AIME 26
AIME 26 - American Invitational Mathematics Examination 2026 (I & II).
Category
STEM & Reasoning
Score type
percent
Scale
100%
higher is better
Models tested
33
What is AIME 26?
AIME 2025 uses the problems of the American Invitational Mathematics Examination 2025 (I & II) as a benchmark. These are competition math problems that only have their exact numeric answer graded. Because the problems are new and public, a model can barely rely on training-data memorisation — the year variants show whether a model really computes or just remembers.
What does it test?
AIME 26 measures hard reasoning in science, technology, engineering and math. In detail, it checks:
- competition-level math problem solving
- exact numeric answers (graded answers must be exact)
- multi-step reasoning without tools (unless explicitly 'with tools')
- fresh problems that defeat memorised answer lists
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the AIME 26 benchmark?
AIME 2025 uses the problems of the American Invitational Mathematics Examination 2025 (I & II) as a benchmark. These are competition math problems that only have their exact numeric answer graded. Because the problems are new and public, a model can barely rely on training-data memorisation — the year variants show whether a model really computes or just remembers.
What does AIME 26 test?
AIME 26 mainly measures hard reasoning in science, technology, engineering and math. Concretely, it checks: competition-level math problem solving; exact numeric answers (graded answers must be exact); multi-step reasoning without tools (unless explicitly 'with tools').
How is the AIME 26 score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get AIME 26 data and scores?
The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/aime-26/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 33 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
GLM-5.2
Zhipu AI
|
99.2%
|
17.06.2026 | Report | Verified |
| 2 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
98.8%
|
21.10.2026 | Report | Unverified |
| 3 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
98.3%
|
— | Report | Unverified |
| 4 |
Kimi K2.6
Moonshot AI
|
96.4%
|
23.08.2026 | Report | Verified |
| 5 |
Kolibri-1
Aleph Alpha
|
96%
|
03.10.2026 | Report | Verified |
| 6 |
Bonsai 2 27B — GGUF
Prism ML
|
95.83%
|
17.09.2026 | Report | Verified |
| 7 |
Kimi K2.5
Moonshot AI
|
95.8%
|
23.08.2026 | Report | Verified |
| 8 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
95.4%
|
— | Report | Unverified |
| 9 |
Nanbeige4.3-33B
Nanbeige
|
95.2%
|
24.10.2026 | Report | Verified |
| 10 |
Ripple-65B Preview
Ricursive Intelligence
|
95%
|
— | Report | Verified |
| 11 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
94.7%
|
01.08.2026 | Meta Superintelligence Lab (official model card); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 92.9 | Verified |
| 12 |
DeepSeek-V4-Pro
DeepSeek
|
94.6%
|
17.06.2026 | Report | Unverified |
| 13 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
94.1%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 14 |
Raon-1 mini
KRAFTON AI
|
93.3%
|
21.10.2026 | Report | Verified |
| 15 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
92.7%
|
16.04.2026 | Qwen Team (official model card); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 92.9 | Verified |
| 16 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
92.6%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 17 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
91%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 18 |
Spark-X2.5-4B
SparkLLM (XHToken)
|
90.7%
|
— | Report | Verified |
| 19 |
Xing4.0-29B-A4B
China Telecom AI
|
90%
|
16.09.2026 | Report | Verified |
| 20 |
Gemma4-31B-it
Google
|
89.2%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 21 |
Gemma4-26B-A4B
Google
|
88.3%
|
16.04.2026 | Qwen Team (official model card - comparison); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 87.1 | Unverified |
| 22 |
MiniCPM5-2B
OpenBMB
|
86.5%
|
— | Report | Verified |
| 23 |
granite-4.2-3B
IBM
|
83.5%
|
— | Report | Verified |
| 24 |
Qwen3.5-4B
Qwen
|
83%
|
— | Report | Verified |
| 25 |
Gemma4-12B
Google
|
82.1%
|
02.07.2026 | Report | Verified |