Mistral Small 3.1 24B vs o3

Summary of our test-by-test comparison (scores are from our internal 1–5 scale unless otherwise noted). Overall wins/ties: o3 wins 9 tests, Mistral wins 1, and 2 tie. Details: - Tool calling: o3 5 vs Mistral 1 — o3 ranks tied for 1st on tool calling; Mistral has a quirk flag no_tool calling=true in the payload, so it is not suitable for tool-driven agent workflows. - Structured output: o3 5 vs Mistral 4 — in our testing o3 ties for 1st in structured output, meaning better JSON/schema compliance for integrations. - Strategic analysis: o3 5 vs Mistral 3 — o3 ties for 1st on strategic analysis, so it handles nuanced tradeoff reasoning better in real tasks. - Constrained rewriting: o3 4 vs Mistral 3 — o3 ranks 6th of 53, so it compresses within hard limits more reliably. - Creative problem solving: o3 4 vs Mistral 2 — o3 ranks 9th, reflecting stronger idea generation on non-obvious tasks. - Faithfulness: o3 5 vs Mistral 4 — o3 ties for 1st for sticking to source material, reducing hallucination risk in technical outputs. - Persona consistency: o3 5 vs Mistral 2 — o3 ties for 1st, better at maintaining character and resisting injection. - Agentic planning: o3 5 vs Mistral 3 — o3 ties for 1st, useful for goal decomposition and multi-step plans. - Multilingual: o3 5 vs Mistral 4 — o3 ties for 1st, so cross-language parity is stronger in our tests. - Long-context: Mistral 5 vs o3 4 — this is Mistral’s single win; Mistral ties for 1st (with 36 other models) on long-context retrieval (30K+ tokens), so it’s the better pick when very large context windows matter. - Classification and Safety calibration: tie in our testing (classification 3/3, safety calibration 1/1). External benchmarks: on SWE-bench Verified (Epoch AI) o3 scores 62.3%; on MATH Level 5 (Epoch AI) o3 scores 97.8%; on AIME 2025 (Epoch AI) o3 scores 83.9%. Mistral has no external scores in the payload. Practical meaning: choose o3 when you need robust tool integration, schema adherence, multilingual and persona-sensitive outputs, or top-tier reasoning/math performance (see MATH Level 5). Choose Mistral when you need cheaper inference plus best-in-class long-context handling and multimodal text+image->text support but do not require tool calling.

Raw per-mTok prices: Mistral Small 3.1 24B charges $0.35 input / $0.56 output per mTok; o3 charges $2 input / $8 output per mTok. Assuming a 50/50 split of input vs output tokens (state this is an example scenario), per 1M total tokens (1000 mTok) costs are: Mistral ≈ $455 (0.455/mTok average ×1000) vs o3 ≈ $5,000 (5.0/mTok ×1000). At 10M tokens/month: Mistral ≈ $4,550 vs o3 ≈ $50,000. At 100M: Mistral ≈ $45,500 vs o3 ≈ $500,000. The gap is material for any sustained production workload — teams shipping high-volume chat, assistants, or API products should care. If your app is output-heavy (more output tokens than input), o3’s $8/mTok output price increases operating costs further; if inputs dominate, the difference narrows but remains large. Smaller projects, prototypes, or latency-sensitive tasks with huge context needs will find Mistral’s lower price compelling.

Choose Mistral Small 3.1 24B if you need: - Very large context retrieval (it scores 5/5 in long-context and is tied for 1st), - A far lower price point for high-volume workloads ($0.35 input / $0.56 output per mTok), - Multimodal text+image->text without expensive spend. Choose o3 if you need: - Tool calling, structured outputs, agentic planning, persona consistency, and multilingual parity (o3 scores 5 in these and ranks tied for 1st in many), - Strong math/coding performance (o3: MATH Level 5 97.8% and SWE-bench Verified 62.3% per Epoch AI), and you can absorb higher operational cost ($2/$8 per mTok).