Claude Opus 4.7 vs Mistral Small 4

Head-to-head by test (scores: Claude Opus 4.7 vs Mistral Small 4), with ranking context and task meaning: 1) Tool calling: 5 vs 4 — Claude wins; ranked “tied for 1st with 17 other models out of 55 tested,” meaning better function selection, argument accuracy, and sequencing. 2) Agentic planning: 5 vs 4 — Claude wins; Claude is “tied for 1st with 15 other models out of 55,” so it decomposes goals and recovers from failures more reliably. 3) Faithfulness: 5 vs 4 — Claude wins; Claude is “tied for 1st with 33 other models out of 56,” so it better sticks to source material and reduces hallucination risk. 4) Structured output: 4 vs 5 — Mistral wins; Mistral is “tied for 1st with 24 other models out of 55,” so it produces more reliable JSON/schema-compliant outputs. 5) Constrained rewriting: 4 vs 3 — Claude wins; Claude ranks 6th of 55, useful when compressing or fitting strict character limits. 6) Creative problem solving: 5 vs 4 — Claude wins; Claude is “tied for 1st with 8 other models,” producing more specific, feasible ideas. 7) Classification: 3 vs 2 — Claude wins; Claude ranks 31/54 while Mistral ranks 52/54, so Claude is safer for routing/categorization. 8) Safety calibration: 3 vs 2 — Claude wins; Claude ranks 10/56 vs Mistral 13/56, meaning better refusal/allow balance. 9) Persona consistency: 5 vs 5 — tie; both are “tied for 1st with 37 others,” so both maintain character well. 10) Multilingual: 4 vs 5 — Mistral wins; Mistral is “tied for 1st with 34 other models out of 56,” so it gives stronger non‑English parity. 11) Strategic analysis: 5 vs 4 — Claude wins; Claude is “tied for 1st with 26 other models,” useful for nuanced tradeoff reasoning. 12) Long context: 5 vs 4 — Claude wins; Claude is “tied for 1st with 37 other models” and also offers a 1,000,000-token context window vs Mistral’s 262,144, making Claude substantially better for retrieval or large-document tasks. Overall, Claude wins 9 tests, Mistral wins 2 (structured output, multilingual), and 1 tie (persona consistency). These differences translate to Claude being stronger for agentic, long-context, and faithfulness-sensitive workflows, and Mistral being the superior choice when schema compliance and multilingual output matter and budget is constrained.

Exact token prices: Claude Opus 4.7 charges $5 per million input tokens and $25 per million output tokens. Mistral Small 4 charges $0.15 per million input tokens and $0.60 per million output tokens. Example combined costs (1M input + 1M output): Claude = $30.00; Mistral = $0.75. At 10M in+out: Claude = $300.00; Mistral = $7.50. At 100M in+out: Claude = $3,000.00; Mistral = $75.00. The per‑token gap (price ratio ~41.67x) means high-volume services, embedded assistants, or any application with heavy output should prefer Mistral to control costs; teams that need Claude’s higher scores for planning, long context, and faithfulness should budget accordingly (Claude becomes materially expensive at scale).

Choose Claude Opus 4.7 if you need best-in-class tool calling, agentic planning, strategic analysis, faithfulness, long-context retrieval (1000000-token window), or higher creative/problem-solving quality and you can absorb higher per-token costs. Choose Mistral Small 4 if you need the best structured-output (JSON) and multilingual performance with a much lower price per token ($0.15/$0.60 vs $5/$25), or if your product is high-volume and cost-sensitive.