Devstral 2 2512 vs GPT-5.4 Mini

Across our 12-test internal benchmark suite, GPT-5.4 Mini wins 5 tests outright, Devstral 2 2512 wins 1, and the two tie on 6.

Where GPT-5.4 Mini wins:

• Strategic analysis (5 vs 4): GPT-5.4 Mini scores 5/5, tying 1st with 25 others out of 54 models. Devstral 2 2512 scores 4/5, ranking 27th of 54. For nuanced tradeoff reasoning with real numbers, GPT-5.4 Mini is the clear pick.
• Faithfulness (5 vs 4): GPT-5.4 Mini scores 5/5, tied for 1st out of 55 models. Devstral 2 2512 scores 4/5, ranking 34th of 55. In RAG pipelines or any task requiring strict adherence to source material, this gap matters — hallucination risk is meaningfully higher with Devstral 2 2512 in our testing.
• Classification (4 vs 3): GPT-5.4 Mini scores 4/5, tied for 1st of 53. Devstral 2 2512 scores 3/5, ranking 31st of 53. This is one of Devstral 2 2512's weakest areas — below the field median of 4 — which affects any routing or categorization use case.
• Safety calibration (2 vs 1): GPT-5.4 Mini scores 2/5, ranking 12th of 55. Devstral 2 2512 scores 1/5, ranking 32nd of 55. Both are weak relative to the field (p75 is 2), but Devstral 2 2512 is at the floor. Neither model should be deployed without additional guardrails in safety-sensitive applications.
• Persona consistency (5 vs 4): GPT-5.4 Mini scores 5/5, tied for 1st of 53. Devstral 2 2512 scores 4/5, ranking 38th of 53. For chatbot or roleplay applications requiring stable character maintenance, GPT-5.4 Mini is noticeably more reliable.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 4): Devstral 2 2512 scores 5/5, tied for 1st with 4 others out of 53 models — a genuine strength. GPT-5.4 Mini scores 4/5, ranking 6th of 53. For tasks requiring compression within hard character limits (headlines, ad copy, summaries), Devstral 2 2512 is the better tool.

Where they tie (6 tests):

• Structured output (5/5 each, both tied 1st of 54): Both are excellent at JSON schema compliance.
• Long context (5/5 each, both tied 1st of 55): Equivalent retrieval accuracy at 30K+ tokens.
• Tool calling (4/4 each, both rank 18th of 54): Solid but not class-leading; 29 models share this score.
• Agentic planning (4/4 each, both rank 16th of 54): Competent goal decomposition, not best-in-class.
• Creative problem solving (4/4 each, both rank 9th of 54): Above median, tied.
• Multilingual (5/5 each, both tied 1st of 55): Equivalent non-English performance.

External benchmark note: Neither model has external benchmark scores (SWE-bench Verified, MATH Level 5, AIME 2025) in the payload. The internal suite is the only available comparison data.

Devstral 2 2512 costs $0.40/MTok input and $2.00/MTok output. GPT-5.4 Mini costs $0.75/MTok input and $4.50/MTok output — 1.875x more expensive on input and 2.25x more expensive on output. In practice, output costs dominate most real workloads, so the gap is meaningful:

• At 1M output tokens/month: $2.00 vs $4.50 — a $2.50 difference, negligible for most teams.
• At 10M output tokens/month: $20 vs $45 — a $25/month gap worth tracking.
• At 100M output tokens/month: $200 vs $450 — a $250/month difference that materially affects unit economics.

GPT-5.4 Mini also adds image and file input support, which Devstral 2 2512 lacks (text-only). If your pipeline requires multimodal inputs, GPT-5.4 Mini's higher cost is unavoidable. For pure text workloads, the cost premium needs to be justified by the quality wins — five benchmark advantages is a real edge, but at scale the savings from Devstral 2 2512 add up fast.

Choose Devstral 2 2512 if:

• Your primary use case is agentic coding or structured output generation — its 256K context window and top-tier structured output and constrained rewriting scores make it well-suited for long-context code tasks.
• You're running high-volume text-only pipelines where the output cost difference ($2.00 vs $4.50/MTok) compounds significantly.
• Your inputs are strictly text — Devstral 2 2512 is text-only and you're not paying for multimodal capabilities you won't use.
• You need tight formatting control (e.g., generating copy within character limits).

Choose GPT-5.4 Mini if:

• You need multimodal inputs — GPT-5.4 Mini accepts text, images, and files; Devstral 2 2512 does not.
• Faithfulness to source material is critical (e.g., document Q&A, RAG): GPT-5.4 Mini scores 5/5 vs 4/5 in our testing.
• Your application involves classification, routing, or categorization tasks: GPT-5.4 Mini ranks 1st vs Devstral 2 2512's 31st of 53.
• You need reliable persona consistency for chatbots or agent personas: 5/5 vs 4/5.
• Strategic analysis and nuanced reasoning are central to your workflow: 5/5 vs 4/5, with GPT-5.4 Mini ranking in the top tier.
• Safety calibration matters more (though both models are weak here — neither should be deployed without external guardrails).