Devstral 2 2512 vs GPT-4o

Across our 12-test benchmark suite, Devstral 2 2512 outperforms GPT-4o on 6 tests, loses on 2, and ties on 4.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 3): Devstral 2 2512 ties for 1st among 53 models; GPT-4o ranks 31st. For tasks requiring compression to hard character limits — ad copy, SMS, headers — the gap is significant.
• Structured output (5 vs 4): Devstral 2 2512 ties for 1st among 54 models; GPT-4o ranks 26th. JSON schema compliance and format adherence are critical for API pipelines and agentic workflows — this is a meaningful advantage.
• Multilingual (5 vs 4): Devstral 2 2512 ties for 1st among 55 models; GPT-4o ranks 36th. If your users or content aren't in English, Devstral 2 2512 produces more consistently equivalent-quality output.
• Strategic analysis (4 vs 2): Devstral 2 2512 ranks 27th of 54; GPT-4o ranks 44th. A two-point gap on nuanced tradeoff reasoning with real numbers is substantial — GPT-4o scored 2/5, which is below the 50th percentile for this test.
• Creative problem solving (4 vs 3): Devstral 2 2512 ranks 9th of 54; GPT-4o ranks 30th. Generating non-obvious, specific, feasible ideas is a clear Devstral 2 2512 strength.
• Long context (5 vs 4): Devstral 2 2512 ties for 1st among 55 models and also carries a 262K context window vs GPT-4o's 128K. GPT-4o ranks 38th on retrieval accuracy at 30K+ tokens. If you're processing long documents or codebases, Devstral 2 2512 has a double advantage: better retrieval performance and twice the context capacity.

Where GPT-4o wins:

• Classification (4 vs 3): GPT-4o ties for 1st among 53 models; Devstral 2 2512 ranks 31st. For routing, categorization, or intent classification pipelines, GPT-4o is the stronger pick.
• Persona consistency (5 vs 4): GPT-4o ties for 1st among 53 models; Devstral 2 2512 ranks 38th. Maintaining character and resisting prompt injection is a clear GPT-4o strength — relevant for chatbots and roleplay applications.

Ties (both scored equally):

• Tool calling (both 4/5, both rank 18th of 54)
• Faithfulness (both 4/5, both rank 34th of 55)
• Safety calibration (both 1/5, both rank 32nd of 55 — a shared weakness)
• Agentic planning (both 4/5, both rank 16th of 54)

External benchmarks (Epoch AI data): GPT-4o has third-party scores on record: 31% on SWE-bench Verified (ranks 12th of 12 models in that set), 53.3% on MATH Level 5 (ranks 12th of 14), and 6.4% on AIME 2025 (ranks 22nd of 23). These external results place GPT-4o at the lower end of models tracked on those math and coding benchmarks. Devstral 2 2512 does not have external benchmark scores in our payload. For context, the median SWE-bench Verified score across tracked models is 70.8%, making GPT-4o's 31% well below the field median on that external measure.

Devstral 2 2512 costs $0.40/M input tokens and $2.00/M output tokens. GPT-4o costs $2.50/M input and $10.00/M output — 6.25× more expensive on input and 5× more on output. In practice: at 1M output tokens/month, GPT-4o costs $10 vs Devstral 2 2512's $2 — a $8 difference. At 10M tokens, that gap becomes $80,000 vs $20,000. At 100M tokens, you're looking at $1,000,000 vs $200,000 — an $800,000 annual swing. Developers running high-volume pipelines (document processing, code generation agents, structured data extraction) should treat that cost ratio as a primary decision factor. GPT-4o's pricing is only justified if multimodal input or its specific benchmark wins (classification, persona consistency) are load-bearing for your use case.

Choose Devstral 2 2512 if:

• You need structured JSON output or format-constrained generation at scale — it scores 5/5 and ties for 1st in our testing.
• Your workload involves long documents, large codebases, or retrieval over extended context — 262K window vs GPT-4o's 128K, with better retrieval scores.
• You're processing high token volumes and cost is a real constraint — at $2/M output tokens vs $10/M, you save 80%.
• Strategic analysis, creative problem solving, or multilingual output quality matter for your use case.
• You're building agentic pipelines that don't require image or file input.

Choose GPT-4o if:

• Your application requires image or file input — GPT-4o supports text+image+file modalities; Devstral 2 2512 is text-only.
• You're building classification or intent-routing systems — GPT-4o ties for 1st of 53 models on classification in our tests.
• Persona consistency and resistance to prompt injection are critical — GPT-4o ties for 1st of 53 models there.
• You're already integrated into the OpenAI ecosystem and the additional parameters (logprobs, top_logprobs, web_search_options) are load-bearing for your application.