AI Benchmarks Database

AI benchmarks are standardized tests that measure how well AI models perform on specific tasks. They provide objective comparisons across models by testing abilities like reasoning, coding, math, and knowledge recall.

Higher scores indicate better performance on that specific test. However, benchmarks don't capture everything - a model with lower MMLU might still be better for your use case. Consider benchmarks as one factor alongside your own testing.

MMLU (Massive Multitask Language Understanding) tests AI knowledge across 57 subjects from elementary math to professional law. It's one of the most comprehensive knowledge benchmarks and widely used for comparing AI capabilities.

HumanEval measures coding ability by testing if AI can write functional Python code to solve programming problems. It's the primary benchmark for comparing coding assistants like GitHub Copilot and code-focused models.

GSM8K (Grade School Math 8K) tests math reasoning with word problems at a grade school level. Despite being 'simple' problems, they require multi-step reasoning that challenges AI models to show logical thinking.

Scores can vary based on evaluation methodology, prompt formatting, number of test samples, and whether chain-of-thought reasoning is allowed. We report scores from official sources when available and note methodology differences.

Not always. Benchmarks test specific abilities that may or may not match your use case. A model that's best at coding benchmarks might not be best for creative writing. Test models on your actual tasks for the best assessment.

We update our database when major models are released or significant benchmark results are published. Check the last updated date for each benchmark. The AI field moves fast, so some scores may be from recent model updates.

HumanEval is the primary coding benchmark. MBPP (Mostly Basic Python Problems) is also relevant. For real-world coding, also consider how well models handle debugging, code explanation, and multi-file projects - which aren't fully captured in benchmarks.

Writing quality is harder to benchmark objectively. MMLU tests knowledge that helps with factual writing. For creative and persuasive writing, benchmarks are less useful - direct comparison of output quality is more informative.