Claude Haiku 4.5 vs Claude Opus 4.7 for Math

Math demands stepwise, reliable chain-of-thought, careful numerical reasoning, error checking, the ability to present answers in strict formats (JSON/LaTeX), and sometimes long-context retrieval or external tool use (calculators). An authoritative external benchmark for this task exists (MATH Level 5, Epoch AI), but neither Claude Haiku 4.5 nor Claude Opus 4.7 has a reported MATH Level 5 score in the provided data, so that external signal is unavailable. In our internal 12-test proxies, the most relevant signals are strategic analysis (how well the model reasons about tradeoffs and multi-step numeric reasoning), faithfulness (avoiding hallucinated steps), creative problem solving (non-obvious but correct approaches), constrained rewriting (respecting strict length/format limits), tool calling (accurate function use for calculators), structured output (JSON/LaTeX compliance), and long context. On those proxies: strategic analysis is tied at 5/5 for both models; faithfulness and tool calling are tied at 5/5; structured output is tied at 4/5. Opus leads on creative problem solving (5 vs 4), constrained rewriting (4 vs 3), and safety calibration (3 vs 2). Haiku leads on classification (4 vs 3) and multilingual support (5 vs 4) and is described in the data as Anthropic’s fastest and most efficient model. Cost and context differences are material: Haiku costs $1 input / $5 output per million tokens and offers a 200k token context window; Opus costs $5 input / $25 output per million tokens and offers a 1,000,000 token context window. Use these concrete internal scores and cost/context tradeoffs to match the model to your math workload.

1. Olympiad-style proofs and creative contest problems — Opus 4.7: its creative problem solving score is 5 vs Haiku’s 4 in our tests, so Opus is more likely to propose non-obvious, contest-grade approaches and handle tricky solution paths. 2) Strict answer-format tasks (e.g., compressed solutions for publishing) — Opus 4.7: constrained rewriting is 4 vs Haiku 3, so Opus better preserves correctness under hard character or format limits. 3) Large-context derivations (entire chapter or long proof) — Opus 4.7: both models score 5 on long context, but Opus’s 1,000,000-token window vs Haiku’s 200,000 tokens lets you keep more reference material in-session. 4) High-volume grading, multilingual tutoring, or low-latency workflows — Claude Haiku 4.5: ties Opus on strategic analysis and faithfulness (5/5), is described as Anthropic’s fastest and most efficient model, costs $1 input / $5 output per million tokens (vs Opus $5/$25), and scores 5/5 on multilingual output vs Opus 4/5 — making Haiku the pragmatic choice for budget-sensitive, high-throughput, or multilingual math tasks. 5) Tool-dependent numeric verification — both models score 5/5 on tool calling in our testing, so either can orchestrate calculator calls and function sequences accurately.

For Math, choose Claude Haiku 4.5 if you need a fast, cost-efficient model for high-volume grading, multilingual tutoring, or budget-sensitive pipelines ($1 input / $5 output per million tokens) and you don’t require the final edge in creative solution generation. Choose Claude Opus 4.7 if you’re solving the hardest open-ended or contest-style math problems where creative problem solving (5 vs 4) and strict constraint handling (4 vs 3) matter, and you can justify the premium ($5 input / $25 output per million tokens) and larger 1,000,000-token context window.