Claude Haiku 4.5 vs DeepSeek V3.1 for Math

Winner: Claude Haiku 4.5. In our testing for Math (the strategic_analysis and structured_output tasks), Claude Haiku 4.5 wins based on higher strategic_analysis (5 vs 4) and tool_calling (5 vs 3), which are central to multi-step mathematical problem solving. DeepSeek V3.1 outperforms on structured_output (5 vs 4) and creative_problem_solving (5 vs 4), making it better for precisely formatted answers or novel problem generation, but those strengths do not outweigh Haiku’s edge on core mathematical reasoning and calculator/tool orchestration in our benchmarks. Note: an external MATH Level 5 (Epoch AI) score is listed for this task but no score is provided for either model in the payload, so this verdict is based on our internal test scores.

anthropic

Claude Haiku 4.5

Overall
4.33/5Strong

Benchmark Scores

Faithfulness
5/5
Long Context
5/5
Multilingual
5/5
Tool Calling
5/5
Classification
4/5
Agentic Planning
5/5
Structured Output
4/5
Safety Calibration
2/5
Strategic Analysis
5/5
Persona Consistency
5/5
Constrained Rewriting
3/5
Creative Problem Solving
4/5

External Benchmarks

SWE-bench Verified
N/A
MATH Level 5
N/A
AIME 2025
N/A

Pricing

Input

$1.00/MTok

Output

$5.00/MTok

Context Window200K

modelpicker.net

deepseek

DeepSeek V3.1

Overall
3.92/5Strong

Benchmark Scores

Faithfulness
5/5
Long Context
5/5
Multilingual
4/5
Tool Calling
3/5
Classification
3/5
Agentic Planning
4/5
Structured Output
5/5
Safety Calibration
1/5
Strategic Analysis
4/5
Persona Consistency
5/5
Constrained Rewriting
3/5
Creative Problem Solving
5/5

External Benchmarks

SWE-bench Verified
N/A
MATH Level 5
N/A
AIME 2025
N/A

Pricing

Input

$0.150/MTok

Output

$0.750/MTok

Context Window33K

modelpicker.net

Task Analysis

What Math demands: rigorous stepwise reasoning, accurate tradeoff reasoning with real numbers, faithfulness (no hallucinated steps), consistent long-context handling for multi-step proofs, and often exact structured outputs (LaTeX/JSON) or external-tool use for high-precision computation. Because an external MATH Level 5 score exists in the payload but is empty for both models, we rely on our internal tests. The two task tests here are strategic_analysis and structured_output. Claude Haiku 4.5 scores 5 on strategic_analysis vs DeepSeek V3.1’s 4, and 5 vs 3 on tool_calling (Haiku advantage). DeepSeek V3.1 scores 5 on structured_output vs Haiku’s 4 (DeepSeek advantage). Faithfulness and long_context are tied (both 5), so neither model loses ground there. In short: Haiku shows the better ability to plan and invoke tools for multi-step numeric reasoning; DeepSeek is stronger at strict output formatting.

Practical Examples

  1. Competitive math solutions (multi-step proofs, strategy-heavy problems): Claude Haiku 4.5 — strategic_analysis 5 vs 4 — provides clearer decomposition and step selection. 2) Problems needing external calculators or sequenced tool use (high-precision arithmetic, multi-query math engine): Claude Haiku 4.5 — tool_calling 5 vs 3 — is preferable. 3) Graded answers, automated pipelines, or judge-friendly JSON/LaTeX outputs where schema compliance matters: DeepSeek V3.1 — structured_output 5 vs 4 — will produce more exact formatted outputs. 4) Generating novel, creative problem sets or non-obvious problem variants: DeepSeek V3.1 — creative_problem_solving 5 vs 4 — is stronger. 5) Long multi-part proofs or exam-length contexts: both tie on long_context (5), so either model handles extended context equally in our tests. Also factor cost: Claude Haiku 4.5 is pricier (input 1 / output 5 per mtok) vs DeepSeek V3.1 (input 0.15 / output 0.75 per mtok); our priceRatio indicates Haiku runs ~6.67x more expensive by the provided cost metrics.

Bottom Line

For Math, choose Claude Haiku 4.5 if you need top-tier strategic reasoning and reliable tool/calc orchestration (strategic_analysis 5, tool_calling 5). Choose DeepSeek V3.1 if you prioritize exact, schema-compliant outputs or creative problem generation (structured_output 5, creative_problem_solving 5) and want lower per-token cost.

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.

Frequently Asked Questions