GPT-4.1 vs Mistral Small 3.1 24B

Across our 12-test suite GPT-4.1 wins 9 categories; Mistral wins none; 3 are ties. Detailed comparisons (our testing):

• Tool calling: GPT-4.1 5 vs Mistral 1. GPT-4.1 is tied for 1st of 54 (tied with 16), while Mistral ranks 53 of 54 — this matters for function selection and argument accuracy in agent workflows. Mistral also has a 'no_tool calling' quirk in the payload.
• Faithfulness: GPT-4.1 5 vs Mistral 4. GPT-4.1 is tied for 1st of 55 (tied with 32); expect fewer hallucinations in grounded tasks with GPT-4.1.
• Persona consistency: GPT-4.1 5 vs Mistral 2. GPT-4.1 tied for 1st of 53; Mistral ranks 51 of 53 — GPT-4.1 is clearly better for chatbots that must maintain character and resist prompt injection.
• Multilingual: GPT-4.1 5 vs Mistral 4. GPT-4.1 tied for 1st of 55; use GPT-4.1 for higher-quality non-English output in our tests.
• Long context: GPT-4.1 5 vs Mistral 5 — tie. Both are tied for 1st of 55 (GPT-4.1 tied with 36). This indicates both handle 30K+ token retrieval well in our testing.
• Strategic analysis: GPT-4.1 5 vs Mistral 3. GPT-4.1 tied for 1st of 54; expect stronger nuanced tradeoff reasoning with GPT-4.1.
• Constrained rewriting: GPT-4.1 5 vs Mistral 3. GPT-4.1 tied for 1st of 53; better for hard character-limited compression tasks.
• Creative problem solving: GPT-4.1 3 vs Mistral 2. GPT-4.1 ranks higher (rank 30 of 54) — more useful for specific feasible idea generation in our tests.
• Classification: GPT-4.1 4 vs Mistral 3. GPT-4.1 tied for 1st of 53; Mistral rank 31 of 53 — GPT-4.1 more accurate at routing and categorization in our benchmarks.
• Agentic planning: GPT-4.1 4 vs Mistral 3. GPT-4.1 rank 16 of 54; Mistral rank 42 of 54 — GPT-4.1 better at goal decomposition and recovery.
• Structured output: tie 4 vs 4. Both rank 26 of 54 (tied) — both comparable for JSON/schema tasks in our tests.
• Safety calibration: tie 1 vs 1. Both rank 32 of 55 (many models share this score) — similar refusal/permission behavior in our testing. External benchmarks: GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 (Epoch AI); Mistral has no external scores in the payload. These external numbers (Epoch AI) are supplementary evidence for GPT-4.1's coding/math strengths and should be read alongside our 1-5 internal scores.

Prices in the payload are per mTok (1,000 tokens). Combined input+output cost per 1,000 tokens: GPT-4.1 = $2 + $8 = $10.00; Mistral Small 3.1 24B = $0.35 + $0.56 = $0.91. At typical monthly volumes: 1M tokens (1,000 mTok) => GPT-4.1 ~$10,000; Mistral ~$910. At 10M tokens => GPT-4.1 ~$100,000; Mistral ~$9,100. At 100M tokens => GPT-4.1 ~$1,000,000; Mistral ~$91,000. The price ratio in the payload is 14.2857, so GPT-4.1 costs ~14.3x more per token. Who should care: teams doing high-volume inference (10M+ tokens/month), embedded SaaS, or consumer apps where cost dominates should strongly consider Mistral for cost savings. Teams that require tool calling, strict faithfulness, or persona consistency should budget for GPT-4.1 despite the higher cost.

Choose GPT-4.1 if: you need robust tool calling, high faithfulness, persona consistency, multilingual quality, or agentic/strategic planning in production — it won 9 of 12 benchmarks in our testing and ranks top in faithfulness, tool calling, and persona consistency. Budget accordingly: expect ~$10,000 per 1M tokens. Choose Mistral Small 3.1 24B if: you need long-context multimodal processing at a fraction of the cost (no tool calling), or you are optimizing for per-token spend — it costs ~$910 per 1M tokens and ties with GPT-4.1 on long-context retrieval. Avoid Mistral when tool calling, agentic planning, or strict persona adherence are required.