Devstral Small 1.1 vs GPT-5 Nano

Summary of head-to-head results in our 12-test suite (scores on a 1–5 scale from our testing):

• Wins for Devstral Small 1.1: classification 4 vs 3. In our tests Devstral ties for 1st on classification (tied with 29 other models out of 53), so it is a reliable choice for routing, tagging, and categorization tasks.
• Wins for GPT-5 Nano: structured_output 5 vs 4 (GPT-5 Nano tied for 1st with 24 others out of 54), strategic_analysis 4 vs 2 (rank 27 of 54), creative_problem_solving 3 vs 2 (rank 30 of 54), long_context 5 vs 4 (GPT-5 Nano tied for 1st on long-context), safety_calibration 4 vs 2 (rank 6 of 55), persona_consistency 4 vs 2, agentic_planning 4 vs 2 (rank 16 of 54), and multilingual 5 vs 4 (GPT-5 Nano tied for 1st on multilingual). These wins mean GPT-5 Nano is measurably better for tasks that need reliable JSON/schema outputs, handling 30K+ token contexts, multi-language parity, safety-sensitive gating, and multi-step planning.
• Ties: constrained_rewriting 3 vs 3 (both rank 31 of 53), tool_calling 4 vs 4 (both rank 18 of 54), faithfulness 4 vs 4 (both rank 34 of 55). Practically, both models perform equivalently for API tool selection, function-argument accuracy, and sticking to source material.
• External math benchmarks (supplementary, not our internal 1–5 scores): GPT-5 Nano posts 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI). Devstral Small 1.1 has no external math scores in the payload. This external signal corroborates GPT-5 Nano's stronger performance on math and strategic reasoning tasks. Interpretation for real tasks: choose GPT-5 Nano when you need long-context retrieval, structured JSON outputs, multilingual parity, safer refusals, or multi-step agent planning. Choose Devstral where classification accuracy and slightly lower balanced cost matter—or when you want a compact model that still performs solidly on routing and basic tool-calling.

Raw rates from the payload: Devstral Small 1.1 charges $0.10 per mTok input and $0.30 per mTok output; GPT-5 Nano charges $0.05 per mTok input and $0.40 per mTok output. Using a 50/50 input:output token split as a realistic baseline: - 1M tokens (1,000 mTok): Devstral ≈ $200/month (500* $0.10 + 500* $0.30 = $50 + $150). GPT-5 Nano ≈ $225/month (500* $0.05 + 500* $0.40 = $25 + $200). Devstral saves $25/month. - 10M tokens: Devstral ≈ $2,000 vs GPT-5 Nano ≈ $2,250 (save $250). - 100M tokens: Devstral ≈ $20,000 vs GPT-5 Nano ≈ $22,500 (save $2,500). Who should care: teams with very high output-volume workflows (large completions, long generations) will feel GPT-5 Nano's higher $0.40 output rate more; teams whose workloads are input-heavy (many short prompts) benefit from GPT-5 Nano's lower $0.05 input rate. For balanced usage, Devstral is modestly cheaper at scale (≈11% lower cost in the 50/50 example).

Choose Devstral Small 1.1 if: - Your priority is classification/routing workflows (Devstral scores 4 vs GPT-5 Nano's 3 and ties for 1st in classification). - You need modest cost savings on balanced workloads (Devstral’s pricing example: ~$200/mo vs ~$225/mo at 1M tokens with a 50/50 split). - Your use cases are short prompts, label/tag pipelines, or you need a lower output-per-token billing impact. Choose GPT-5 Nano if: - You need best-in-class long-context and multilingual behavior (GPT-5 Nano scores 5 vs 4 and ties for 1st on both). - You require high-quality structured outputs (5 vs 4), stronger safety calibration (4 vs 2), and better agentic planning/strategic analysis. - You benefit from external math performance: GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI).