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Best LLM for math in 2026: how AI models rank

Updated: 7 hours ago

Ever wondered which AI model is best at math?


Math benchmarks are tests that score AI models on math problems. Think of them as a leaderboard for mathematical reasoning.


Since AI models have improved so fast, the benchmarks have changed too. When the MATH benchmark was first introduced, the best models only scored around 7%. Today, frontier models are close to the ceiling, which makes the test less useful for comparison.


That’s why we also report AIME 2025, one of the most relevant benchmarks right now for measuring true math reasoning.


Best LLM for math, comparing frontier models
Benchmark data last checked: January 2026

Why should you care?

Math benchmarks are not just about math.


They are one of the best proxies we have for:

  • logical reasoning

  • step-by-step problem solving

  • consistency under pressure


So if your work involves finance, engineering, coding, or analytical tasks, these scores are a useful signal.


Not sure which AI model to pick?

Read our full guide to the best LLMs


Which LLM is best at math in 2026?

The AIME 2025 benchmark shows something new: math performance at the top is now very clustered.


The top tier is basically a dead heat

OpenAI’s GPT-5.2 leads this snapshot with a perfect 100%.


But the gap is small:

  • Gemini 3 Pro scores 95%

  • Claude Opus 4.5 sits at 93%

  • Qwen 3 and Grok 4.1 land around 92%


At this level, the difference between models is no longer “can it do math” but “how reliably can it solve the hardest problems.”


The middle tier is still strong, but less consistent

Models like DeepSeek V3.2, Ernie 5.0, and Mistral 3 score in the mid-to-high 80s.


That is still impressive performance, but it suggests more mistakes on competition-level reasoning tasks.


For most business use cases, these models are “good enough.”

For research-grade math, the top tier matters more.


Llama 4 is the clear outlier

Meta’s Llama 4 scores far below the frontier group in this benchmark.


This is a reminder that not all large models are equally strong at structured reasoning, even if they perform well in writing or chat tasks.


What is the AIME 2025 benchmark?

The AIME 2025 benchmark stands for the American Invitational Math Exam.


It was introduced by the Mathematical Association of America as an exam given to American students as the second test in a sequence to prove their ability to participate in the International Math Olympiad (IMO) or the European Girls’ Mathematical Olympiad (EGMO).


This benchmark consists of 15 problems with an integer solution between 0 and 999, while American students are given a 3 hour time limit, the LLM’s are not given a time limit as this benchmark is purely used to test raw mathematical skills.



Compared to the old MATH 500 the AIME is not an easy test.


Even strong high school students often solve only 5 out of 15 problems correctly. That is why AIME is used as a stepping stone before selecting candidates for the International Math Olympiad.


Top AI models, on the other hand, now score close to perfect on this benchmark.


That gap is striking: a test designed to challenge the best young mathematicians is becoming solvable by frontier language models.


This is exactly why AIME 2025 matters. It is one of the few remaining math benchmarks that can still separate the very best models from the rest.


What is the MATH 500 benchmark?

The MATH LLM benchmark is, for once, not an acronym—it simply stands for math.


It was introduced by Hendrycks et al. (2021) as a way to evaluate how well LLMs perform on challenging math problems.


This benchmark consists of 12,500 problems sourced from high school math competitions and covers topics like:

  • Algebra

  • Geometry

  • Probability

  • Calculus


It’s a tough test:

  • A PhD student without a strong math background scored 40%

  • A three-time IMO gold medalist scored 90% (IMO = International Mathematical Olympiad)


When the dataset was first introduced, even the best LLMs only managed 6.9%. Today, frontier models like Claude 3.7 Sonnet have come close to human expert performance, reaching nearly 97%.


As LLM’s have evolved they have scored higher scores on the MATH 500 until eventually they consistently scored 90%. Therefore making the old MATH 500 obsolete, leaving LLM’s without a way to accurately test their growth in math until the AIME 2025 benchmark was introduced.


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