Guide · 2026-06-21

Phi-4 14B vs Mistral Small 3.1 24B — Underdog vs Sweet Spot

Last updated 2026-06-21

The reasoning underdog meets the all-round sweet spot. We pit Microsoft's Phi-4 14B against Mistral Small 3.1 24B on benchmarks, VRAM, speed and cost.

By Mohamed Meguedmi · 9 min read

The Matchup at a Glance

These two models occupy adjacent niches in the 14B–24B class, the band most developers actually run locally. Microsoft's Phi-4 14B is a dense, reasoning-tuned model trained heavily on synthetic and curated data. Mistral AI's Mistral Small 3.1 24B Instruct is a broader, multimodal generalist with a far longer context window.

Attribute	Phi-4 14B	Mistral Small 3.1 24B
Parameters	14.7B (dense)	24B (dense)
Architecture	Decoder-only transformer	Decoder-only transformer + vision encoder
Context window	16K tokens	128K tokens
Multimodal	No (text only)	Yes (image input)
License	MIT	Apache 2.0
Released	Dec 2024	Mar 2025
Best at	Math, science, structured reasoning	General assistant, vision, long context

Both ship with permissive, commercially friendly licenses — MIT for Phi-4 and Apache 2.0 for Mistral Small 3.1 — so neither carries the usage restrictions that plague some other open-weight releases. You can deploy either in a commercial product without a separate agreement.

Benchmarks — Where Each Model Wins

Headline benchmark numbers tell a clear story: Phi-4 over-indexes on reasoning and STEM, while Mistral Small 3.1 holds its own across general knowledge and coding while adding vision. The figures below are drawn from the published model cards and Microsoft's Phi-4 technical report; see our methodology for how we normalize across sources.

Benchmark	Phi-4 14B	Mistral Small 3.1 24B	Edge
MMLU (5-shot)	84.8	~81	Phi-4
GPQA (graduate science)	56.1	~46	Phi-4
MATH	80.4	~70	Phi-4
HumanEval (code)	82.6	~84	Mistral
MMMU (vision)	N/A	~64	Mistral
Long-context recall	16K ceiling	Strong to 128K	Mistral

The pattern is consistent: Phi-4 wins the thinking-hard benchmarks, Mistral Small 3.1 wins the do-everything benchmarks. Neither is strictly "better" — they are tuned for different jobs.

The standout result for Phi-4 is GPQA, where a 14B model lands above 56 — a score that embarrasses several 30B+ peers. That is the underdog story in a single data point. Mistral Small 3.1's counter is breadth: it reads images, handles 8x the context, and edges Phi-4 on practical coding tasks like HumanEval.

Hardware & Memory Footprint

For local deployment, VRAM is destiny. The 10B parameter gap between these models translates into a real difference in which GPUs can run them. The table below estimates total memory (weights + a modest KV cache) for common GGUF quantizations.

Quant	Phi-4 14B	Mistral Small 3.1 24B	Min. GPU (comfortable)
Q4_K_M	~9.0 GB	~14.5 GB	12 GB vs 16 GB
Q5_K_M	~10.5 GB	~17 GB	12 GB vs 20 GB
Q6_K	~12 GB	~20 GB	16 GB vs 24 GB
Q8_0	~15.5 GB	~25.5 GB	16 GB vs 32 GB / dual-GPU

The practical reading: Phi-4 14B at Q4_K_M is a 12 GB-card model — it runs on an RTX 3060 12GB or 4070 with room for context. Mistral Small 3.1 24B realistically wants 16–24 GB; a 16 GB card handles Q4_K_M with short context, and a 24 GB card (4090/3090) gives you the high-quality quants plus a long context buffer. If your hardware budget tops out at 12 GB, the decision is largely made for you.

Browse the full quant matrix and download links for both models in our model catalog.

