Devstral 2 2512 vs GPT-5.2

Across our 12-test suite, GPT-5.2 wins 7 benchmarks, Devstral 2 2512 wins 2, and they tie on 3. Here's what that looks like test by test:

Where GPT-5.2 wins:

• Agentic planning (5 vs 4): GPT-5.2 ties for 1st among 15 models at the top score; Devstral 2 2512 sits at rank 16 of 54. For goal decomposition and failure recovery in autonomous workflows, GPT-5.2 has a measurable edge.
• Creative problem solving (5 vs 4): GPT-5.2 ties for 1st with 7 other models; Devstral 2 2512 is rank 9 of 54. Not a huge gap in rank, but the score difference is real.
• Faithfulness (5 vs 4): GPT-5.2 ties for 1st with 32 other models; Devstral 2 2512 ranks 34 of 55. In RAG pipelines or document Q&A, GPT-5.2's higher faithfulness score means fewer hallucinated citations.
• Classification (4 vs 3): GPT-5.2 ties for 1st with 29 models; Devstral 2 2512 ranks 31 of 53. Devstral 2 2512's score of 3 sits below the 50th percentile for this benchmark.
• Safety calibration (5 vs 1): This is the starkest gap. GPT-5.2 ties for 1st with 4 other models out of 55; Devstral 2 2512 ranks 32 of 55 with a score of 1 — well below the 25th percentile (p25: 1, p50: 2). For customer-facing applications, this difference is critical.
• Persona consistency (5 vs 4): GPT-5.2 ties for 1st with 36 models; Devstral 2 2512 ranks 38 of 53. Relevant for chatbot and assistant use cases.
• Strategic analysis (5 vs 4): GPT-5.2 ties for 1st with 25 models; Devstral 2 2512 ranks 27 of 54. GPT-5.2 edges ahead on nuanced tradeoff reasoning.

Where Devstral 2 2512 wins:

• Structured output (5 vs 4): Devstral 2 2512 ties for 1st with 24 models; GPT-5.2 ranks 26 of 54. JSON schema compliance matters for any pipeline that parses model responses programmatically — and here Devstral 2 2512 outperforms.
• Constrained rewriting (5 vs 4): Devstral 2 2512 ties for 1st with 4 other models out of 53; GPT-5.2 ranks 6 of 53. Compression within hard character limits is a meaningful win for content workflows.

Ties (both score equally):

• Tool calling (both 4/5): Both rank 18 of 54 in the same 29-model group. No differentiation here.
• Long context (both 5/5): Both tie for 1st with 36 other models out of 55. With 256K and 400K context windows respectively, neither is a bottleneck.
• Multilingual (both 5/5): Both tie for 1st with 34 other models out of 55.

External benchmarks (Epoch AI data): GPT-5.2 scores 73.8% on SWE-bench Verified (rank 5 of 12 models tested) and 96.1% on AIME 2025 (rank 1 of 23 models tested — sole holder of that score). These third-party results place GPT-5.2 among the strongest coding and math models by external measure. Devstral 2 2512 has no external benchmark scores in our dataset, so direct comparison on those axes is not possible.

The price gap here is substantial. Devstral 2 2512 costs $0.40/M input and $2.00/M output tokens. GPT-5.2 costs $1.75/M input and $14.00/M output tokens — making output 7x more expensive.

At real-world volumes, that gap compounds quickly:

• 1M output tokens/month: Devstral 2 2512 costs $2.00; GPT-5.2 costs $14.00. A $12 difference — trivial for most.
• 10M output tokens/month: Devstral 2 2512 costs $20; GPT-5.2 costs $140. A $120/month gap that starts to matter for indie developers.
• 100M output tokens/month: Devstral 2 2512 costs $200; GPT-5.2 costs $1,400. A $1,200/month difference that is a real budget line item for production applications.

Developers running high-throughput pipelines — automated code review, document processing, agentic workflows at scale — should take the cost difference seriously. GPT-5.2's superior benchmark scores may be worth the premium at low volumes, but at 100M+ tokens/month, you need strong justification to pay 7x more. If your pipeline leans on structured output or constrained rewriting (where Devstral 2 2512 scores 5/5 vs GPT-5.2's 4/5), the cost argument for Devstral 2 2512 becomes even clearer.

Choose Devstral 2 2512 if:

• Your pipeline is structured-output-heavy — JSON parsing, schema-compliant generation — where it scores 5/5 vs GPT-5.2's 4/5.
• You need constrained rewriting at scale (character-limit compression, templated content), where it ties for 1st of 53 models.
• You're running high-volume API workloads (10M+ output tokens/month) where the 7x output cost difference ($2 vs $14/M tokens) materially affects your budget.
• Safety calibration is not a hard requirement for your use case — Devstral 2 2512 scores 1/5 here, well below the field median.

Choose GPT-5.2 if:

• Safety calibration is non-negotiable — its 5/5 score (tied for 1st of 55) vs Devstral 2 2512's 1/5 is not a minor gap.
• You need strong agentic planning for autonomous, multi-step workflows (5 vs 4 in our tests).
• Faithfulness to source material matters — RAG pipelines, document Q&A, legal or medical summarization (5 vs 4).
• You need multimodal input: GPT-5.2 accepts text, image, and file inputs; Devstral 2 2512 is text-only per our data.
• Math-intensive tasks are in scope — GPT-5.2 scores 96.1% on AIME 2025, ranking 1st of 23 models tested (Epoch AI).
• Volume is low enough that the 7x output cost premium doesn't materially affect your budget.