Devstral 2 2512 vs GPT-5.1

Across our 12-test internal benchmark suite, GPT-5.1 wins 5 categories, Devstral 2 2512 wins 2, and 5 are tied. Neither model has had their average score computed yet in our system, so this analysis is based on individual test results.

Where GPT-5.1 wins:

• Strategic analysis: GPT-5.1 scores 5/5 (tied for 1st among 54 models, shared with 25 others) vs Devstral 2 2512's 4/5 (rank 27 of 54). For nuanced tradeoff reasoning with real numbers, GPT-5.1 is the stronger pick.
• Faithfulness: GPT-5.1 scores 5/5 (tied for 1st among 55 models) vs Devstral 2 2512's 4/5 (rank 34 of 55). GPT-5.1 is less likely to introduce information not present in source material — a meaningful difference for RAG pipelines and summarization.
• Classification: GPT-5.1 scores 4/5 (tied for 1st among 53 models) vs Devstral 2 2512's 3/5 (rank 31 of 53). A full point gap here; Devstral 2 2512 sits below the field median on this test.
• Safety calibration: GPT-5.1 scores 2/5 (rank 12 of 55) vs Devstral 2 2512's 1/5 (rank 32 of 55). Both models are in the bottom half of our tested field on this dimension — GPT-5.1 is better, but neither should be treated as a safety-first choice based on our testing.
• Persona consistency: GPT-5.1 scores 5/5 (tied for 1st among 53 models) vs Devstral 2 2512's 4/5 (rank 38 of 53). For chatbot or roleplay applications that need stable character maintenance, GPT-5.1 has a clear edge.

Where Devstral 2 2512 wins:

• Structured output: Devstral 2 2512 scores 5/5 (tied for 1st among 54 models, with 24 others) vs GPT-5.1's 4/5 (rank 26 of 54). JSON schema compliance is a real differentiator here — Devstral 2 2512 is more reliable for structured data extraction tasks in our testing.
• Constrained rewriting: Devstral 2 2512 scores 5/5 (tied for 1st among 53 models, with 4 others — a smaller tie group, making this score more distinctive) vs GPT-5.1's 4/5 (rank 6 of 53). For tasks requiring tight compression within hard character limits, Devstral 2 2512 is the top performer.

Tied categories (both models score identically):

• Creative problem solving: Both score 4/5, tied at rank 9 of 54.
• Tool calling: Both score 4/5, tied at rank 18 of 54. Neither model stands out for agentic tool use in our testing — both are mid-field performers.
• Long context: Both score 5/5, tied for 1st among 55 models. Devstral 2 2512 offers a 262K context window; GPT-5.1 offers 400K. Both max out our long-context benchmark.
• Agentic planning: Both score 4/5, tied at rank 16 of 54.
• Multilingual: Both score 5/5, tied for 1st among 55 models.

External benchmarks (GPT-5.1 only): On SWE-bench Verified (Epoch AI), GPT-5.1 scores 68% — rank 7 of 12 models with that data point, placing it mid-field among tracked models on real GitHub issue resolution. On AIME 2025 (Epoch AI), GPT-5.1 scores 88.6% — rank 7 of 23 models, above the field median of 83.9%. No external benchmark data is available for Devstral 2 2512 in the payload.

Devstral 2 2512 costs $0.40/M input and $2/M output. GPT-5.1 costs $1.25/M input and $10/M output — 3.1× more expensive on input and 5× more expensive on output. In practice: at 1M output tokens/month, Devstral 2 2512 costs $2 vs GPT-5.1's $10, a difference of $8. At 10M output tokens, that gap becomes $80. At 100M output tokens — typical for a production application — you're paying $200 vs $1,000 per month. That's an $800/month savings with Devstral 2 2512 at that scale, before factoring in input costs. Developers building high-volume pipelines — document processing, code generation, structured data extraction — should weigh that gap seriously against the benchmark advantages GPT-5.1 holds. If your use case doesn't depend heavily on strategic reasoning, faithfulness, or classification quality, the 5× output premium for GPT-5.1 is hard to justify.

Choose Devstral 2 2512 if: You're building high-volume pipelines that depend on structured output quality or constrained text generation — it scores 5/5 on both in our testing and costs $2/M output tokens. It's also worth serious consideration for any production application where the 5× output cost premium of GPT-5.1 would compound significantly at scale. Its 256K context window handles most long-document tasks.

Choose GPT-5.1 if: Your application depends on faithfulness to source material (5/5 vs 4/5), accurate classification and routing (4/5 vs 3/5), strategic reasoning (5/5 vs 4/5), or stable persona consistency (5/5 vs 4/5). It also supports image and file input alongside text — a capability Devstral 2 2512 lacks per the payload — and offers a 400K context window. The $10/M output cost is justified when those benchmark margins translate directly to quality requirements in your use case.