Claude Haiku 4.5 vs Codestral 2508 for Math

What Math demands: precise multi-step reasoning, robust plan decomposition, faithful step-by-step derivations, and reliable adherence to requested output formats (for automatic grading or extraction). When an external benchmark exists we lead with it — the payload includes math_level_5 (MATH Level 5, Epoch AI) as the primary external measure, but scoreA and scoreB are null (no external scores for either model), so that external signal is unavailable. Our internal tests therefore become the primary evidence: the task uses two test names in our suite—strategic_analysis and structured_output. In our testing Haiku 4.5 scores 5 on strategic_analysis vs Codestral 2508's 2, indicating much stronger high-level mathematical planning. Codestral 2508 scores 5 vs Haiku's 4 on structured_output, meaning it is marginally better at exact schema/format compliance. Supporting metrics: both models tie at tool_calling (5) and faithfulness (5), and both handle long_context (5), so neither is disadvantaged for long derivations or tool-assisted calculation. Haiku's advantages in strategic_analysis (5 vs 2), creative_problem_solving (4 vs 2), and agentic_planning (5 vs 4) explain its overall superiority for pure mathematical reasoning in our suite.

When to pick Haiku 4.5 (examples tied to scores): - Multi-step olympiad-style proofs or non-obvious solution strategies: Haiku 4.5 scored 5 on strategic_analysis vs Codestral 2508's 2 in our tests, so it is likelier to produce correct high-level plans and nontrivial solution paths. - Error-checking, counterexample search, and alternative proofs: Haiku's creative_problem_solving 4 vs 2 means better generation of feasible alternate approaches. - Long, decomposed solutions or failure-recovery in multi-step problems: Haiku's agentic_planning 5 vs 4 supports task decomposition and robust step sequencing. When to pick Codestral 2508 (examples tied to scores and cost): - Strict answer extraction, automated graders, or JSON/CSV outputs where schema compliance is critical: Codestral 2508 scored structured_output 5 vs Haiku's 4, so it has an edge producing exact machine-parseable outputs. - High-volume, low-latency math pipelines (e.g., auto-grading thousands of short problems): Codestral is cheaper—input $0.30 per M-token and output $0.90 per M-token vs Haiku's $1.00 input and $5.00 output—so it lowers running cost substantially. - Short, format-sensitive tasks like unit-tests or fill-in-the-blank math question generation where the primary requirement is format fidelity rather than deep strategy.

For Math, choose Claude Haiku 4.5 if you need superior mathematical planning, multi-step reasoning, or creative problem solving (strategic_analysis 5 vs 2; creative_problem_solving 4 vs 2 in our tests). Choose Codestral 2508 if you prioritize strict schema/format compliance and lower cost (structured_output 5 vs 4; input $0.30/M-token, output $0.90/M-token vs Haiku $1.00/$5.00). Note: neither model has a math_level_5 (MATH Level 5, Epoch AI) score in the payload, so external verification is unavailable.