Capability · Comparison
Microsoft Phi-4 vs Mistral Small 3
Phi-4 and Mistral Small 3 are the two strongest small open models for reasoning and everyday tasks on a single GPU. Phi-4 (14B) trains heavily on synthetic reasoning data and punches above its weight on math and logic. Mistral Small 3 (24B) is tuned for speed and general chat, with a fully permissive Apache 2.0 license. Which you pick depends on whether you need reasoning density or conversational breadth.
Side-by-side
| Criterion | Phi-4 | Mistral Small 3 |
|---|---|---|
| Parameters | 14B | 24B |
| License | MIT | Apache 2.0 |
| Math / reasoning (GSM8K, MATH) | Best-in-class for size | Good, not reasoning-specialised |
| General chat / instruction following | Reasonable, feels drier | Strong, warmer tone |
| Context window | 16k tokens | 32k tokens |
| Inference speed (same hardware) | Moderate | Fast |
| Fits on 24GB consumer GPU | Yes, comfortably | Yes, tighter with long context |
| Training data | Heavy synthetic reasoning mix | Curated web + instruction mix |
Verdict
For math, logic, and code puzzles on a single GPU, Phi-4 is the strongest small open model — it trades breadth for reasoning density and wins that trade if your workload is narrow. For everyday chat, general agents, or multilingual work where latency matters, Mistral Small 3 is a better all-rounder and its Apache 2.0 license makes it easier to ship commercially. Many teams keep both around and route by task type.
When to choose each
Choose Phi-4 if…
- Your workload is math, logic, or reasoning-heavy.
- You want the best quality per parameter on a laptop GPU.
- You're fine with the MIT license but your product is research-y.
- Context window under 16k is enough.
Choose Mistral Small 3 if…
- You need a well-rounded chat model for general agents.
- Apache 2.0 matters for your downstream distribution.
- Latency budget is tight — Small 3 is faster per token.
- You need 32k context or multilingual coverage.
Frequently asked questions
Is Phi-4 actually competitive with 70B models?
On narrow reasoning tasks like math and logic puzzles, yes — it's remarkably close. On general conversation, knowledge breadth, and long-form writing, 70B-class models still lead.
Which is easier to fine-tune?
Both are well supported in Transformers, Axolotl, and Unsloth. Mistral Small 3 has a slightly larger community of published fine-tunes to learn from.
Can I run either as an on-device model?
With 4-bit quantisation both can run on a 16GB Mac or 12GB GPU, though Mistral Small 3 is tighter with long context.
Sources
- Microsoft — Phi-4 technical report — accessed 2026-04-20
- Mistral — Small 3 — accessed 2026-04-20