Devstral 2 2512 vs GPT-4.1 Mini

We ran both models across our 12-test suite and compared scores and rankings from our testing. Key wins and ties: - Devstral 2 2512 wins structured output (score 5 vs GPT's 4) — in our testing Devstral is tied for 1st of 54 models for structured output, which matters when you need strict JSON/schema adherence. - Devstral also wins constrained rewriting (5 vs 4) — tied for 1st of 53, which shows it compresses content reliably under hard limits. - Devstral wins creative problem solving (4 vs 3); it ranks 9th of 54 in that task vs GPT-4.1 Mini's rank 30, so expect more non-obvious, feasible ideas from Devstral in our tests. - GPT-4.1 Mini wins safety calibration (2 vs 1) and persona consistency (5 vs 4). GPT's safety calibration rank is 12 of 55 (vs Devstral rank 32), and persona consistency is tied for 1st — useful when refusal behavior or character fidelity is critical. - The models tie on many practical dimensions in our testing: strategic analysis (4), tool calling (4), faithfulness (4), classification (3), long context (5), agentic planning (4), and multilingual (5). Those ties mean both models are broadly comparable for long-context retrieval, multilingual output, and tool sequencing in our suite. - External benchmarks (supplementary): GPT-4.1 Mini scores 87.3% on MATH Level 5 and 44.7% on AIME 2025 — according to Epoch AI — which provides extra context for math-heavy tasks. Overall, Devstral's strengths in structured output and constrained rewriting make it superior where exact format and tight limits matter; GPT-4.1 Mini trades a modest drop on those tasks for better safety calibration, persona consistency, multimodal inputs, and lower output cost.

Both models charge $0.40/mtok for input. Devstral 2 2512 charges $2.00/mtok for output versus GPT-4.1 Mini at $1.60/mtok. Using a 50/50 input/output split as a simple real-world example: 1M tokens/month (500k input + 500k output) costs $1,200 on Devstral 2 2512 and $1,000 on GPT-4.1 Mini. At 10M tokens/month those totals scale to $12,000 (Devstral) vs $10,000 (GPT-4.1 Mini). At 100M tokens/month it's $120,000 vs $100,000. The per-token gap (Devstral ≈1.25× more expensive overall) matters most for high-volume production systems and startups with tight margins; for low-volume prototyping the performance differences may outweigh the cost delta.

Choose Devstral 2 2512 if: - You need top-tier structured outputs (score 5, tied for 1st) or reliable constrained rewriting for character-limited UIs. - You prioritize creative problem generation in our tests (4 vs 3). Choose GPT-4.1 Mini if: - You need better safety calibration and persona consistency (GPT wins these in our tests) or multimodal input support (text+image+file->text). - You operate at scale and want lower output costs ($1.6 vs $2.0 per mtok) or need the external math benchmarks (MATH Level 5 87.3%, AIME 44.7% per Epoch AI).