Devstral 2 2512 vs Mistral Medium 3.1

We compared both models across our 12-test suite. Summary (scores shown A = Devstral 2 2512, B = Mistral Medium 3.1):

• Structured output: A 5 vs B 4 — Devstral wins. This measures JSON/schema compliance; Devstral is tied for 1st (tied with 24 others) on structured_output, so it's a reliable choice for strict format adherence.
• Creative problem solving: A 4 vs B 3 — Devstral wins. A ranks 9 of 54 on creative_problem_solving, so it generates more non-obvious feasible ideas in our tests.
• Strategic analysis: A 4 vs B 5 — Mistral wins. B is tied for 1st (with 25 others) on strategic_analysis, indicating stronger nuanced tradeoff reasoning in numeric scenarios.
• Classification: A 3 vs B 4 — Mistral wins. B is tied for 1st (29 others) on classification, so it is better at routing and categorization in our benchmarks.
• Safety calibration: A 1 vs B 2 — Mistral wins. B ranks 12 of 55 on safety_calibration versus A at rank 32; Mistral better refuses harmful prompts while allowing legitimate ones in our tests.
• Persona consistency: A 4 vs B 5 — Mistral wins. B is tied for 1st (with 36 others), so it better maintains character and resists injection in chat tasks.
• Agentic planning: A 4 vs B 5 — Mistral wins. B is tied for 1st (with 14 others), showing superior goal decomposition and failure recovery.
• Constrained rewriting: A 5 vs B 5 — tie. Both are tied for 1st, strong at compression under hard character limits.
• Tool calling: A 4 vs B 4 — tie. Both rank 18 of 54, performing similarly on function selection and argument accuracy.
• Faithfulness: A 4 vs B 4 — tie. Both rank 34 of 55, comparable at sticking to source material.
• Long context: A 5 vs B 5 — tie. Both tied for 1st (with 36 others) on retrieval accuracy at 30K+ tokens.
• Multilingual: A 5 vs B 5 — tie. Both tied for 1st (with 34 others) for non-English parity. Overall, Mistral Medium 3.1 wins 5 benchmarks (strategic_analysis, classification, safety_calibration, persona_consistency, agentic_planning), Devstral 2 2512 wins 2 (structured_output, creative_problem_solving), and 5 are ties. Rankings show Mistral leads on agentic and safety-related axes, while Devstral is best for strict schema outputs and ideation quality.

Both models share identical pricing in the payload: input_cost_per_mtok = $0.40 and output_cost_per_mtok = $2.00. Translate that to monthly spend (mTok = per 1k tokens):

• 1M tokens (1,000 mTok): input-only $400; output-only $2,000; 50/50 split $1,200.
• 10M tokens (10,000 mTok): input-only $4,000; output-only $20,000; 50/50 split $12,000.
• 100M tokens (100,000 mTok): input-only $40,000; output-only $200,000; 50/50 split $120,000. Because price is identical, cost-sensitive teams should focus on which model reduces overall token usage (shorter outputs, fewer retries). High-volume deployers (10M+ tokens/month) will care most about small quality differences that cut user retries and system prompts — here capability wins, not price.

Choose Devstral 2 2512 if: you require precise structured outputs or schema-first generation (structured_output 5 vs 4) or stronger creative problem solving (4 vs 3). It’s ideal for tasks where JSON compliance and inventive solutions matter.
Choose Mistral Medium 3.1 if: you need better agentic planning (5 vs 4), classification (4 vs 3), safety calibration (2 vs 1), or persona consistency (5 vs 4) — e.g., production assistants, automated planners, or safety-sensitive deployments. Pricing is the same, so pick the model whose winning benchmarks map to your primary tasks.