Devstral 2 2512 vs o4 Mini

Our 12-test suite gives o4 Mini a clear edge on balance: it wins 5 benchmarks outright, ties 6, and loses only 1 against Devstral 2 2512.

Where o4 Mini wins:

• Tool calling (5 vs 4): o4 Mini ties for 1st among 54 models tested (shared with 16 others); Devstral 2 2512 ranks 18th of 54 (tied with 28 others). For agentic workflows that depend on accurate function selection and argument passing, this is a meaningful gap.
• Faithfulness (5 vs 4): o4 Mini ties for 1st among 55 models; Devstral 2 2512 ranks 34th of 55. In RAG and summarization tasks where sticking to source material matters, o4 Mini is the safer choice.
• Strategic analysis (5 vs 4): o4 Mini ties for 1st among 54 models; Devstral 2 2512 ranks 27th of 54. This test measures nuanced tradeoff reasoning with real numbers — relevant for business analysis, financial modeling, and research synthesis.
• Classification (4 vs 3): o4 Mini ties for 1st among 53 models; Devstral 2 2512 ranks 31st of 53. Routing and categorization tasks will be more reliable with o4 Mini.
• Persona consistency (5 vs 4): o4 Mini ties for 1st among 53 models; Devstral 2 2512 ranks 38th of 53. For chatbots or character-driven applications, o4 Mini maintains character more reliably under adversarial prompts.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 3): Devstral 2 2512 ties for 1st among 53 models tested; o4 Mini ranks 31st of 53. This test compresses text within hard character limits — a task Devstral 2 2512 handles at the top of the field while o4 Mini falls well below the median.

Where they tie (6 tests):

• Structured output (5/5): Both tie for 1st among 54 models — equal JSON schema compliance.
• Long context (5/5): Both tie for 1st among 55 models — equivalent retrieval at 30K+ tokens. Note that Devstral 2 2512's context window is 262,144 tokens vs o4 Mini's 200,000 tokens, which could matter at the extreme end.
• Agentic planning (4/4): Both rank 16th of 54, tied with 25 others — no advantage for either on goal decomposition.
• Creative problem solving (4/4): Both rank 9th of 54 — equal on non-obvious, feasible idea generation.
• Multilingual (5/5): Both tie for 1st among 55 models — neither has an edge on non-English output quality.
• Safety calibration (1/1): Both rank 32nd of 55 — neither model handles harmful request refusal well relative to the field. This is the lowest score either model earns, and it sits at the 25th percentile of all models tested.

External benchmarks (Epoch AI data, o4 Mini only): o4 Mini scores 97.8% on MATH Level 5, ranking 2nd of 14 models with external scores (tied with 2 others) — at the 75th percentile of that group. On AIME 2025, it scores 81.7%, ranking 13th of 23 models with scores. Devstral 2 2512 has no external benchmark scores in the payload, so no direct comparison is possible on these math-specific tests. The o4 Mini external scores confirm strong quantitative reasoning capability that our internal proxies support.

Devstral 2 2512 costs $0.40/M input and $2.00/M output tokens. o4 Mini costs $1.10/M input and $4.40/M output tokens — 2.75x more expensive on input and 2.2x more on output.

At 1M output tokens/month: Devstral 2 2512 runs $2.00 vs o4 Mini's $4.40 — a $2.40 difference that's negligible for most users.

At 10M output tokens/month: the gap grows to $24 vs $44 — $20/month in added cost. Still manageable for most teams.

At 100M output tokens/month: Devstral 2 2512 costs $200 vs o4 Mini's $440 — a $240/month difference that starts to matter for cost-sensitive products. Factor in input tokens and a mixed 1:3 input-to-output ratio, and a 100M-output workload with 33M input tokens adds another $13 (Devstral) vs $36 (o4 Mini) — bringing the full monthly gap closer to $267.

Note also that o4 Mini uses reasoning tokens (flagged in the payload), meaning effective token consumption — and therefore real cost — can exceed what a simple per-token estimate suggests. Teams should benchmark actual spend on their workload before committing. Devstral 2 2512's cost advantage is most compelling for high-throughput agentic pipelines where output volume is large and constrained rewriting or structured output quality is the bottleneck.

Choose o4 Mini if: your workload depends on accurate tool calling for agentic pipelines, faithfulness to source material in RAG or summarization, strong strategic and tradeoff analysis, reliable classification/routing, or consistent persona maintenance. It scores higher on 5 of 12 benchmarks in our testing and brings multimodal input (text + image + file) that Devstral 2 2512 does not support per the payload. Its external math scores (97.8% on MATH Level 5, 81.7% on AIME 2025 per Epoch AI) also make it a strong choice for quantitative reasoning tasks. Accept that you'll pay $4.40/M output tokens and that reasoning token overhead can push real costs higher than estimates.

Choose Devstral 2 2512 if: your primary use case is agentic coding — the model is explicitly designed for that task per its description — or if you need best-in-class constrained rewriting (tied for 1st of 53 models in our tests). It also offers a larger context window (262K vs 200K tokens) and costs 2.2x less on output tokens, making it the better choice for cost-sensitive, high-throughput text generation pipelines. If the five benchmarks o4 Mini wins are not central to your application, Devstral 2 2512 delivers competitive quality at a substantially lower price.