Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers... The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on...
Published byEdited with GPT-5.5Images generated with GPT Image 2
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers...
The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on HMMT 2025, while LLM Stats shows GPT 5.2 Pro and GPT 5.2 in rank 1 AIME 2025 entries.[2][4]
Use leaderboards to make a shortlist, then test models on fresh problems from your actual use case before trusting any one ranking.
Best AI for Math: Gemini Leads AIME, but Benchmarks Need ContextAI-generated editorial illustration of AI math benchmarking and competition-style problem solving.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context. Article summary: For public AIME style competition math, Vals AI’s clearest winner is Gemini 3.1 Pro Preview at 98.13% accuracy, but that does not make it the universal best because AIME is public and other leaderboards differ.[1][4]. Topic tags: ai, math, ai benchmarks, gemini, openai. Reference image context from search candidates: Reference image 1: visual subject "Gemini 3.1 Pro leads every unsaturated math benchmark: GPQA Diamond (94.1%), HLE (44.7%), and ARC-AGI-2 (77.1%) · AIME 2025 is dead as a ranking" source context "Best AI Models for Math Reasoning - April 2026 | Awesome Agents" Reference image 2: visual subject "Gemini 3.1 Pro leads every unsaturated math benchmark: GPQA Diamond (94.1%), HLE (44.7%), and ARC-AGI-2 (77.1%) · AIME 2025 is de
openai.com
The most accurate answer depends on what kind of math you mean. For public AIME-style competition math, the clearest single benchmark result in the provided sources is Gemini 3.1 Pro Preview: Vals AI lists it as the top AIME model with 98.13% accuracy. For broader math help—homework, tutoring, contest prep, quantitative reasoning, or product workflows—there is no single uncontested winner.
The clearest benchmark pick: Gemini on AIME
AIME and HMMT are high school math olympiad competitions that are now used to benchmark AI systems. On Vals AI’s AIME benchmark, Gemini 3.1 Pro Preview is listed as the top-performing model at 98.13% accuracy.
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What is the short answer to "Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context"?
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers...
What are the key points to validate first?
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers... The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on HMMT 2025, while LLM Stats shows GPT 5.2 Pro and GPT 5.2 in rank 1 AIME 2025 entries.[2][4]
What should I do next in practice?
Use leaderboards to make a shortlist, then test models on fresh problems from your actual use case before trusting any one ranking.
Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers... The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on...
Published byEdited with GPT-5.5Images generated with GPT Image 2
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers...
The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on HMMT 2025, while LLM Stats shows GPT 5.2 Pro and GPT 5.2 in rank 1 AIME 2025 entries.[2][4]
Use leaderboards to make a shortlist, then test models on fresh problems from your actual use case before trusting any one ranking.
Best AI for Math: Gemini Leads AIME, but Benchmarks Need ContextAI-generated editorial illustration of AI math benchmarking and competition-style problem solving.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context. Article summary: For public AIME style competition math, Vals AI’s clearest winner is Gemini 3.1 Pro Preview at 98.13% accuracy, but that does not make it the universal best because AIME is public and other leaderboards differ.[1][4]. Topic tags: ai, math, ai benchmarks, gemini, openai. Reference image context from search candidates: Reference image 1: visual subject "Gemini 3.1 Pro leads every unsaturated math benchmark: GPQA Diamond (94.1%), HLE (44.7%), and ARC-AGI-2 (77.1%) · AIME 2025 is dead as a ranking" source context "Best AI Models for Math Reasoning - April 2026 | Awesome Agents" Reference image 2: visual subject "Gemini 3.1 Pro leads every unsaturated math benchmark: GPQA Diamond (94.1%), HLE (44.7%), and ARC-AGI-2 (77.1%) · AIME 2025 is de
openai.com
The most accurate answer depends on what kind of math you mean. For public AIME-style competition math, the clearest single benchmark result in the provided sources is Gemini 3.1 Pro Preview: Vals AI lists it as the top AIME model with 98.13% accuracy. For broader math help—homework, tutoring, contest prep, quantitative reasoning, or product workflows—there is no single uncontested winner.
