AIME 2025
Progress Over Time
Interactive timeline showing model performance evolution on AIME 2025
AIME 2025 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Google | — | — | — | ||
| 1 |  Moonshot AI | 1.0T | — | — | ||
| 1 |  OpenAI | — | 400K | $1.75 / $14.00 | ||
| 1 |  xAI | — | — | — | ||
| 1 | OpenAI | — | — | — | ||
| 6 |  Anthropic | — | 1.0M | $5.00 / $25.00 | ||
| 7 | Google | — | 1.0M | $0.50 / $3.00 | ||
| 8 | OpenAI | — | — | — | ||
| 8 |  Meituan | 560B | — | — | ||
| 10 | 32B | 262K | $0.05 / $0.20 | |||
| 11 | OpenAI | 21B | — | — | ||
| 12 | OpenAI | — | 400K | $1.25 / $10.00 | ||
| 13 |  ByteDance | — | 256K | $0.50 / $3.00 | ||
| 14 |  StepFun | 196B | 66K | $0.10 / $0.40 | ||
| 15 |  Microsoft | 1.0T | — | — | ||
| 16 | OpenAI | — | — | — | ||
| 16 |  Sarvam AI | 105B | — | — | ||
| 16 | Sarvam AI | 30B | — | — | ||
| 19 | Moonshot AI | 1.0T | — | — | ||
| 20 |  DeepSeek | 685B | — | — | ||
| 21 |  Zhipu AI | 358B | 203K | $0.40 / $1.75 | ||
| 22 | OpenAI | — | — | — | ||
| 22 | OpenAI | — | — | — | ||
| 24 | ByteDance | — | 256K | $0.25 / $2.00 | ||
| 25 |  Xiaomi | 309B | — | — | ||
| 26 | OpenAI | — | 400K | $1.25 / $10.00 | ||
| 26 | OpenAI | — | 400K | $1.25 / $10.00 | ||
| 26 | OpenAI | — | — | — | ||
| 29 | Zhipu AI | 357B | 203K | $0.50 / $2.00 | ||
| 30 | xAI | — | 128K | $3.00 / $15.00 | ||
| 31 | DeepSeek | 685B | — | — | ||
| 31 | DeepSeek | 685B | 164K | $0.26 / $0.38 | ||
| 33 | ByteDance | — | — | — | ||
| 34 |  LG AI Research | 33B | — | — | ||
| 35 | LG AI Research | 236B | — | — | ||
| 36 | OpenAI | — | — | — | ||
| 37 | OpenAI | 117B | — | — | ||
| 38 |  Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 38 |  Amazon | — | — | — | ||
| 40 | Amazon | — | — | — | ||
| 41 | xAI | — | — | — | ||
| 42 | xAI | — | — | — | ||
| 43 | Zhipu AI | 30B | — | — | ||
| 44 |  Inception | — | 128K | $0.25 / $0.75 | ||
| 44 | OpenAI | — | 400K | $0.25 / $2.00 | ||
| 46 | Amazon | — | 1.0M | $0.30 / $2.50 | ||
| 47 | xAI | — | — | — | ||
| 48 | Meituan | 560B | — | — | ||
| 49 | 120B | 262K | $0.09 / $0.40 | |||
| 50 |  Cohere | 218B | — | — |
Sub-benchmarks
What is AIME 2025?
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
AIME 2025 is a text benchmark evaluating models on math and reasoning tasks. LLM Stats tracks 122 models on this benchmark, scored on a 0–1 scale. The current average is 0.8, with the leader at 1.0.
Compare leaders on the best AI for math and best AI for reasoning leaderboards.
Current leaders
Gemini 3 Pro from Google currently leads the AIME 2025 leaderboard with a score of 1.000 across 122 evaluated AI models.
Source paper
- Title
- Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
- Authors
- Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, and 1 others
- Published
- arXiv
- 2503.21380
Abstract
The rapid advancement of large reasoning models has saturated existing math benchmarks, underscoring the urgent need for more challenging evaluation frameworks. To address this, we introduce OlymMATH, a rigorously curated, Olympiad-level math benchmark comprising 350 problems, each with parallel English and Chinese versions. OlymMATH is the first benchmark to unify dual evaluation paradigms within a single suite: (1) natural language evaluation through OlymMATH-EASY and OlymMATH-HARD, comprising 200 computational problems with numerical answers for objective rule-based assessment, and (2) formal verification through OlymMATH-LEAN, offering 150 problems formalized in Lean 4 for rigorous process-level evaluation. All problems are manually sourced from printed publications to minimize data contamination, verified by experts, and span four core domains. Extensive experiments reveal the benchmark's significant challenge, and our analysis also uncovers consistent performance gaps between languages and identifies cases where models employ heuristic "guessing" rather than rigorous reasoning. To further support community research, we release 582k+ reasoning trajectories, a visualization tool, and expert solutions at https://github.com/RUCAIBox/OlymMATH.
FAQ
Common questions about the AIME 2025 benchmark and leaderboard.