Claude Haiku 4.5 vs DeepSeek V3.1 Terminus for Math

External benchmark context: the payload includes a MATH Level 5 (math_level_5) external benchmark field, but scoreA and scoreB are null — no external scores are available for either model. Therefore our primary evidence is the internal task tests listed for Math (strategic_analysis and structured_output) plus supporting capabilities. What Math demands from an LLM: precise stepwise numeric reasoning, faithful adherence to problem statements (no hallucinations), correct selection and sequencing of tools/calculators when required, and strict formatting when machine-readable outputs (JSON/LaTeX) are needed. In our tests: both models score 5 on strategic_analysis (tie), so they match on nuanced numerical tradeoffs. DeepSeek V3.1 Terminus wins structured_output 5 vs Claude Haiku 4, which favors tasks that require exact JSON/format compliance. Claude Haiku 4.5 scores higher on faithfulness (5 vs 3) and tool_calling (5 vs 3), indicating it is more likely to stick to given premises and invoke functions or calculators correctly — both critical for correct math solutions. Additional relevant differences: Haiku supports text+image->text (useful for parsing diagrams) and a larger context window (200,000 vs 163,840 tokens). DeepSeek is text->text and has top structured-output performance. Cost and token limits also matter: Haiku has higher output cost per mTok (5.00 vs 0.79), so cost-sensitive workloads may favor DeepSeek despite the smaller faithfulness/tool gap.

1. Long, multi-step proof that must avoid hallucination: Choose Claude Haiku 4.5. Evidence: faithfulness 5 vs 3 and strategic_analysis 5 vs 5. Haiku is likelier to keep intermediate steps consistent and correctly reference earlier derivations. 2) Exact machine-readable answer (JSON schema required, autograders or downstream parsers): Choose DeepSeek V3.1 Terminus. Evidence: structured_output 5 vs 4 — DeepSeek is stronger at strict format compliance. 3) Problems requiring external calculators or sequenced tool calls (e.g., compute, then verify, then format): Choose Claude Haiku 4.5. Evidence: tool_calling 5 vs 3; Haiku better at selecting and sequencing function calls. 4) Image-based geometry or diagram parsing: Prefer Claude Haiku 4.5 because it supports text+image->text; DeepSeek is text->text only. 5) Budgeted large-scale grading pipelines where structured JSON answers are the sole requirement: DeepSeek V3.1 Terminus may be preferable because its output cost is lower (output_cost_per_mtok $0.79 vs Claude Haiku $5.00), and it scores 5 on structured_output.

For Math, choose Claude Haiku 4.5 if you need higher faithfulness, reliable tool/calc invocation, image-to-text diagram parsing, or more robust stepwise correctness (faithfulness 5 vs 3; tool_calling 5 vs 3). Choose DeepSeek V3.1 Terminus if your priority is strict machine-readable output and lower per-token output cost (structured_output 5 vs 4; output_cost_per_mtok $0.79 vs $5.00).