Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context

The broader pattern is that several frontier models are now clustered near the top on competition-style math. BenchLM reports that top models are above 95% on AIME 2025 and above 90% on HMMT 2025.^[2] When performance is that close, the practical choice may depend less on a small leaderboard gap and more on explanation quality, consistency, latency, price, and whether the model handles your exact problem format well.

The big caveat: public benchmarks can be contaminated

AIME is a useful signal, but it is not a perfect test of fresh reasoning. Vals AI notes that AIME questions and answers are publicly available, creating a risk that models may have encountered them during pretraining.^[1]

Vals AI also reports that models tend to perform better on older 2024 questions than on the newer 2025 set, which raises questions about data contamination and true generalization.^[1] In practical terms, a very high AIME score shows benchmark strength, but it does not guarantee the same reliability on new, private, or unusual problems.

What to use for different math needs

A simple private test is better than a leaderboard-only choice

For schoolwork, tutoring, contest prep, or a math-heavy product workflow, use public leaderboards to pick a shortlist. Then run your own small evaluation:

1. Give each model the same fresh problems.
2. Require both a final answer and a clear derivation.
3. Ask for verification by substitution, an alternate method, or a numerical check when appropriate.
4. Track flawed reasoning, not just wrong final answers.
5. Prefer the model that is accurate, understandable, and consistent on your actual problem type.

This matters because math use cases differ. A model that is excellent at short-answer contest problems may not be the best fit for step-by-step tutoring, symbolic manipulation, long proofs, or code-based quantitative work.

Bottom line

For AIME-style benchmark math, Gemini 3.1 Pro Preview is the leading model in Vals AI’s listing, with 98.13% accuracy.^[1] For the broader question of the best AI for math, the evidence does not support one universal winner: frontier models are tightly clustered on competition benchmarks, rankings vary by leaderboard, and public AIME data creates a real reason to test on fresh problems before trusting any result too much.^[1][2][4]

Find the best AI models for mathematics and quantitative reasoning. Ranked by Artificial Analysis math index including AIME, MATH 500 & more.