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 |
|---|---|---|---|---|---|
| 26 |
Spark-X2.5-1.7B
SparkLLM (XHToken)
|
69.4%
|
— | Report | Verified |
| 27 |
Nemotron-3-Nano-4B
NVIDIA
|
62.1%
|
— | Report | Verified |
| 28 |
LFM2.5-8B-A1B
LiquidAI
|
56.7%
|
— | Report | Verified |
| 29 |
LFM2.5-2.6B
LiquidAI
|
45.2%
|
— | Report | Verified |
| 30 |
Gemma4-E4B
Google
|
42.5%
|
02.07.2026 | Report | Verified |
| 31 |
Gemma4-E2B
Google
|
37.5%
|
02.07.2026 | Report | Verified |
| 32 |
Qwen3.5-2B
Qwen Team (Alibaba Cloud)
|
30.8%
|
— | Report | Verified |
| 33 |
Gemma3-27B
Google
|
20.8%
|
02.07.2026 | Report | Unverified |