Devstral 2 2512 vs GPT-4.1 Nano

Across our 12-test suite, Devstral 2 2512 wins 5 benchmarks, GPT-4.1 Nano wins 2, and they tie on 5.

Where Devstral 2 2512 leads:

• Strategic analysis: 4 vs 2. This is the largest gap — Devstral 2 2512 ranks 27th of 54 on this dimension while GPT-4.1 Nano ranks 44th of 54. For nuanced tradeoff reasoning with real numbers, Devstral 2 2512 is substantially more capable in our testing.
• Creative problem-solving: 4 vs 2. Devstral 2 2512 ranks 9th of 54 (tied with 20 others); GPT-4.1 Nano ranks 47th of 54. GPT-4.1 Nano's score of 2 falls well below the field median of 4, meaning it underperforms most models on generating non-obvious, feasible ideas.
• Constrained rewriting: 5 vs 4. Devstral 2 2512 is tied for 1st of 53 models (with 4 others); GPT-4.1 Nano ranks 6th of 53. Both are strong here, but Devstral 2 2512 has the edge for compression tasks with hard character limits.
• Long context: 5 vs 4. Devstral 2 2512 ties for 1st of 55 models; GPT-4.1 Nano ranks 38th of 55. At 30K+ token retrieval accuracy, Devstral 2 2512 is in the top tier. This matters for agentic coding or document analysis over large codebases.
• Multilingual: 5 vs 4. Devstral 2 2512 ties for 1st of 55; GPT-4.1 Nano ranks 36th of 55. For non-English output quality, Devstral 2 2512 is the clear choice.

Where GPT-4.1 Nano leads:

• Faithfulness: 5 vs 4. GPT-4.1 Nano ties for 1st of 55 (with 32 others); Devstral 2 2512 ranks 34th of 55. For RAG pipelines or summarization tasks where sticking to source material without hallucination is critical, GPT-4.1 Nano's score is more reliable in our tests.
• Safety calibration: 2 vs 1. Neither model scores well here — both are below the field median of 2 — but GPT-4.1 Nano ranks 12th of 55 while Devstral 2 2512 ranks 32nd of 55. Devstral 2 2512's score of 1 places it in the bottom quarter of all models tested. This is a meaningful gap for any application where appropriate refusal behavior matters.

Ties (both score equally):

• Structured output: both 5/5, tied for 1st of 54. JSON schema compliance is a non-differentiator.
• Tool calling: both 4/5, rank 18 of 54. Equivalent for agentic function-calling workflows.
• Classification: both 3/5, rank 31 of 53. Both are mid-field here.
• Persona consistency: both 4/5, rank 38 of 53.
• Agentic planning: both 4/5, rank 16 of 54.

External benchmarks (Epoch AI): GPT-4.1 Nano has scores on two third-party benchmarks not available for Devstral 2 2512. On MATH Level 5, GPT-4.1 Nano scores 70% — ranking 11th of 14 models with this data, below the field median of 94.15%. On AIME 2025, it scores 28.9%, ranking 20th of 23 models with this data, well below the median of 83.9%. These scores confirm that GPT-4.1 Nano is not a strong choice for competition-level mathematics. No external benchmark data is available for Devstral 2 2512 in our payload.

GPT-4.1 Nano costs $0.10/MTok input and $0.40/MTok output. Devstral 2 2512 costs $0.40/MTok input and $2.00/MTok output. On output tokens — where most cost accumulates in generative workloads — Devstral 2 2512 is exactly 5x more expensive.

At real-world volumes:

• 1M output tokens/month: GPT-4.1 Nano costs $0.40; Devstral 2 2512 costs $2.00. Difference: $1.60.
• 10M output tokens/month: GPT-4.1 Nano costs $4.00; Devstral 2 2512 costs $20.00. Difference: $16.00.
• 100M output tokens/month: GPT-4.1 Nano costs $400; Devstral 2 2512 costs $2,000. Difference: $1,600/month.

For consumer-facing apps or high-volume classification/routing pipelines, that $1,600/month gap at 100M tokens is hard to justify unless Devstral 2 2512's capabilities are genuinely required. Developers running agentic coding workflows with long context and complex planning tasks will find more value in the premium. Budget-sensitive teams, startups, or anyone running lightweight text tasks should default to GPT-4.1 Nano.

Choose Devstral 2 2512 if:

• Your primary workload is agentic coding, complex analysis, or long-document retrieval — its 5/5 long-context score (tied 1st of 55) and 4/5 strategic analysis (rank 27 of 54, vs GPT-4.1 Nano's 2/5 at rank 44) are built for these tasks.
• You need strong multilingual output. Its 5/5 score ties for 1st of 55 models; GPT-4.1 Nano scores 4 at rank 36.
• You're building creative applications requiring non-obvious ideation — GPT-4.1 Nano's 2/5 creative problem-solving (rank 47 of 54) falls significantly short.
• The 5x output cost premium ($2.00 vs $0.40/MTok) is acceptable relative to the capability gains above.

Choose GPT-4.1 Nano if:

• You need multimodal input: GPT-4.1 Nano accepts text, images, and files; Devstral 2 2512 is text-only.
• Your application is faithfulness-sensitive (RAG, summarization, document Q&A) — GPT-4.1 Nano ties for 1st of 55 on faithfulness (5/5) vs Devstral 2 2512's 4/5 at rank 34.
• Cost efficiency is a priority. At 100M output tokens/month, GPT-4.1 Nano saves $1,600 vs Devstral 2 2512.
• You need a larger context window — GPT-4.1 Nano's 1M token context far exceeds Devstral 2 2512's 256K for extremely long document work.
• Safety calibration matters for your deployment: GPT-4.1 Nano scores 2/5 (rank 12 of 55) vs Devstral 2 2512's 1/5 (rank 32 of 55).
• You need math capabilities: GPT-4.1 Nano has documented scores on MATH Level 5 (70%) and AIME 2025 (28.9%); Devstral 2 2512 has no external math benchmark data in our payload.