Devstral 2 2512 vs Mistral Small 3.1 24B

In our 12-test benchmark suite, Devstral 2 2512 wins 8 categories outright, ties 4, and loses none against Mistral Small 3.1 24B.

Tool calling (4 vs 1): This is the most consequential gap. Devstral 2 2512 scores 4/5 (rank 18 of 54, tied with 28 others), while Mistral Small 3.1 24B scores 1/5 — rank 53 of 54, the second-lowest score in the entire field. The payload confirms Mistral Small 3.1 24B has a no_tool calling quirk flagged in its API parameters. This effectively disqualifies it from any agentic or function-calling workflow.

Agentic planning (4 vs 3): Devstral 2 2512 scores 4/5 (rank 16 of 54), while Mistral Small 3.1 24B scores 3/5 (rank 42 of 54). Both scores fall in the middle of the distribution, but the ranking gap is substantial — Devstral 2 2512 is in the top third, Mistral Small 3.1 24B in the bottom quarter.

Creative problem solving (4 vs 2): Devstral 2 2512 scores 4/5 (rank 9 of 54) — well above the p50 of 4. Mistral Small 3.1 24B scores 2/5 (rank 47 of 54), near the bottom of the distribution where p25 sits at 3. A two-point gap here means noticeably less novel, specific output.

Constrained rewriting (5 vs 3): Devstral 2 2512 scores 5/5, tied for 1st among 53 tested models. Mistral Small 3.1 24B scores 3/5, rank 31. For tasks requiring tight character limits or precise compression, this is a meaningful difference.

Structured output (5 vs 4): Devstral 2 2512 ties for 1st (rank 1 of 54, 25 models share the score). Mistral Small 3.1 24B scores 4/5 (rank 26 of 54) — still solid but one point behind.

Strategic analysis (4 vs 3): Devstral 2 2512 scores 4/5 (rank 27 of 54); Mistral Small 3.1 24B scores 3/5 (rank 36 of 54). Both below the p75 of 5, but Devstral 2 2512 is noticeably stronger at nuanced tradeoff reasoning.

Persona consistency (4 vs 2): Devstral 2 2512 scores 4/5 (rank 38 of 53 — below median on this test). Mistral Small 3.1 24B scores 2/5 (rank 51 of 53), near last place. For chatbot or roleplay applications, this gap matters.

Multilingual (5 vs 4): Devstral 2 2512 ties for 1st (rank 1 of 55, 35 models share the score). Mistral Small 3.1 24B scores 4/5 (rank 36 of 55). Both above the p50 of 5 — wait, the p50 here is 5, meaning Mistral Small 3.1 24B's score of 4 falls below the median for this benchmark across all models.

Ties (4 categories): Both models score identically on faithfulness (4/5, rank 34 of 55), classification (3/5, rank 31 of 53), long context (5/5, tied for 1st among 55 tested), and safety calibration (1/5, rank 32 of 55). The safety calibration tie at the bottom is worth flagging — both models score below the p25 of 1, meaning this is a shared weakness relative to the broader field.

Devstral 2 2512 costs $0.40/M input and $2.00/M output tokens. Mistral Small 3.1 24B costs $0.35/M input and $0.56/M output — making output tokens 3.57x cheaper. Input costs are nearly identical ($0.05/M difference). At 1M output tokens/month, Devstral 2 2512 costs $2.00 vs $0.56 — a $1.44 difference that barely registers. At 10M output tokens/month, the gap becomes $14.40 ($20.00 vs $5.60). At 100M output tokens/month — the scale of a production API serving thousands of users — you're looking at $200.00 vs $56.00, a $144/month difference. For startups or individual developers, this is a non-issue. For high-volume production deployments generating hundreds of millions of tokens monthly, Mistral Small 3.1 24B's lower cost becomes worth evaluating, provided you can work around its near-bottom tool calling score (rank 53 of 54 in our tests) and the lack of native tool calling support in the API.

Choose Devstral 2 2512 if you're building agentic pipelines, function-calling workflows, or any system that relies on tool use — Mistral Small 3.1 24B has a flagged no_tool calling limitation and scores 1/5 on tool calling in our tests (rank 53 of 54). Also choose Devstral 2 2512 for tasks where creative problem solving, constrained rewriting, structured output, or multilingual quality matter. The $2.00/M output cost is 3.57x higher, but the capability lead is wide enough to justify it for most professional use cases.

Choose Mistral Small 3.1 24B if your primary need is image understanding (it accepts text+image inputs; Devstral 2 2512 is text-only), you're running very high output volumes where the $0.56/M vs $2.00/M cost difference compounds significantly, and your workload doesn't require tool calling or agentic behavior. It also has a smaller 128K context window versus Devstral 2 2512's 262K — if you need to process very long documents, Devstral 2 2512 is the only option of the two.