Devstral 2 2512 vs o3

Across our 12-test internal suite, o3 wins 5 benchmarks, Devstral 2 2512 wins 2, and the two tie on 5.

Where o3 wins:

• Tool calling (5 vs 4): o3 ties for 1st among 54 models; Devstral ranks 18th of 54. For agentic workflows where function selection and argument accuracy determine whether a pipeline succeeds or fails, this gap is operationally significant.
• Agentic planning (5 vs 4): o3 ties for 1st among 54 models; Devstral ties for 16th of 54. Better goal decomposition and failure recovery makes o3 more reliable in multi-step autonomous tasks.
• Strategic analysis (5 vs 4): o3 ties for 1st among 54 models; Devstral ranks 27th of 54. On nuanced tradeoff reasoning with real numbers, o3 is clearly in the top tier.
• Faithfulness (5 vs 4): o3 ties for 1st among 55 models; Devstral ranks 34th of 55. For RAG applications or any task where sticking to source material matters, o3 hallucinates less in our tests.
• Persona consistency (5 vs 4): o3 ties for 1st among 53 models; Devstral ranks 38th of 53. Character maintenance and resistance to prompt injection is a meaningful advantage for customer-facing AI applications.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 4): Devstral ties for 1st among 53 models; o3 ranks 6th of 53. At hard character limits — ad copy, metadata, headlines — Devstral is demonstrably tighter.
• Long context (5 vs 4): Devstral ties for 1st among 55 models; o3 ranks 38th of 55. At 30K+ token retrieval tasks, Devstral's performance is notably stronger. Combined with its 262K context window versus o3's 200K, this makes Devstral the better choice for large-document workflows.

Ties (both score equally):

• Structured output (5/5): Both tie for 1st among 54 models — JSON schema compliance is a wash.
• Creative problem solving (4/4): Both rank 9th of 54.
• Classification (3/3): Both rank 31st of 53 — neither excels here.
• Safety calibration (1/1): Both rank 32nd of 55 — a shared weakness worth noting for regulated use cases.
• Multilingual (5/5): Both tie for 1st among 55 models.

External benchmarks (Epoch AI data): o3 scores 62.3% on SWE-bench Verified, ranking 9th of 12 models with that score in our dataset — placing it in the lower half of the SWE-bench leaderboard we track, though above the median of 70.8%... wait, 62.3% is actually below the p50 of 70.8% for SWE-bench Verified across our dataset. On MATH Level 5, o3 scores 97.8%, ranking 2nd of 14 models tracked (3 models share this score) — well above the p50 of 94.15%. On AIME 2025, o3 scores 83.9%, ranking 12th of 23 models tracked — exactly at the p50 of 83.9%. Devstral 2 2512 has no external benchmark scores in our dataset. These external scores paint o3 as a strong math model but not the top SWE-bench performer among models we track.

Devstral 2 2512 costs $0.40/M input and $2.00/M output tokens. o3 costs $2.00/M input and $8.00/M output tokens — exactly 5x more on input and 4x more on output. At real-world volumes, that gap compounds fast: at 1M output tokens/month, you're paying $2 vs $8 — a $6 difference that's negligible for most teams. At 10M output tokens/month, that's $20K vs $80K annually — now a meaningful budget line. At 100M output tokens/month, Devstral saves roughly $600K per year over o3. Developers running high-throughput pipelines — document processing, code generation at scale, batch summarization — should take that gap seriously. Teams running lower volumes where quality on tool calling or agentic tasks matters more than cost will find o3's premium justifiable. One important caveat: o3 supports image and file inputs (text+image+file->text modality) while Devstral 2 2512 is text-only — if your pipeline requires multimodal inputs, o3 is the only option here regardless of price.

Choose Devstral 2 2512 if: Your workload is primarily long-document processing, large-context retrieval, or constrained text editing (ad copy, metadata, character-limit rewrites). You're running at 10M+ output tokens/month and the ~4x output cost difference is meaningful to your budget. Your pipeline is text-only and you don't need image or file input support. You want a 262K context window over o3's 200K.

Choose o3 if: You're building agentic systems where tool calling accuracy and multi-step planning determine success. Your application requires high faithfulness to source material (RAG, summarization, document Q&A). You need persona consistency for customer-facing deployments. You need multimodal inputs (images, files) — Devstral 2 2512 does not support these. You're doing math-heavy work: o3 scores 97.8% on MATH Level 5 and 83.9% on AIME 2025 (Epoch AI). Volume is low enough that the 4x output cost premium ($8 vs $2/M tokens) fits your budget.