GPT-5 vs Mistral Small 4

Score-by-score (our 12-test suite):

• Tool calling: GPT-5 5 vs Mistral 4. GPT-5 ties for 1st (tied with 16 others of 54), meaning better function selection and argument accuracy for tool-enabled apps.
• Long context: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (36 others of 55) — stronger retrieval at 30K+ tokens.
• Faithfulness: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (32 others of 55) — less hallucination risk on source-based tasks.
• Classification: GPT-5 4 vs Mistral 2. GPT-5 tied for 1st (29 others of 53); Mistral ranks 51 of 53 — GPT-5 is clearly better for routing and label accuracy.
• Strategic analysis: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (25 others of 54) — stronger on nuanced tradeoff reasoning.
• Agentic planning: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st — better at goal decomposition and recovery.
• Constrained rewriting: GPT-5 4 vs Mistral 3. GPT-5 ranks 6 of 53 vs Mistral 31 — better at tight character/format constraints.
• Structured output: tie 5/5; both are tied for 1st (24 others of 54) — both reliably follow JSON/schema outputs.
• Creative problem solving: tie 4/4; both rank 9 of 54 — equal for non-obvious idea generation.
• Persona consistency: tie 5/5; both tied for 1st — both maintain character well.
• Multilingual: tie 5/5; both tied for 1st — both perform well in non-English languages.
• Safety calibration: tie 2/2; both rank 12 of 55 — similar refusal vs permissive behavior. External benchmarks (supplementary, Epoch AI): GPT-5 scores 73.6% on SWE-bench Verified, 98.1% on MATH Level 5, and 91.4% on AIME 2025 — these external results support GPT-5’s strong coding/math and reasoning performance (attribution: SWE-bench Verified / MATH Level 5 / AIME 2025 from Epoch AI). Mistral Small 4 has no external scores in the payload. Overall, GPT-5 wins 7 tests, Mistral wins 0, and 5 tests tie — so GPT-5 is the benchmark winner on the majority of our suite, especially for tool integration, long-context tasks, and faithfulness.

Using the payload's per_mtok prices (assumed per 1,000-token unit as a common convention): GPT-5 charges $1.25 input and $10.00 output per mTok; Mistral Small 4 charges $0.15 input and $0.60 output per mTok. That yields per-million-token (1,000 × per_mTok) costs: GPT-5 output ≈ $10,000 and input ≈ $1,250 (combined ≈ $11,250 for 1M in + 1M out); Mistral output ≈ $600 and input ≈ $150 (combined ≈ $750). At scale: for equal 1M in+1M out units, GPT-5 ≈ $11,250 vs Mistral ≈ $750; at 10M in+10M out GPT-5 ≈ $112,500 vs Mistral ≈ $7,500; at 100M GPT-5 ≈ $1,125,000 vs Mistral ≈ $75,000. The ~16.67× output price ratio (10 / 0.6) means teams with heavy token use (APIs, chatbots, high-throughput pipelines) will see large monthly cost differences; small projects or low-volume research users may prefer GPT-5’s quality, while high-volume or budget-constrained production should consider Mistral Small 4.

Choose GPT-5 if you need the best tool calling, long-context retrieval, classification accuracy, strategic reasoning, and faithfulness for production systems and you can absorb much higher per-token costs. Choose Mistral Small 4 if you must optimize cost at scale (≈16.67× cheaper output price) while retaining top-tier structured output, creative problem solving, persona consistency, and multilingual quality — ideal for high-volume chat, localized apps, or price-sensitive deployments.