Mistral Small 4 vs Mistral Small 3.2 24B

All benchmark statements below are from our testing. Overall wins: Mistral Small 4 (A) wins 6 categories, Mistral Small 3.2 24B (B) wins 2, and 4 are ties. Details: - Structured_output: A scores 5 vs B 4; A is tied for 1st in our ranking ("tied for 1st with 24 other models out of 54 tested"). This matters for strict JSON/schema outputs — Small 4 will more reliably follow format constraints. - Creative_problem_solving: A 4 vs B 2; A ranks "rank 9 of 54 (21 models share this score)" while B ranks "rank 47 of 54...". For ideation or non-obvious solutions, Small 4 produces more specific, feasible ideas. - Safety_calibration: A 2 vs B 1; A ranks "rank 12 of 55 (20 models share this score)" vs B "rank 32 of 55...". Small 4 better balances refusal and permitting legitimate requests in our tests. - Persona_consistency: A 5 vs B 3; A is "tied for 1st with 36 other models out of 53 tested" while B is "rank 45 of 53...". If maintaining character/role is important, Small 4 is stronger. - Multilingual: A 5 vs B 4; A is "tied for 1st with 34 other models out of 55 tested" while B is "rank 36 of 55..." — Small 4 gives higher-quality non-English outputs in our suite. - Strategic_analysis: A 4 vs B 2; A "rank 27 of 54" vs B "rank 44 of 54" — for nuanced tradeoff reasoning with numbers, Small 4 is more capable. Where B wins: - Constrained_rewriting: B 4 vs A 3; B ranks "rank 6 of 53 (25 models share this score)" compared to A "rank 31 of 53...". For tight character-limited rewriting (compression), Small 3.2 24B performed better. - Classification: B 3 vs A 2; B is "rank 31 of 53" vs A "rank 51 of 53" — B is more reliable for routing and simple categorization. Ties (identical scores in our tests): tool calling (both 4; both show "rank 18 of 54..."), faithfulness (both 4; A "rank 34 of 55..." and B "rank 34 of 55..."), long context (both 4; both "rank 38 of 55..."), and agentic planning (both 4; both "rank 16 of 54..."). These ties mean both models behave similarly for function selection, retrieval over long context, and goal decomposition in our suite. In short: Small 4 trades higher per-token cost for clearer wins in structured formats, creative problem solving, multilingual output, persona consistency and safety calibration; Small 3.2 24B is cheaper and better at constrained rewriting and basic classification.

Per the payload, Mistral Small 4 costs $0.15 input + $0.60 output = $0.75 per mTok; Mistral Small 3.2 24B costs $0.075 input + $0.20 output = $0.275 per mTok. At 1M tokens/month (1,000 mTok) that is $750 vs $275. At 10M tokens it's $7,500 vs $2,750. At 100M tokens it's $75,000 vs $27,500. The payload lists a priceRatio of 3 (Small 4 is roughly three times the list-rate). Teams with heavy output volumes (analytics, large-scale chat or content generation) should care about the ~2.7–3x cost gap; smaller projects or those that need the specific quality wins of Small 4 may absorb the premium.

Choose Mistral Small 4 if you need reliable schema/JSON outputs, stronger creative problem solving, multilingual parity, better persona consistency, or improved safety calibration — you pay a premium (~$0.75/mTok total). Choose Mistral Small 3.2 24B if your priority is cost-efficiency (about $0.275/mTok total) or you need the best constrained rewriting and classification performance from these two models.