Mistral Small 3.2 24B vs o3

We compare the two across our 12-test suite (scores 1–5). In our testing o3 wins 8 tests, Mistral wins 0, and 4 tie. Detailed breakdown (Mistral score → o3 score):

• structured output: 4 → 5. o3 ties for 1st on structured output (rank 1 of 54, tied with 24 others); Mistral ranks 26 of 54. For JSON/schema outputs, o3 is more reliable at schema adherence.
• strategic analysis: 2 → 5. o3 is tied for 1st (rank 1 of 54). Expect better numerical tradeoff reasoning and nuanced planning from o3.
• creative problem solving: 2 → 4. o3 ranks 9 of 54; Mistral ranks 47 of 54. o3 produces more feasible, non‑obvious ideas in our tests.
• tool calling: 4 → 5. o3 is tied for 1st (rank 1 of 54); Mistral is rank 18 of 54. o3 selects and sequences functions with higher accuracy in our tool-calling tasks.
• faithfulness: 4 → 5. o3 is tied for 1st (rank 1 of 55); Mistral ranks 34 of 55. o3 better sticks to source material in our benchmarks.
• persona consistency: 3 → 5. o3 is tied for 1st (rank 1 of 53); Mistral ranks 45 of 53. o3 resists injection and maintains character more strongly.
• agentic planning: 4 → 5. o3 tied for 1st (rank 1 of 54); Mistral rank 16 of 54. Expect more robust goal decomposition and failure recovery from o3.
• multilingual: 4 → 5. o3 tied for 1st (rank 1 of 55); Mistral rank 36 of 55. Non‑English outputs are higher quality on o3 in our tests. Ties (no clear winner in our testing): constrained rewriting 4→4 (both rank 6), classification 3→3 (both rank 31), long context 4→4 (both rank 38), safety calibration 1→1 (both rank 32). External benchmarks (supplementary): according to Epoch AI, o3 scores 62.3% on SWE-bench Verified, 97.8% on MATH Level 5, and 83.9% on AIME 2025 — supporting its strength on coding/math tasks. The payload contains no external benchmark scores for Mistral to compare on those tests. Overall, o3 consistently outperforms Mistral on technical, structured, and multilingual benchmarks in our suite; Mistral remains competitive on a few ties but lags on creative and strategic tasks.

The price gap is huge and material at scale. Token costs from the payload: Mistral Small 3.2 24B input $0.075/mTok and output $0.20/mTok; o3 input $2/mTok and output $8/mTok. Using a 50/50 input/output split (common for chat+completion):

• 1M tokens (500 mTok input + 500 mTok output): Mistral ≈ $137.50; o3 ≈ $5,000. o3 costs ~36x more in this scenario.
• 10M tokens: Mistral ≈ $1,375; o3 ≈ $50,000.
• 100M tokens: Mistral ≈ $13,750; o3 ≈ $500,000. Who should care: startups, high-volume chat services, or any production pipeline serving millions of tokens/month must budget for o3’s dramatically higher bills. The payload’s priceRatio is 0.025, i.e., Mistral is ~2.5% of o3 by the provided ratio — useful when deciding between cost-sensitive scale vs quality-sensitive workloads.

Choose Mistral Small 3.2 24B if: you must minimize inference cost at scale (input $0.075/mTok, output $0.20/mTok), need a capable long-context model, and can accept lower scores on strategic analysis, creative problem solving, and structured output. Choose o3 if: you need the highest quality for structured JSON outputs, tool calling, multilingual output, persona consistency, strategic analysis, or coding/math reliability — o3 wins 8 of 12 tests in our benchmarking but at ~36x the cost under a 50/50 token split.