Claude Haiku 4.5 vs Claude Sonnet 4.6 for Math

What Math demands: accurate stepwise reasoning, symbolic manipulation, long derivations, consistent structured outputs (for solution steps and final answers), tool calling or function use for calculators, and faithfulness to avoid hallucinated steps. In our data the canonical internal signals are strategic_analysis (reasoning), structured_output (format adherence), tool_calling, long_context, faithfulness, and creative_problem_solving (non-obvious but correct approaches). There is no primary MATH Level 5 external score provided for either model in the payload, so we rely on our internal benchmarks as the comparator. In our testing both models score 5/5 on strategic_analysis and 4/5 on structured_output, meaning they handle multi-step reasoning and output schemas equivalently. Sonnet’s advantages are a 5/5 creative_problem_solving score (Haiku 4/5) and a much higher safety_calibration (5/5 vs Haiku’s 2/5), which improves robustness on adversarial or ambiguous math prompts. Additionally, Sonnet reports 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI) — supplementary external evidence of strong coding/problem-solving and contest-style math performance. Haiku offers a cost and efficiency advantage (input: 1 vs 3 and output: 5 vs 15 ¢/mTok), and still ties Sonnet on many core benchmarks, so it’s viable where budget and latency matter more than the top creative/math edge.

1. Competition math / AIME-style problems: Sonnet shines — in our testing it scored 5/5 creative_problem_solving and Sonnet also posts 85.8% on AIME 2025 (Epoch AI). Choose Sonnet when you need higher success on nonstandard, contest-level reasoning. 2) Multi-step derivations with strict formatting (solutions for publications or graders): Both models tie at 5/5 strategic_analysis and 4/5 structured_output in our testing, so either model can produce stepwise solutions and valid JSON/structured answers. 3) Adversarial or ambiguous prompts (requests that probe boundary conditions or try to induce incorrect permissive answers): Sonnet’s 5/5 safety_calibration vs Haiku’s 2/5 makes Sonnet much more reliable at correct refusals and safe behavior in our tests. 4) High-volume tutoring or interactive chat where latency/cost matter: Haiku is cheaper (input/output costs 1/5 ¢/mTok) and is described as faster and more efficient — pick Haiku when you need many cheap turns and can accept a small drop on creative_problem_solving and safety. 5) Large-context walkthroughs or project-scale math (long derivations, massive context): Both models have long_context 5/5, but Sonnet’s larger context window (1,000,000 tokens vs Haiku’s 200,000) and higher max output tokens (128k vs 64k) favor Sonnet for extremely long proofs or notebooks.

For Math, choose Claude Haiku 4.5 if you need lower-cost, lower-latency inference for many short-to-medium math tasks and can accept a small drop in creative problem solving and safety behavior. Choose Claude Sonnet 4.6 if you need stronger contest-level reasoning, safer handling of adversarial or ambiguous math prompts, or longer-context/large-output proofs — Sonnet leads Haiku by +1 on creative_problem_solving and +3 on safety in our testing and also posts 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI).