Claude Haiku 4.5 vs Mistral Small 3.1 24B

We ran a 12-test suite and compared per-task scores and rankings from our testing. Summary: Claude Haiku 4.5 wins 9 tests, Mistral Small 3.1 24B wins none, and three tests tie. Detailed walk-through:

• Strategic analysis: Haiku 5 vs Mistral 3. Haiku is tied for 1st (tied with 25 others of 54) while Mistral ranks 36 of 54. For tasks requiring nuanced tradeoff reasoning and numeric judgment, Haiku is substantially stronger.
• Creative problem solving: Haiku 4 (rank 9 of 54) vs Mistral 2 (rank 47). Haiku produces more feasible, non-obvious ideas in our tests.
• Tool calling: Haiku 5 (tied for 1st of 54) vs Mistral 1 (rank 53 of 54). Haiku reliably selects functions, arguments and sequencing; Mistral’s payload also flags a quirk: no_tool_calling = true, matching its weak score. This is the clearest functional gap.
• Faithfulness: Haiku 5 (tied for 1st of 55) vs Mistral 4 (rank 34). Haiku sticks to source material with fewer hallucinations in our tests.
• Classification: Haiku 4 (tied for 1st of 53) vs Mistral 3 (rank 31). For routing and categorization Haiku is more accurate in our suite.
• Safety calibration: Haiku 2 (rank 12 of 55) vs Mistral 1 (rank 32). Haiku is better at refusing harmful requests while permitting legitimate ones, though neither scores high overall.
• Persona consistency: Haiku 5 (tied for 1st of 53) vs Mistral 2 (rank 51). Haiku maintains persona and resists prompt injection much better.
• Agentic planning: Haiku 5 (tied for 1st of 54) vs Mistral 3 (rank 42). Haiku decomposes goals and handles failure recovery more effectively.
• Multilingual: Haiku 5 (tied for 1st of 55) vs Mistral 4 (rank 36). Haiku gives equivalent-quality non-English outputs more often in our tests.
• Structured output: tie 4 vs 4 (both rank 26 of 54). Both models meet JSON/schema requirements comparably in our testing.
• Constrained rewriting: tie 3 vs 3 (both rank 31 of 53). Both compress or rewrite under hard limits at similar fidelity.
• Long context: tie 5 vs 5 (both tied for 1st of 55). Both handle retrieval/accuracy at 30K+ tokens in our tests. What this means in practice: if your application relies on tool calling/agents, strict faithfulness, persona persistence, or strategic reasoning, Claude Haiku 4.5 demonstrably performs better in our suite. If you need long-context retrieval or schema compliance at far lower cost, Mistral Small 3.1 24B is competitive on those specific dimensions but lacks tool-calling support.

Raw token rates from the payload: Claude Haiku 4.5 charges $1.00 per input mTok and $5.00 per output mTok; Mistral Small 3.1 24B charges $0.35 per input mTok and $0.56 per output mTok. The price ratio in the payload is ~8.93× (Haiku vs Mistral) driven mainly by Haiku’s $5.00 output rate. Example monthly costs (using mTok = 1,000 tokens):

• 1M tokens, 50/50 input/output split: Haiku = $3,000; Mistral = $455.
• 10M tokens, 50/50: Haiku = $30,000; Mistral = $4,550.
• 100M tokens, 50/50: Haiku = $300,000; Mistral = $45,500. If you bill or operate at high volume (millions of tokens/month), Mistral’s rates yield large savings: saving ~$26,450/month at 10M tokens (50/50 split) and ~$254,500/month at 100M. Teams building high-volume chatbots, consumer apps, or cost-sensitive pipelines should care most; teams that need reliable tool calling, highest faithfulness, or top-tier agentic planning (where Haiku leads) may accept the higher spend.

Choose Claude Haiku 4.5 if you need reliable tool calling, high faithfulness, advanced agentic planning, strong multilingual and persona consistency, or the best strategic/creative outputs — and you can absorb higher per-token costs. Choose Mistral Small 3.1 24B if your priority is cost-efficiency at scale (input $0.35/mTok, output $0.56/mTok), you require long-context performance or structured-output parity, and you do not need tool calling or the highest safety/persona guarantees.