Claude Haiku 4.5 vs Devstral 2 2512 for Math
Winner: Claude Haiku 4.5. The external MATH Level 5 benchmark is present in the payload but provides no scores for either model, so our verdict is based on our internal proxies. In our testing Haiku 4.5 scores higher on strategic_analysis (5 vs 4) and faithfulness (5 vs 4) — the two most consequential measures for mathematical reasoning. Haiku also beats Devstral on tool_calling (5 vs 4) and safety_calibration (2 vs 1), which reduces error-prone computations and risky outputs. Devstral 2 2512 wins on structured_output (5 vs 4) and offers a larger context window (262,144 vs 200,000) and lower input/output costs, making it a strong alternative when strict output formats and cost matter. Overall, for pure math reasoning and trustworthy multi-step work, choose Claude Haiku 4.5.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
mistral
Devstral 2 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Task Analysis
What Math demands: rigorous multi-step reasoning, precise symbolic/manipulative steps, error-free arithmetic or tool integration, and an extractable final answer. Key capabilities: strategic_analysis (nuanced tradeoff reasoning with real numbers), faithfulness (sticking to correct steps), structured_output (strict formats for answers or machine parsing), tool_calling (using calculators or CAS accurately), long_context (keeping long derivations), and safety_calibration (refusing bad math/unsafe shortcuts). The payload includes an external benchmark entry (MATH Level 5, Epoch AI) but no scores for either model; therefore we rely on our internal test proxies. In our testing Haiku 4.5: strategic_analysis 5, faithfulness 5, tool_calling 5, structured_output 4, long_context 5, safety_calibration 2. Devstral 2 2512: strategic_analysis 4, faithfulness 4, tool_calling 4, structured_output 5, long_context 5, safety_calibration 1. Those numbers show Haiku has an edge on reasoning rigor and result fidelity; Devstral is superior at strict format compliance and offers cost/context advantages.
Practical Examples
- Olympiad-style multi-step proof: Haiku 4.5 is the safer pick — strategic_analysis 5 and faithfulness 5 in our tests mean clearer decomposition and fewer step errors. 2) Autograded homework with strict JSON/CSV answers: Devstral 2 2512 shines — structured_output 5 (vs Haiku 4) makes it better at exact schema compliance and machine parsing. 3) Large multi-section derivations or textbook chapter summarization: both score long_context 5, but Devstral's 262,144 token window edges out Haiku's 200,000 for extremely long notebooks. 4) Tool-backed numeric verification (calculator/CAS): Haiku's tool_calling 5 (vs 4) and higher faithfulness reduce hallucinated intermediate results in our tests. 5) Cost-sensitive batch evaluation: Devstral is materially cheaper (input $0.40/mtok, output $2/mtok) versus Haiku (input $1/mtok, output $5/mtok), so for high-volume structured tasks Devstral lowers spend while keeping strong format compliance.
Bottom Line
For Math, choose Claude Haiku 4.5 if you prioritize rigorous multi-step reasoning, result fidelity, and reliable tool integration — Haiku leads in strategic_analysis (5 vs 4), faithfulness (5 vs 4), and tool_calling (5 vs 4). Choose Devstral 2 2512 if you need strict, machine-parseable outputs, a larger context window, or lower cost — Devstral leads in structured_output (5 vs 4), has a 262,144 token window, and lower per-mTok pricing.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.
For math tasks, we supplement our benchmark suite with MATH/AIME scores from Epoch AI, an independent research organization.