Speed & Throughput

Fewer parameters means faster tokens. On a single 24 GB GPU with llama.cpp at Q4_K_M, Phi-4 14B typically generates in the 55–75 tok/s range single-stream, while Mistral Small 3.1 24B lands around 35–50 tok/s on the same setup. The exact numbers depend on backend, batch size and context length, but the ~1.4–1.6x speed advantage for Phi-4 is consistent and predictable from the parameter ratio.

For interactive agents and tool-calling loops where many short turns dominate, that latency gap is noticeable. For batch document processing where you stream long outputs, Mistral Small 3.1's larger context lets you process more per call, which can offset its lower per-token rate. Model your own throughput against pricing with the cost calculator.

Context, Multimodality & Licensing

This is where Mistral Small 3.1 separates itself. Its 128K context window versus Phi-4's 16K is an 8x difference that changes what workloads are even possible. Retrieval-augmented pipelines over long PDFs, whole-codebase reasoning, and multi-document summarization all favor Mistral. Phi-4's 16K ceiling is adequate for chat and focused reasoning but becomes a hard wall for long-document work.

Mistral Small 3.1 also accepts image input natively — diagrams, screenshots, charts and document scans — making it a genuine multimodal assistant out of the box. Phi-4 is text-only. If your application touches visual data at all, the comparison ends here in Mistral's favor.

On licensing, both are clean for commercial use, but Apache 2.0 (Mistral) is marginally more permissive than MIT (Phi-4) for patent-grant purposes. In practice either is fine for a shipped product.

Cost to Run

Self-hosting cost is dominated by the GPU you must buy or rent. Because Phi-4 fits on cheaper cards and runs faster, its effective cost-per-token is lower when you are GPU-bound. The rough picture for a single-GPU local deployment:

Factor	Phi-4 14B	Mistral Small 3.1 24B
Entry GPU	12 GB (~$300 used)	16–24 GB (~$600–900 used)
Tokens/sec (Q4, 24GB)	55–75	35–50
Relative cost/token	Lower	Higher
Cost per capability	Excellent for STEM	Excellent for breadth + vision

If you are renting cloud API access instead of self-hosting, both sit at the low end of the market — typically well under $0.20 per million input tokens — so the meaningful cost lever is which model does your job in fewer calls. For Phi-4, that means math/reasoning tasks; for Mistral Small 3.1, long-context and multimodal tasks where a single model replaces a pipeline.

Which Should You Choose?

Choose Phi-4 14B if your workload is math-heavy, science-heavy, or logic-heavy; if you run on 12 GB of VRAM; or if you need the fastest reasonable reasoning model for an agent loop. It is the efficiency champion and the value pick for STEM. See our roundup of the best local reasoning models for where it sits against larger peers.

Choose Mistral Small 3.1 24B if you need vision, a long context window, or a single dependable generalist for a production assistant. It is the lower-risk default for broad applications and the better fit when your inputs include images or large documents. For more pairings in this class, see our model comparison guides.

Conclusion — The Verdict

This is a genuine "depends on the job" matchup, but the framing in the title holds. Phi-4 14B is the underdog that wins on reasoning-per-parameter and fits where Mistral can't. Mistral Small 3.1 24B is the sweet spot — the model you reach for when you want one capable, multimodal, long-context generalist and have the VRAM to host it.

Your priority	Winner
Math / science / reasoning	Phi-4 14B
12 GB VRAM budget	Phi-4 14B
Raw tokens/sec	Phi-4 14B
Vision / image input	Mistral Small 3.1 24B
Long documents (128K)	Mistral Small 3.1 24B
All-round production assistant	Mistral Small 3.1 24B
Overall versatility	Mistral Small 3.1 24B

Want the underlying numbers programmatically? Every benchmark and quant figure on this page is available through the free BestLLMfor public API (CC BY 4.0) and our open-source MCP server, so you can wire live model data straight into your own tooling. Pull both model cards from ollama.com/library/phi4 and ollama.com/library/mistral-small to start testing today.