Claude Haiku 4.5 vs Gemini 2.5 Flash Lite for Math

What Math demands: precision in multi-step reasoning, clear step-by-step explanations, adherence to structured output (for graders or calculators), and the ability to maintain long mathematical contexts (proofs, multi-part problems). Tool-calling (for symbolic engines or calculators) and faithfulness (avoiding hallucinated steps) are also important. The payload includes an external MATH Level 5 benchmark entry but both models have null external scores, so we rely on our internal proxies. On those proxies: strategic_analysis (nuanced tradeoff reasoning with real numbers) is the primary measure for math reasoning here — Claude Haiku 4.5 scores 5 vs Gemini 2.5 Flash Lite's 3. Structured_output (JSON/schema compliance) is tied 4–4, so both models can produce well-formatted answers. Additional supporting signals: Haiku leads on creative_problem_solving (4 vs 3) and agentic_planning (5 vs 4), while both models score 5 on tool_calling and faithfulness and 5 on long_context, indicating both handle long problems and tool workflows well. Safety calibration is higher for Haiku (2 vs 1) in our tests, which affects risky or ambiguous prompts.

1. Hard contest problems (multi-step olympiad-style): Choose Claude Haiku 4.5 — it scores 5 vs 3 on strategic_analysis in our tests, so it better reasons about multi-step tradeoffs and strategy. 2) Graded answers requiring strict JSON output for automated graders: Either model — both tie 4–4 on structured_output, so both can meet schema constraints. 3) Batch numeric verification with external calculators/tools: Either model — both score 5 on tool_calling in our tests, so both reliably select and sequence function calls. 4) Large multi-problem notebooks or long proofs: Both models score 5 on long_context; Gemini’s larger context window (1,048,576 vs 200,000 tokens) gives an engineering advantage if you plan to feed extremely long transcripts. 5) Cost-sensitive large-scale evaluation (automated problem sets): Choose Gemini 2.5 Flash Lite — its input/output costs are 0.1 / 0.4 per mTok vs Haiku’s 1 / 5 per mTok, making it ~12.5× cheaper per the payload priceRatio. 6) Ambiguous or risky prompts where cautious refusal matters: Claude Haiku 4.5 is stronger on safety_calibration in our tests (2 vs 1).

For Math, choose Claude Haiku 4.5 if you need stronger multi-step reasoning and strategy (it scores 5 vs 3 on strategic_analysis in our tests), better creative problem solving, and slightly better safety calibration — accept higher cost. Choose Gemini 2.5 Flash Lite if you need the lowest token cost (input 0.1 / output 0.4 per mTok), the largest raw context window (1,048,576 tokens), or are doing massive automated grading where cost dominates and structured output is sufficient (both models tie on structured_output). Note: an external MATH Level 5 score exists in the payload but has no values for either model, so this recommendation is based on our internal benchmark proxies.