Best LLM for math in 2026: how AI models rank
- Jul 17, 2024
- 4 min read
Updated: 3 days ago
Ever wonder which AI model is best at solving frontier-level mathematics?
Mathematics benchmarks test how well models can reason through complex problems rather than simply recall formulas. They are a way to measure whether AI can perform multi-step logical reasoning.
Earlier math benchmarks focused on school or competition math. Datasets like the AIME 25 or the MATH 500 benchmark mostly test high-school or Olympiad-style problems.
FrontierMath pushes much further. It is designed to test reasoning closer to research mathematics, where problems require deep understanding and long chains of deduction.
Because of that, FrontierMath is one of the hardest AI benchmarks in existence today.
Why should you care?
FrontierMath is not just “math trivia”. It is one of the best ways to evaluate the best AI for advanced reasoning.
For FrontierMath, it is one of the best proxies we have for:
multi-step mathematical reasoning
connecting concepts across different fields of math
solving problems that resemble real research work
So if your work touches scientific research, engineering, data science, or quantitative finance, these scores are a useful signal when choosing an AI model.
Not sure which AI model to pick?
Read our full guide to the best LLMs
Which LLM is best at math in 2026?
FrontierMath shows something unusual: there is a clear leader, and then a very steep drop.
The top tier breaks away
Open AI's GPT-5.4 leads with 38%
Claude Opus 4.6 follows at 23%
Gemini 3 Pro scores 19%
FrontierMath is so difficult that even the best models struggle. A score under 40% is still considered extremely strong.
The gap between GPT-5.4 and the rest of the field suggests that improvements in reasoning architecture and tool use are beginning to matter more than raw scale.
Even the second-place model solves barely a quarter of the benchmark.
The bottom disappears quickly
Grok 4 and DeepSeek v3.2 both score 2% and Llama 4 Maverick scores 1%.
These scores highlight how extreme the difficulty of FrontierMath is. A small drop in reasoning capability translates into a massive drop in benchmark performance.
For many practical tasks these models still work well. But on deep mathematical reasoning they struggle to sustain long chains of logic.
What is the FrontierMath benchmark?
FrontierMath is a benchmark created by Epoch AI with contributions from more than 60 professional mathematicians, including Fields Medalists.
It was released in 2024 to evaluate whether AI systems can reason at the level of advanced mathematics rather than memorized knowledge.
The benchmark contains about 350 extremely difficult math problems that require open-ended reasoning rather than multiple-choice answers.
The test includes about 350 questions across:
number theory
real analysis
algebraic geometry and topology
Problems span many other fields as well, including combinatorics, category theory, and computational mathematics.
The benchmark includes multiple tiers of difficulty, ranging from advanced undergraduate math to PhD-level and research-level problems.
Even expert mathematicians typically achieve around 90% accuracy with time.
By comparison, modern AI models often score only 5–10% on average.
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.
Dataset: AIME problems and solutions
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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