Mistral Small 3.1 24B vs Mistral Small 3
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mistral Small 3.1 24B | Mistral Small 3 |
|---|---|---|
| Parameters | 24B | 24B |
| Author | Mistral AI | Mistral AI |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 14 GB |
| VRAM at Q5 | 17 GB | 17 GB |
| VRAM at Q8 | 26 GB | 26 GB |
| VRAM at FP16 | 48 GB | 48 GB |
| Use cases | chat, general, vision, multilingual, fr | chat, general, code |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Mistral Small 3.1 24B
Mistral AI's Small 3.1 — Small 3 plus a vision encoder, a 128k context, and ~150 tok/s inference under Apache 2.0. Small 3.2 (June 2025) is a drop-in upgrade. Strengths: Vision and text combined in one 24B model, 128k context window, Apache 2.0 license, Around 150 tokens/sec inference.
About Mistral Small 3
Mistral AI's 24B dense model that closes most of the gap with 70B-class models. Best quality-per-parameter we've measured at this size in 2025. Strengths: Quality approaching Llama 3 70B at a third the size, Low latency relative to peers, 128k context window, Strong tool use and agent behavior.
How they compare
Mistral Small 3.1 24B comes from Mistral AI and Mistral Small 3 from Mistral AI. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
Mistral Small 3.1 24B and Mistral Small 3 share the same 24B parameter class. Both need about 14 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
Where they overlap on benchmarks, Mistral Small 3 takes MMLU with 81 against 80.6 — a narrow 0.4-point margin. For workloads weighted toward that benchmark, Mistral Small 3 is the stronger default.
For long-context work, Mistral Small 3.1 24B offers the bigger window (125k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, Mistral Small 3.1 24B requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB, while Mistral Small 3 requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB. In practice Mistral Small 3.1 24B needs a 16 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Mistral Small 3.1 24B needs roughly 24 GB of system RAM to run on CPU and Mistral Small 3 about 24 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 15 tokens/sec from Mistral Small 3.1 24B and 15 from Mistral Small 3, scaling up to 40 and 40 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Mistral Small 3.1 24B or Mistral Small 3 to the card you actually own:
- On a 16 GB GPU: Mistral Small 3.1 24B runs at Q4 (14 GB); Mistral Small 3 runs at Q4 (14 GB).
- On a 24 GB GPU: Mistral Small 3.1 24B runs at Q5 (17 GB); Mistral Small 3 runs at Q5 (17 GB).
Benchmark scores
Reported benchmarks for Mistral Small 3.1 24B: MMLU 80.6, MMMU 64.
Reported benchmarks for Mistral Small 3: MMLU 81, GPQA 42.2, HumanEval 84.8.
Bottom line: which should you pick?
- Pick Mistral Small 3.1 24B for long-context work (up to 125k tokens).
- Pick Mistral Small 3 if MMLU performance is your priority (81 vs 80.6).
- Pick Mistral Small 3.1 24B if your workload is fr, multilingual, vision.
- Pick Mistral Small 3 if your workload is code.
Which GPU should you buy to run Mistral Small 3.1 24B?
To run Mistral Small 3.1 24B locally at Q4, you need ~14 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between Mistral Small 3.1 24B and Mistral Small 3?
The headline differences: both are 24B models; their context windows differ (125k vs 32k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Mistral Small 3.1 24B and Mistral Small 3 run on a 24 GB GPU?
At a Q4 quantization, Mistral Small 3.1 24B needs about 14 GB of VRAM and fits comfortably on a 24 GB GPU; Mistral Small 3 needs about 14 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
Is Mistral Small 3.1 24B or Mistral Small 3 more capable?
On MMLU, Mistral Small 3 scores higher (81 vs 80.6), a 0.4-point advantage on this benchmark.
What licenses do Mistral Small 3.1 24B and Mistral Small 3 use?
Mistral Small 3.1 24B is licensed under Apache 2.0 and Mistral Small 3 under Apache 2.0.
Which has the longer context window, Mistral Small 3.1 24B or Mistral Small 3?
Mistral Small 3.1 24B has the larger context window (125k vs 32k tokens), so it handles longer documents and codebases in a single prompt.