The clearest benchmark pick: Gemini on AIME
AIME and HMMT are high school math olympiad competitions that are now used to benchmark AI systems. On Vals AI’s AIME benchmark, Gemini 3.1 Pro Preview is listed as the top-performing model at 98.13% accuracy.
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "Best AI for Math: Gemini Leads AIME, but Benchmarks Need Context"?
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers...
What are the key points to validate first?
Vals AI lists Gemini 3.1 Pro Preview as the top AIME model at 98.13%, making it the clearest benchmark pick for competition style math; the caveat is that AIME questions are public, so high scores do not prove univers... The top tier is crowded: BenchLM reports frontier models above 95% on AIME 2025 and above 90% on HMMT 2025, while LLM Stats shows GPT 5.2 Pro and GPT 5.2 in rank 1 AIME 2025 entries.[2][4]
What should I do next in practice?
Use leaderboards to make a shortlist, then test models on fresh problems from your actual use case before trusting any one ranking.
That makes Gemini 3.1 Pro Preview the strongest source-backed answer if the question is specifically: which model leads this AIME leaderboard? It does not automatically answer which AI is best for every type of math problem.
Why one leaderboard does not settle the question
Different benchmark sites can point to different leaders. Vals AI lists Gemini 3.1 Pro Preview first on its AIME benchmark, while LLM Stats shows GPT-5.2 Pro and GPT-5.2 in rank-1 entries on its AIME 2025 leaderboard.
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. 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.
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. 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
If you need...
Best way to decide
The strongest single AIME result in these sources
Start with Gemini 3.1 Pro Preview, because Vals AI lists it first on AIME at 98.13% accuracy.
Competition-math practice
Compare AIME and HMMT-style results, since BenchLM reports top models above 95% on AIME 2025 and above 90% on HMMT 2025.
A broader quantitative-reasoning ranking
Look at composite math leaderboards. LLMBase says its math ranking uses the Artificial Analysis math index, including AIME and MATH 500.
A different advanced-math evaluation format
Consider FrontierMath-style benchmarks; Epoch AI’s FrontierMath Tier 4 requires each model to submit a Python answer() function for each question.
Real-world reliability
Build a small private test set, especially because public AIME questions may have appeared in training data.
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:
Give each model the same fresh problems.
Require both a final answer and a clear derivation.
Ask for verification by substitution, an alternate method, or a numerical check when appropriate.
Track flawed reasoning, not just wrong final answers.
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. 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.
That makes Gemini 3.1 Pro Preview the strongest source-backed answer if the question is specifically: which model leads this AIME leaderboard? It does not automatically answer which AI is best for every type of math problem.
Why one leaderboard does not settle the question
Different benchmark sites can point to different leaders. Vals AI lists Gemini 3.1 Pro Preview first on its AIME benchmark, while LLM Stats shows GPT-5.2 Pro and GPT-5.2 in rank-1 entries on its AIME 2025 leaderboard.
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. 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.
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. 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
If you need...
Best way to decide
The strongest single AIME result in these sources
Start with Gemini 3.1 Pro Preview, because Vals AI lists it first on AIME at 98.13% accuracy.
Competition-math practice
Compare AIME and HMMT-style results, since BenchLM reports top models above 95% on AIME 2025 and above 90% on HMMT 2025.
A broader quantitative-reasoning ranking
Look at composite math leaderboards. LLMBase says its math ranking uses the Artificial Analysis math index, including AIME and MATH 500.
A different advanced-math evaluation format
Consider FrontierMath-style benchmarks; Epoch AI’s FrontierMath Tier 4 requires each model to submit a Python answer() function for each question.
Real-world reliability
Build a small private test set, especially because public AIME questions may have appeared in training data.
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:
Give each model the same fresh problems.
Require both a final answer and a clear derivation.
Ask for verification by substitution, an alternate method, or a numerical check when appropriate.
Track flawed reasoning, not just wrong final answers.
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. 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.
