Devstral 2 2512 vs GPT-4.1

Summary of head-to-head results in our 12-test suite (scores shown are from our testing):

• GPT-4.1 wins (5 tests): strategic_analysis 5 vs 4 (GPT-4.1 tied for 1st of 54), tool_calling 5 vs 4 (GPT-4.1 tied for 1st of 54), faithfulness 5 vs 4 (GPT-4.1 tied for 1st of 55), classification 4 vs 3 (GPT-4.1 tied for 1st of 53), persona_consistency 5 vs 4 (GPT-4.1 tied for 1st of 53). Practical meaning: GPT-4.1 is stronger for nuanced tradeoff reasoning, reliable function selection & argumenting in tool-call flows, and sticking closely to source material — important for production agents, routing/classification pipelines, and systems where hallucination risk must be minimized.
• Devstral 2 2512 wins (2 tests): structured_output 5 vs 4 (Devstral tied for 1st with 24 others) and creative_problem_solving 4 vs 3 (Devstral ranks 9 of 54 for creative problem solving). Practical meaning: Devstral generates cleaner machine-readable outputs and offers more non-obvious feasible ideas in our creative tasks — helpful for schema-heavy integrations and ideation workflows.
• Ties (5 tests): constrained_rewriting 5/5 (both tied for 1st), long_context 5/5 (both tied for 1st), safety_calibration 1/1 (both rank 32 of 55), agentic_planning 4/4 (both rank 16 of 54), multilingual 5/5 (both tied for 1st). Practical meaning: both models handle very long contexts and strict compression equally well in our tests, but both scored low on safety calibration in our suite, indicating similar behavior on refusal/permission calibration. Additional external context: GPT-4.1 has external benchmark scores on third-party tests (Epoch AI): SWE-bench Verified 48.5, MATH Level 5 83, AIME 2025 38.3 — we present these as supplementary evidence (Epoch AI). Devstral has no external benchmark entries in the payload. Overall, GPT-4.1 wins the greater number of distinct benchmarking categories important for production engineering and classification/faithful outputs; Devstral’s strengths are structured output fidelity and creative idea generation, plus a much lower inference cost.

Per the payload, Devstral 2 2512 charges $0.4 per mTok input and $2 per mTok output; GPT-4.1 charges $2 per mTok input and $8 per mTok output. Translate per-million-token costs (1 mTok = 1,000 tokens): Devstral input = $400/1M tokens, output = $2,000/1M; GPT-4.1 input = $2,000/1M, output = $8,000/1M. At scale: for 10M tokens multiply by 10 (Devstral input $4,000 / output $20,000; GPT-4.1 input $20,000 / output $80,000). For 100M tokens multiply by 100 (Devstral input $40,000 / output $200,000; GPT-4.1 input $200,000 / output $800,000). If your workload has roughly equal input and output volumes, combined per-1M cost (50/50 split) is about $2,400 for Devstral vs $10,000 for GPT-4.1. The cost gap matters most for high-volume production inference (10M–100M tokens/month) and teams with tight ML infra budgets; smaller experimentation or high-stakes quality use cases may justify GPT-4.1’s higher price.

Choose Devstral 2 2512 if: you need lower-cost inference at scale (input/output $0.4/$2 per mTok), require excellent JSON/schema adherence, or prioritize creative problem ideation and large-context affordance on a budget. Choose GPT-4.1 if: you need the safest option for faithfulness, classification, tool calling, and strategic analysis (GPT-4.1 wins 5 of 12 benchmarks in our testing), or you run production agents and can justify the higher cost ($2/$8 per mTok) for those quality gains.