Devstral Medium vs Ministral 3 14B 2512

Across our 12-test suite, Ministral 3 14B 2512 wins the majority (5 wins), Devstral Medium wins 1, and 6 tests tie. Detailed per-test summary (score: Devstral → Ministral, with rank context and task meaning):

• strategic_analysis: 2 → 4 — Ministral wins. In our testing this reflects better nuanced tradeoff reasoning (Ministral ranks 27 of 54). Expect Ministral to handle numeric tradeoffs and multi-constraint decisions more reliably.
• constrained_rewriting: 3 → 4 — Ministral wins (rank 6 of 53). This means Ministral is substantially better at strict-length or character-limited rewriting tasks (e.g., ad copy under tight limits).
• structured_output: 4 → 4 — tie (both rank ~26 of 54). Both models handle JSON/schema compliance similarly in our tests.
• long_context: 4 → 4 — tie (both rank 38 of 55). Both perform comparably on retrieval/accuracy across 30k+ token contexts.
• persona_consistency: 3 → 5 — Ministral wins (tied for 1st with 36 others). Ministral is far stronger at maintaining character and resisting injection in our testing.
• agentic_planning: 4 → 3 — Devstral wins (Devstral rank 16 of 54 vs Ministral rank 42). Devstral is better at goal decomposition and failure recovery scenarios in our agentic planning tests.
• creative_problem_solving: 2 → 4 — Ministral wins (rank 9 of 54). Ministral produces more non-obvious, feasible ideas in our creative tasks.
• tool_calling: 3 → 4 — Ministral wins (rank 18 of 54). Ministral selects functions, arguments, and sequencing more accurately in our function-calling tests.
• faithfulness: 4 → 4 — tie (both rank 34 of 55). Both models stick to source material equally in our tests.
• classification: 4 → 4 — tie (both tied for 1st with 29 others). Both are accurate for routing and categorization tasks in our suite.
• safety_calibration: 1 → 1 — tie (both low; rank 32 of 55). Both models show weak refusal/permissiveness calibration on harmful requests in our testing and need guardrails.
• multilingual: 4 → 4 — tie (both rank 36 of 55). Comparable non-English performance in our tests.

Practical interpretation: Ministral 3 14B 2512 is stronger on persona, tool use, creative problem solving, and constrained rewriting — tasks common in production assistants and coding tools. Devstral Medium is narrowly better at agentic planning. For schema adherence, long-context retrieval, classification, and faithfulness the models perform similarly in our tests.

Pricing gap: Devstral Medium charges input $0.40 and output $2.00 per 1k tokens; Ministral 3 14B 2512 charges input $0.20 and output $0.20 per 1k tokens (priceRatio 10). Example costs assuming a 50/50 input/output split: for 1M tokens/month Devstral ≈ $1,200 vs Ministral ≈ $200; for 10M tokens Devstral ≈ $12,000 vs Ministral ≈ $2,000; for 100M tokens Devstral ≈ $120,000 vs Ministral ≈ $20,000. If you treat all tokens as outputs (worst-case), 1M tokens cost Devstral $2,000 vs Ministral $200. Teams at high volume (≥10M tokens/month), startups on tight budgets, and cost-sensitive consumer products should prefer Ministral 3 14B 2512; R&D teams or products where agentic planning accuracy justifies large spend may consider Devstral Medium despite the 6–10x higher bill depending on token mix.

Choose Ministral 3 14B 2512 if you need the best cost-to-performance balance: it wins 5 benchmarks (persona_consistency, tool_calling, creative_problem_solving, constrained_rewriting, strategic_analysis), costs far less (output $0.20 vs $2.00 per 1k tokens), and is the better default for production assistants, tool-integrated agents, and high-volume deployments. Choose Devstral Medium if your primary need is higher-quality agentic planning (it scores 4 vs 3 in our agentic_planning tests and ranks higher for that task) and you can justify much higher token bills for that specific capability.