GPT-4.1 vs Mistral Small 3.2 24B

Overview: In our 12-test suite GPT-4.1 wins 9 benchmarks, Mistral wins 0, and 3 are ties (structured output, safety calibration, agentic planning). Specifics: - Long context: GPT-4.1 scores 5 vs Mistral 4. GPT-4.1 is tied for 1st among 55 models (tied with 36 others), while Mistral ranks 38 of 55 — meaning GPT-4.1 is stronger at retrieval/accuracy over 30K+ tokens. - Tool calling: GPT-4.1 5 vs Mistral 4; GPT-4.1 tied for 1st of 54 (with 16 others) vs Mistral rank 18 of 54 — GPT-4.1 is more reliable at function selection and argument accuracy per our tool calling benchmark. - Faithfulness: 5 vs 4 (GPT-4.1 tied for 1st of 55 with 32 others; Mistral ranks 34 of 55) — GPT-4.1 better resists hallucination and sticks to source material. - Constrained rewriting: 5 vs 4 (GPT-4.1 tied for 1st of 53; Mistral rank 6 of 53) — GPT-4.1 compresses content within hard limits more consistently. - Creative problem solving: 3 vs 2 (GPT-4.1 ranks 30/54 vs Mistral 47/54) — GPT-4.1 produces more feasible, non-obvious ideas in our tests. - Classification: 4 vs 3 (GPT-4.1 tied for 1st of 53; Mistral rank 31/53) — GPT-4.1 is better at routing and categorization tasks. - Persona consistency & multilingual: GPT-4.1 scores 5 vs Mistral 3–4 and ranks tied for 1st — stronger for character maintenance and non-English parity. Ties: structured output (both 4) — both models meet JSON/schema constraints similarly; safety calibration (both 1) — both are cautious on harmful requests in our tests; agentic planning (both 4) — both decompose goals comparably. External benchmarks: Beyond our internal scores, GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on Math Level 5, and 38.3% on AIME 2025 (Epoch AI). Mistral Small 3.2 24B has no SWE-bench/Math/AIME external scores in the provided payload. Practical interpretation: GPT-4.1’s higher ranks and 5/5 marks on long context, tool calling, and faithfulness translate into fewer errors on long-document reasoning, more accurate function calls, and more reliable source grounding. Mistral is competent at structured output and basic agentic planning but trails on multi-step reasoning, creative problem solving, and multilingual/persona consistency.

Listed unit prices: GPT-4.1 input $2.00 / mTok and output $8.00 / mTok; Mistral Small 3.2 24B input $0.075 / mTok and output $0.20 / mTok (priceRatio = 40). Raw costs for 1M tokens (1,000 mTok): GPT-4.1 = input $2,000 + output $8,000 = $10,000 if all tokens are counted as both input+output; Mistral = input $75 + output $200 = $275. For a typical 50/50 input/output split per million tokens: GPT-4.1 ≈ $5,000; Mistral ≈ $137.50. Scale: at 10M tokens/month (50/50) GPT-4.1 ≈ $50,000 vs Mistral ≈ $1,375; at 100M tokens/month GPT-4.1 ≈ $500,000 vs Mistral ≈ $13,750. Who should care: anyone running multi-million-token production workloads (chatbots, ingestion + generation pipelines) — the cost gap (roughly 36–40× at typical splits) quickly becomes the dominant factor. Small-scale experimentation or projects where model accuracy and long-context reasoning are mission critical should prioritize GPT-4.1 despite higher cost.

Choose GPT-4.1 if you need: - Best-in-class long-context reasoning (5/5) for documents, books, or large codebases; - Reliable tool calling and function argument accuracy (5/5); - Strong faithfulness and classification (5/5 and 4/5) for accuracy-sensitive production systems; and you can justify the higher cost (≈ $5,000 per 1M tokens at a 50/50 split). Choose Mistral Small 3.2 24B if you need: - A much lower-cost model for high-volume applications (≈ $137.50 per 1M tokens at a 50/50 split) where budget dominates; - Good structured outputs and instruction-following with improved function calling per its description, and you can accept lower scores on long-context, creative problem solving, and multilingual/persona consistency.