AIME

What is AIME?

AIME is the American Invitational Mathematics Examination, a difficult 15-question math contest that is used as a benchmark for evaluating reasoning models. In AI, it is a common way to measure how well a model handles contest-style math and multi-step problem solving. (maa.org)

Understanding AIME

In the original contest, AIME sits above AMC 10 and AMC 12 and is reserved for students who score highly on those exams. It is a three-hour exam with integer answers from 0 to 999, which makes it useful for checking whether a model can arrive at exact solutions rather than just approximate ones. (maa.org)

In machine learning, AIME has become a compact but demanding math eval. Because the problems reward careful reasoning, algebra, number theory, and combinatorics, model teams use AIME to compare systems that can think through multiple steps instead of guessing from pattern match. PromptLayer users often treat it as one piece of a broader reasoning evaluation suite, alongside other hard benchmarks. (openai.com)

Key aspects of AIME include:

1. Exact-answer format: Each problem has a single integer answer, which makes scoring straightforward.
2. High reasoning load: Problems are designed to be significantly harder than AMC-level questions.
3. Selective entry: Only top AMC performers are invited, so the exam reflects a strong competition pool.
4. Model benchmark value: It gives LLM builders a focused test for math reasoning quality.
5. Good for regression testing: Teams can reuse it to track model changes over time.

Advantages of AIME

1. Clear scoring: Integer answers make evaluation simple and objective.
2. Strong signal: It separates surface competence from deeper reasoning ability.
3. Widely recognized: Many AI teams already understand what a good AIME score means.
4. Compact benchmark: A small set of questions can still reveal useful differences between models.
5. Useful for iteration: It is practical for repeated model comparisons during development.

Challenges in AIME

1. Narrow domain: It mostly measures math reasoning, not broader assistant quality.
2. Memorization risk: Publicly available problems can be contaminated in training data.
3. Answer-only scoring: A model can get the right answer without showing robust reasoning.
4. Limited real-world coverage: Contest math is not the same as production user tasks.
5. Hard to diagnose failures: A wrong final answer can hide where the reasoning went off track.

Example of AIME in Action

Scenario: A team is comparing two reasoning models before shipping a new release.

They run both models on a small AIME set and score exact integer matches. Model A does better on easy problems, while Model B performs better on the hardest multi-step questions, so the team keeps both scores in their eval dashboard instead of trusting one aggregate number.

With PromptLayer, that team can store the prompts, track outputs, attach scoring rules, and compare runs over time. That makes AIME-style testing easier to repeat when the model, prompt, or decoding settings change.

How PromptLayer helps with AIME

PromptLayer helps teams organize AIME-style evals, log model outputs, and compare reasoning performance across prompt versions and model releases. That makes it easier to turn a hard benchmark into a repeatable workflow.

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