AI Model Benchmarks
Compare AI model performance across standard evaluation benchmarks
Related Tools
Model Capability Matrix
Compare feature support (vision, function calling, json mode) across major LLMs
LLM Head-to-Head
Directly compare two models on specs, pricing, and capabilities side-by-side
Context Window Visualizer
Visual comparison of context window sizes across different models
AI Model Pricing Tracker
Up-to-date pricing table for input/output tokens across all major providers
AI System Status Board
Aggregated status page for OpenAI, Anthropic, Google, and other AI services
AI Model Release Timeline
Interactive timeline of major LLM and generative AI model releases
What Are AI Benchmarks?
AI benchmarks are standardized tests used to evaluate and compare the capabilities of different language models. They provide objective metrics that help developers choose the right model for their needs, rather than relying solely on marketing claims or subjective impressions.
This tool displays data from the HuggingFace Open LLM Leaderboard, one of the most respected independent benchmark collections in the AI community. Scores are updated regularly as new models are tested and added to the leaderboard.
However, benchmarks have limitations — they may not perfectly reflect real-world performance, and models are sometimes optimized specifically for benchmark scores. Use them as one input among many when selecting a model for production use.
How to Use This Tool
Select a Benchmark
Use the benchmark selector to choose which test to compare. Start with "Average" for an overall view, then drill into specific benchmarks relevant to your use case.
Filter by Provider
Narrow results to specific providers to compare models within the same ecosystem, or see how different providers compare on the same tests.
Sort and Search
Click column headers to sort by score. Use the search box to find specific models. The bar visualization makes it easy to compare performance at a glance.
Copy as Markdown
Export the current filtered table as Markdown for documentation, presentations, or sharing benchmark data with your team.
Benchmark Definitions
IFEval (Instruction Following)
Tests how well models follow specific instructions. Critical for applications requiring precise output formats or constraints.
BBH (BIG-Bench Hard)
23 challenging tasks from BIG-Bench including logical reasoning, word problems, and multi-step thinking challenges.
MATH Level 5
Competition-level mathematics problems requiring advanced reasoning and multi-step problem solving.
GPQA (Science Q&A)
Graduate-level science questions across physics, chemistry, and biology. Tests deep domain knowledge.
MuSR (Multi-step Reasoning)
Tests ability to chain multiple reasoning steps together to reach correct conclusions.
MMLU-PRO
Massive Multitask Language Understanding Professional - tests knowledge across 57 subjects including STEM and humanities.
Pro Tip: Choosing the Right Benchmark
Match benchmarks to your use case: MATH and BBH for reasoning-heavy applications, IFEval for structured outputs, MMLU-PRO for general knowledge tasks, and GPQA for scientific applications. Don't just look at the average score — a model with lower average but higher scores on relevant benchmarks may perform better for your specific needs.
Interpreting Benchmark Scores
Important: Benchmark Limitations
Benchmarks have known limitations. Models may be trained on benchmark data (contamination), leading to inflated scores. Benchmarks test specific capabilities that may not match your use case. Always validate with your own evaluation suite before production deployment.
Frequently Asked Questions
Do higher benchmark scores always mean better performance?
Not always. Benchmarks test specific capabilities, but your use case may require different skills. A model with lower MMLU but higher HumanEval might be better for coding tasks. Additionally, benchmark scores don't capture factors like response style, safety alignment, or handling of edge cases.
Why are some scores missing?
Not all providers publish benchmark results for all tests, or the data may not yet be available for newer models. Some closed-source models haven't been independently evaluated. Missing data doesn't necessarily indicate poor performance.
Where does this benchmark data come from?
Benchmark data is sourced from the HuggingFace Open LLM Leaderboard, which conducts standardized evaluations of open-weight models. For closed-source models, we use official provider reports where available. Data is refreshed regularly to include new model releases.
How should I use benchmarks for model selection?
Use benchmarks as a first filter to narrow candidates, then conduct your own evaluation on representative samples of your actual tasks. Consider cost-performance ratio — a model scoring 5% lower but costing 80% less may be the better choice for your use case.
What is benchmark contamination?
Benchmark contamination occurs when models are trained on data that appears in evaluation sets, leading to artificially inflated scores. The AI community actively works to detect and address this, including creating new benchmarks and using held-out test sets.
Related Tools
Model Comparison
Compare pricing and specs alongside benchmark performance to find the best value for your use case.
Capabilities Matrix
See which features each model supports — vision, tools, streaming, and more.
Pricing Table
Compare benchmark performance against pricing to calculate cost-effectiveness.
Context Windows
Find models with the context length you need for document processing or long conversations.