14B.
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Phi-4 14B benchmarks above Llama 3.1 70B on math, reasoning, and code at 1/5 the parameter count. If your local rig is 16 GB or less, Phi-4 14B is the model you should default to — it gives you frontier-tier reasoning quality at 9 GB VRAM. The downside: smaller context window (16K) than Qwen3 / Llama 4.
Phi-4 mini fits on almost any modern card. Quality is solid for a 4B model — beats Llama 3.2 3B on most benchmarks.
*Price sourced from Amazon.com. Price and availability subject to change.
14B Q4 fits in 9 GB; 14B Q8 needs 14 GB. The 16 GB tier (RTX 4070 Ti Super, RTX 4060 Ti 16 GB) gives Q8 quality with comfortable headroom.
*Price sourced from Amazon.com. Price and availability subject to change.
Native FP16 inference for the best possible quality. Overkill for most use cases — Q8 is indistinguishable in practice — but available if you have the hardware.
*Price sourced from Amazon.com. Price and availability subject to change.
Microsoft trained Phi-4 on a heavily curated synthetic dataset focused on reasoning chains, math proofs, and code traces. The "phi" series has always punched above its parameter count on reasoning benchmarks; Phi-4 just keeps that pattern with a stronger base.
Smaller context window (16K vs 128K+ on competing families) and weaker creative writing / open-ended chat. For factual Q&A, math, code, and tool-use it's great; for "tell me a story" it's worse than Llama 4 / Gemma 4 of comparable size.
For autocomplete-style tasks, yes — it's competitive with Llama 3.1 8B at half the VRAM. For multi-step refactoring or full-file code gen, step up to 14B or pair with Qwen3-Coder.