Claude Sonnet 4.6 vs Gemini 2.5 Pro for Math
This is a genuinely close race. Neither Claude Sonnet 4.6 nor Gemini 2.5 Pro has a MATH Level 5 score in our payload — that external benchmark data is not available for either model — so we cannot declare a winner on that measure. What we do have is AIME 2025 (Epoch AI), where Claude Sonnet 4.6 scores 85.8% versus Gemini 2.5 Pro's 84.2%, a 1.6-percentage-point gap that is real but narrow. Both models rank 14th out of 52 on our internal math-proxy composite (strategic analysis and structured output). On AIME 2025, Sonnet 4.6 holds a modest lead, but the margin is too slim to call this a decisive win — especially given that Gemini 2.5 Pro costs meaningfully less ($1.25 input / $10 output per million tokens versus $3 input / $15 output for Sonnet 4.6). For pure math accuracy, Sonnet 4.6 has a slight edge on the external benchmark data we have. For cost-sensitive or high-volume math workloads, Gemini 2.5 Pro is the more practical choice without materially sacrificing accuracy.
anthropic
Claude Sonnet 4.6
Benchmark Scores
External Benchmarks
Pricing
Input
$3.00/MTok
Output
$15.00/MTok
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Gemini 2.5 Pro
Benchmark Scores
External Benchmarks
Pricing
Input
$1.25/MTok
Output
$10.00/MTok
modelpicker.net
Task Analysis
Mathematical reasoning at the LLM level demands multi-step symbolic manipulation, the ability to decompose hard problems into tractable sub-problems, and resistance to plausible-but-wrong intermediate steps. Competition math benchmarks like AIME 2025 and MATH Level 5 (Epoch AI) are the most direct measures of this — they test whether a model can actually solve hard problems, not just explain math concepts fluently.
On AIME 2025 (Epoch AI), Claude Sonnet 4.6 scores 85.8% and Gemini 2.5 Pro scores 84.2% — both rank in the top half of the 23 models measured, with Sonnet 4.6 at rank 10 and Gemini 2.5 Pro at rank 11. Neither model has a MATH Level 5 score available in our data. The AIME 2025 gap is 1.6 percentage points, which is meaningful on a hard olympiad-style test but not commanding.
Our internal proxy benchmarks add supporting context. Both models score 4/5 on structured output in our testing — important for math workflows that emit LaTeX, JSON-wrapped answers, or step-by-step structured proofs. Sonnet 4.6 scores 5/5 on strategic analysis (nuanced tradeoff reasoning with real numbers) versus Gemini 2.5 Pro's 4/5, suggesting stronger quantitative reasoning when problems require weighing trade-offs. Both score 5/5 on tool calling, which matters for agentic math setups where a model calls a calculator, symbolic solver, or code interpreter to verify results. Sonnet 4.6 also scores 5/5 on agentic planning versus Gemini 2.5 Pro's 4/5 — relevant when solving multi-step problems that require self-checking and backtracking.
Practical Examples
Competition-style problem solving (AIME/Olympiad): On AIME 2025 (Epoch AI), Sonnet 4.6 scores 85.8% vs Gemini 2.5 Pro's 84.2%. If you are running a tutoring app or research tool that needs to solve or verify olympiad-level problems, Sonnet 4.6's marginal lead is meaningful at the tail of the difficulty distribution.
Quantitative reasoning in applied contexts: Sonnet 4.6 scores 5/5 on strategic analysis in our testing versus Gemini 2.5 Pro's 4/5. This manifests in scenarios like financial modeling, engineering trade-off calculations, or optimization problems where the model must reason with actual numbers rather than symbolic abstractions. Sonnet 4.6's edge here is more reliable.
Structured math output (LaTeX, JSON schemas): Gemini 2.5 Pro scores 5/5 on structured output in our testing versus Sonnet 4.6's 4/5. For pipelines that require the model to emit math answers in a strict schema — say, a grading system parsing JSON-wrapped solutions — Gemini 2.5 Pro is the safer bet.
High-volume math tutoring or batch grading: Gemini 2.5 Pro at $1.25 input / $10 output per million tokens versus Sonnet 4.6 at $3 / $15 is a significant cost difference at scale. At equivalent math accuracy (the AIME gap is 1.6 points), running 1 million math queries through Gemini 2.5 Pro saves substantially. The performance difference does not justify the price premium for most production math applications.
Agentic math workflows (code interpreter + solver loops): Both score 5/5 on tool calling in our tests. Sonnet 4.6 scores 5/5 on agentic planning versus Gemini 2.5 Pro's 4/5, giving it a slight advantage in multi-step solver chains where the model must decompose a problem, call tools, check intermediate results, and recover from errors.
Bottom Line
For Math, choose Claude Sonnet 4.6 if you need the highest AIME-level accuracy available between these two models (85.8% vs 84.2% on Epoch AI's AIME 2025 benchmark), are running agentic math pipelines where planning and error recovery matter (5/5 vs 4/5 on agentic planning in our tests), or need strong quantitative reasoning in applied contexts (5/5 vs 4/5 on strategic analysis). Choose Gemini 2.5 Pro if you are processing math at scale and cost is a real constraint — it is roughly 58% cheaper on output tokens ($10 vs $15 per million) for a 1.6-point accuracy gap on AIME 2025, and it outperforms Sonnet 4.6 on structured output (5/5 vs 4/5 in our tests), making it better suited to pipelines that parse model-generated math answers programmatically.
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.