Claude Haiku 4.5 vs Devstral Medium for Math

What Math demands: precise stepwise reasoning, reliable numeric fidelity, and the ability to present answers in strict formats (e.g., exact expressions or JSON). For this task we treat strategic_analysis as the primary internal proxy (nuanced tradeoff reasoning with real numbers) and structured_output as a critical secondary capability (JSON/schema compliance). External resource: the dataset includes MATH Level 5 (Epoch AI) as the authoritative external benchmark, but scoreA and scoreB are null — we therefore lead with our internal results. Key supporting capabilities in our tests: faithfulness (avoids hallucinated numeric steps), tool_calling (selecting/using calculators or numeric tools), long_context (holds multi-step derivations across many tokens), agentic_planning (decomposes problems), and creative_problem_solving (non-obvious strategies). Claude Haiku 4.5: strategic_analysis 5, structured_output 4, faithfulness 5, tool_calling 5, long_context 5, agentic_planning 5, creative_problem_solving 4. Devstral Medium: strategic_analysis 2, structured_output 4, faithfulness 4, tool_calling 3, long_context 4, agentic_planning 4, creative_problem_solving 2. Those internal scores explain why Haiku handles complex, multi-step math more reliably; structured_output parity means both can meet format requirements equally well.

Where Claude Haiku 4.5 shines (based on scores): - Multi-step contest problems requiring tradeoffs or case analysis (strategic_analysis 5 vs 2); Haiku is more likely to select correct decomposition strategies. - Long derivations or proofs needing token continuity (long_context 5 vs 4) and faithful intermediate calculations (faithfulness 5 vs 4). - Workflows that call external numeric tools or sequence functions (tool_calling 5 vs 3). Where Devstral Medium is appropriate (based on scores and cost): - Short to medium complexity math tasks with strict formatting needs — both models tie on structured_output (4/5), so Devstral can produce compliant JSON or answer templates. - Cost-sensitive batch jobs or inference where deep strategic reasoning is not required: Devstral is cheaper (input_cost_per_mtok: 0.4 vs 1; output_cost_per_mtok: 2 vs 5). Concrete grounded differences: strategic_analysis 5 vs 2 is a 3-point internal gap favoring Haiku for reasoning-heavy math; tool_calling 5 vs 3 indicates Haiku is materially better at orchestrating calculator/tool steps; structured_output 4 vs 4 shows both meet format constraints equally.

For Math, choose Claude Haiku 4.5 if you need deep, multi-step mathematical reasoning, reliable symbolic/numeric fidelity, tool orchestration, or long derivations (strategic_analysis 5 vs 2, tool_calling 5 vs 3, faithfulness 5 vs 4). Choose Devstral Medium if you need cheaper inference (input 0.4 vs 1, output 2 vs 5 per mTok), your problems are shorter or format-driven (structured_output tie 4/5), and you can tolerate weaker strategic reasoning.