Devstral 2 2512 vs GPT-5 Nano

Across the 12 internal benchmarks where both models were tested, Devstral 2 2512 wins 2, GPT-5 Nano wins 1, and they tie on 9.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 3): Devstral scores 5/5, tied for 1st among 5 models out of 53 tested. GPT-5 Nano scores 3/5, ranking 31st of 53. This is the largest gap in the comparison and matters for tasks requiring precise compression — marketing copy, summaries with hard character limits, or formatting-strict outputs.
• Creative problem solving (4 vs 3): Devstral scores 4/5 (rank 9 of 54, shared with 21 models); GPT-5 Nano scores 3/5 (rank 30 of 54, shared with 17 models). Non-obvious ideation and lateral thinking are meaningfully better with Devstral in our testing.

Where GPT-5 Nano wins:

• Safety calibration (4 vs 1): This is the starkest reversal. GPT-5 Nano scores 4/5 (rank 6 of 55 in our testing), while Devstral 2 2512 scores 1/5 — the bottom quartile (p25 = 1 across all tested models). For any application requiring reliable refusal of harmful prompts alongside permissiveness on legitimate requests, GPT-5 Nano is far better calibrated in our testing.

Where they tie (9 of 12 benchmarks):

• Structured output (5/5 both, tied for 1st with 25 models out of 54): Both handle JSON schema compliance and format adherence at the top level.
• Tool calling (4/4, rank 18 of 54 for both): Equivalent function selection and argument accuracy — no advantage for either in agentic pipelines on this dimension alone.
• Agentic planning (4/4, rank 16 of 54 for both): Goal decomposition and failure recovery are identical.
• Long context (5/5, tied for 1st with 37 models out of 55): Both excel at retrieval at 30K+ tokens. Note that GPT-5 Nano has a larger context window (400K vs 256K tokens).
• Multilingual (5/5, tied for 1st with 35 models out of 55): No differentiation.
• Faithfulness (4/4, rank 34 of 55 for both): Equivalent source adherence.
• Strategic analysis (4/4, rank 27 of 54 for both): Tied on nuanced tradeoff reasoning.
• Persona consistency (4/4, rank 38 of 53 for both): Identical character maintenance scores.
• Classification (3/3, rank 31 of 53 for both): Both are below the median (p50 = 4) on categorization.

External benchmarks (GPT-5 Nano only): On third-party benchmarks sourced from Epoch AI, GPT-5 Nano scores 95.2% on MATH Level 5 (rank 7 of 14 models with this data) and 81.1% on AIME 2025 (rank 14 of 23 models with this data). The median across tested models for MATH Level 5 is 94.15% and for AIME 2025 is 83.9%, putting GPT-5 Nano slightly above median on competition math and slightly below median on olympiad math. No external benchmark data is available for Devstral 2 2512 in our payload.

GPT-5 Nano comes in at $0.05/MTok input and $0.40/MTok output. Devstral 2 2512 costs $0.40/MTok input and $2.00/MTok output — 8× more expensive on input and exactly 5× more expensive on output. In practice: at 1M output tokens/month, GPT-5 Nano costs $0.40 vs Devstral's $2.00 — a $1.60 difference that's negligible. At 10M output tokens, that gap grows to $16. At 100M output tokens — realistic for a production API integration — you're looking at $400 vs $2,000 per month, a $1,600/month difference. Developers running high-throughput pipelines (batch classification, document processing, chatbots) will feel that gap immediately. Devstral 2 2512's pricing is easier to justify for lower-volume, high-value agentic coding workflows where its benchmark advantages are most relevant. GPT-5 Nano's pricing makes it the default for cost-sensitive or high-volume applications.

Choose Devstral 2 2512 if: You are building agentic coding workflows, need strong constrained rewriting (e.g., copy that must fit hard character limits), or require creative problem-solving depth. Its 256K context window and specialized 123B-parameter architecture are purpose-built for coding agent tasks, and it outperforms GPT-5 Nano on two of the twelve benchmarks we tested. Accept the 5× output cost premium only when the task demands Devstral's specific strengths.

Choose GPT-5 Nano if: Safety calibration is non-negotiable for your use case — it scores 4/5 vs Devstral's 1/5 in our testing, a critical gap for consumer-facing or policy-sensitive applications. Also choose GPT-5 Nano when cost matters at scale ($0.40/MTok output vs $2.00), when you need multimodal inputs (image and file support that Devstral 2 2512 lacks), when you want a larger 400K context window, or when you need reasoning token support. For high-volume pipelines — batch classification, document routing, chat interfaces — GPT-5 Nano wins on both economics and safety.