Mistral Small 3.1 24B vs Devstral Small 2 24B

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 Devstral Small 2 24B

Mistral AI's 24B coding specialist co-developed with All Hands AI, scoring 72.2% on SWE-Bench under Apache 2.0. Fits on a single RTX 4090. Strengths: 72.2% SWE-Bench in a 24B dense model, Runs comfortably on a single RTX 4090, 256K context for whole-repo work, Apache 2.0 license.

How they compare

Mistral Small 3.1 24B comes from Mistral AI and Devstral Small 2 24B 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 Devstral Small 2 24B share the same 24B parameter class. Both need about 14 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.

The two models target different sweet spots: Mistral Small 3.1 24B is tuned for chat, general, vision, multilingual, fr, while Devstral Small 2 24B leans toward code, fr. Match the model to your dominant workload rather than to raw size.

For long-context work, Devstral Small 2 24B offers the bigger window (250k vs 125k 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 Devstral Small 2 24B 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 Devstral Small 2 24B 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 Devstral Small 2 24B, 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 Devstral Small 2 24B to the card you actually own:

• On a 16 GB GPU: Mistral Small 3.1 24B runs at Q4 (14 GB); Devstral Small 2 24B runs at Q4 (14 GB).
• On a 24 GB GPU: Mistral Small 3.1 24B runs at Q5 (17 GB); Devstral Small 2 24B runs at Q5 (17 GB).

Benchmark scores

Reported benchmarks for Mistral Small 3.1 24B: MMLU 80.6, MMMU 64.

Reported benchmarks for Devstral Small 2 24B: SWE-Bench 72.2.

Bottom line: which should you pick?

• Pick Devstral Small 2 24B for long-context work (up to 250k tokens).
• Pick Mistral Small 3.1 24B if your workload is chat, general, multilingual, vision.
• Pick Devstral Small 2 24B 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).

Frequently asked questions

What is the difference between Mistral Small 3.1 24B and Devstral Small 2 24B?

The headline differences: both are 24B models; their context windows differ (125k vs 250k 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 Devstral Small 2 24B 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; Devstral Small 2 24B needs about 14 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.

What licenses do Mistral Small 3.1 24B and Devstral Small 2 24B use?

Mistral Small 3.1 24B is licensed under Apache 2.0 and Devstral Small 2 24B under Apache 2.0.

Which has the longer context window, Mistral Small 3.1 24B or Devstral Small 2 24B?

Devstral Small 2 24B has the larger context window (250k vs 125k tokens), so it handles longer documents and codebases in a single prompt.