Glossary · Measuring and comparing
AIME (benchmark)
AIME (benchmark) refers to using problems from the American Invitational Mathematics Examination to evaluate language models. These competition questions test performance on challenging mathematical problems, but a reported result depends on the selected papers and testing procedure, including the tools, attempts, and computing resources available to the model.
AIME (benchmark): the explanation
AIME is originally a mathematics examination for human contestants, described by the [Mathematical Association of America](https://maa.org/maa-invitational-competitions/). In model evaluation, problems from the examination are used as test questions. The system produces an answer that can be checked against the expected solution. You should distinguish the human competition from the computing experiment built around its questions. A model evaluation does not automatically follow the same rules, time limits, or restrictions that apply to people sitting the examination.
For your own tool selection, this benchmark can provide evidence about handling formal problems. It cannot replace testing with your actual work. Preparing a budget, explaining a calculation, and interpreting a spreadsheet also require attention to data, units, and your intended outcome. Treat an AIME result as a starting point and then try representative tasks. A practically useful assistant must understand how a problem is framed, notice missing information, and communicate an answer you can check and use.
Compare results only when you understand how they were obtained. An answer produced on the first attempt differs from one selected after several attempts. Access to a calculation tool and the available reasoning budget also matter. Public questions can raise concerns about training data contamination. Finally, a correct final answer does not establish that every displayed step is valid. If you need explanations, inspect their logic and clarity rather than assuming that answer accuracy guarantees good mathematical teaching.
A concrete example
You are looking for an assistant to help explain mathematics homework. A model has a strong result on an AIME evaluation, so you try it on a problem from your child’s textbook. You ask for an explanation of each step in accessible language and then check the arithmetic and reasoning. The benchmark points to a possible capability, while your own trial shows whether the assistant can support the learning situation you actually face.
Why it matters
An AIME result can inform comparisons between models intended for mathematical problem solving. Its value falls when the question edition, tool access, or number of attempts is unclear. Match the evidence to your actual needs. An assistant that notices a missing unit and explains a calculation carefully may be more useful to you than one with stronger competition performance. Consider explanation quality, reproducibility, and operating cost alongside the published result.
Common misconceptions
- An excellent AIME result does not mean that a model understands every area of mathematics perfectly, because competition problems cover only part of the concepts, representations, and practical situations involved when you work with numbers in everyday tasks.
- Two scores labelled AIME are not automatically comparable, because they may use different examination papers, permitted attempts, external tools, reasoning budgets, or procedures for selecting the final answer from several candidate responses produced by the model.
Related terms: measuring and comparing
- benchmark
- Benchmark is a structured test with defined rules for comparing system performance.
- Epoch Capabilities Index
- Epoch Capabilities Index is a composite measure that combines results from multiple benchmarks to compare AI models.
- SWE-bench
- SWE-bench is a benchmark for resolving software issues drawn from real projects.
- GPQA Diamond
- GPQA Diamond is a selected subset of GPQA containing difficult multiple-choice science questions.
- FrontierMath
- FrontierMath is an advanced mathematics benchmark developed by Epoch AI with mathematical specialists.
- OSWorld
- OSWorld is a family of benchmarks and environments for evaluating agents that perform computer tasks.
- Terminal-Bench
- Terminal-Bench is a collection of tests for measuring how well an AI agent completes tasks through a terminal, an interface controlled with text commands.
- SimpleQA
- SimpleQA is a benchmark for checking how accurately a language model answers short factual questions.
Where this site covers it
The term appears on 396 pages of this site. A few of them:
- Measuring progress toward AGI: benchmarks, limits, saturation, and contamination Guide
- Evaluating an AI agent: test sets, metrics, public benchmarks, and production monitoring Guide
- ARC-AGI: the test, its versions, and published scores Guide
- ARC Prize: the competition, its rules, editions, results, and lessons Guide
- How We Get To AGI: François Chollet’s talk, chapter by chapter Guide
- Choosing a language model: criteria, benchmarks, pricing, context, and licensing Guide
- When will AGI arrive? Published forecasts, their methods, and why they differ Guide
- The limits of deep learning: what models struggle with, according to François Chollet and other researchers Guide
Go further
Frequently asked questions
Was AIME created for AI?
No. It is originally a mathematics examination for human contestants. Evaluators reuse its problems to test models. The resulting AI benchmark is an adaptation, so you need to check its particular procedure rather than assume the competition rules apply.
Does the score validate explanations?
Not necessarily. An evaluation may focus mainly on the final answer. If you need a teaching assistant, separately assess whether the intermediate steps are correct, the language is clear, and the system can explain a reasoning mistake.
Which conditions should you check?
Check the question papers, attempt allowance, available time, and access to calculation tools. Also examine the scoring method and how multiple responses are combined or selected. Those details determine what the final result can reasonably tell you.
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score