FunctionalMATH

A functional variant of the MATH benchmark that tests language models' ability to generalize reasoning patterns across different problem instances, revealing the reasoning gap between static and functional performance.

Gemini 1.5 Pro from Google currently leads the FunctionalMATH leaderboard with a score of 0.646 across 2 evaluated AI models.

Paper

GoogleGemini 1.5 Pro leads with 64.6%, followed by GoogleGemini 1.5 Flash at 53.6%.

Progress Over Time

Interactive timeline showing model performance evolution on FunctionalMATH

State-of-the-art frontier
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FunctionalMATH Leaderboard

2 models
ContextCostLicense
12.1M$2.50 / $10.00
21.0M$0.15 / $0.60
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FAQ

Common questions about FunctionalMATH.

What is the FunctionalMATH benchmark?

A functional variant of the MATH benchmark that tests language models' ability to generalize reasoning patterns across different problem instances, revealing the reasoning gap between static and functional performance.

What is the FunctionalMATH leaderboard?

The FunctionalMATH leaderboard ranks 2 AI models based on their performance on this benchmark. Currently, Gemini 1.5 Pro by Google leads with a score of 0.646. The average score across all models is 0.591.

What is the highest FunctionalMATH score?

The highest FunctionalMATH score is 0.646, achieved by Gemini 1.5 Pro from Google.

How many models are evaluated on FunctionalMATH?

2 models have been evaluated on the FunctionalMATH benchmark, with 0 verified results and 2 self-reported results.

Where can I find the FunctionalMATH paper?

The FunctionalMATH paper is available at https://arxiv.org/abs/2402.19450. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does FunctionalMATH cover?

FunctionalMATH is categorized under math and reasoning. The benchmark evaluates text models.

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