Mistral Large 3 2512 vs Mistral Small 3.2 24B

Summary from our 12-test suite: Mistral Large 3 2512 wins 5 tests (structured output 5 vs 4, creative problem solving 3 vs 2, faithfulness 5 vs 4, multilingual 5 vs 4, strategic analysis 4 vs 2). Mistral Small 3.2 24B wins 1 test (constrained rewriting 4 vs 3). Six tests tie (tool calling 4/4, classification 3/3, long context 4/4, safety calibration 1/1, persona consistency 3/3, agentic planning 4/4). Detailed context and impact:

• structured output: Large 3 2512 scores 5 (tied for 1st with 24 others out of 54). This matters for JSON schema compliance and API integrations — Large is stronger at strict format adherence.
• faithfulness: Large 3 2512 scores 5 (tied for 1st with 32 others out of 55) vs Small 4 (rank 34). For tasks requiring minimal hallucination and strict adherence to sources, Large has a measurable edge.
• multilingual: Large 5 (tied for 1st with 34 others out of 55) vs Small 4 (rank 36). Expect higher parity across non-English languages with Large.
• creative problem solving: Large 3 (rank 30 of 54) vs Small 2 (rank 47). Large generates more feasible, non-obvious ideas in our tests.
• strategic analysis: Large 4 (rank 27) vs Small 2 (rank 44). Large better handles nuanced tradeoff reasoning and numeric justification.
• constrained rewriting: Small 3.2 24B wins 4 (rank 6 of 53) vs Large 3 (rank 31). Small is better when compressing or rewriting to strict character limits.
• ties (tool calling, classification, long context, safety calibration, persona consistency, agentic planning): Both models match on these scores; for example tool calling is 4/4 (rank 18 of 54), so function selection and argument accuracy are comparable. Long_context is 4/4 (rank 38 of 55) — both handle 30K+ retrievals similarly in our tests.

Practical takeaway: Large 3 2512 gives higher output fidelity, structured compliance, multilingual performance and reasoning ability; Small 3.2 24B provides a strong, cheaper alternative with a notable advantage on constrained rewriting.

Prices from the payload: Mistral Large 3 2512 input $0.50 / mTok and output $1.50 / mTok; Mistral Small 3.2 24B input $0.075 / mTok and output $0.20 / mTok. Using a simple 50/50 input:output token split as an example, 1M tokens (1,000 mTok) costs: Large 3 2512 = $1,000 (input $250 + output $750); Small 3.2 24B = $137.50 (input $37.50 + output $100). At 10M tokens: Large $10,000 vs Small $1,375. At 100M tokens: Large $100,000 vs Small $13,750. The payload also reports a priceRatio of 7.5. Who should care: teams with high-volume inference (10M+ tokens/month), real-time user-facing apps, or tight margins must account for the Large model’s much higher recurring cost. Experimentation, prototyping, or large-scale chatbots with budget constraints will likely prefer the Small 3.2 24B.

Choose Mistral Large 3 2512 if you need: strict structured outputs (JSON schema compliance), top-tier faithfulness and multilingual parity, or stronger creative/strategic reasoning — and your budget can absorb roughly $1,000 per 1M tokens (50/50 I/O example). Choose Mistral Small 3.2 24B if you need: a far lower cost per token (about $137.50 per 1M tokens in the same 50/50 example), good tool-calling and long-context behavior, or superior constrained rewriting for tight character limits — ideal for high-volume production or cost-sensitive apps.