Daily AI Model Benchmarks
Transparent, automated daily testing of leading AI models across 50 curated prompts. Free and open data to help you choose the best model for your needs.
What are Daily Benchmarks?#
The Daily AI Model Benchmark Leaderboard is a free, public resource that provides transparent, automated testing of leading AI models. Every day at 12:00 AM GMT, a new benchmark test is run across all active models using a curated prompt from our library of 50 test cases.
50 Test Prompts
Comprehensive coverage spanning knowledge, reasoning, math, coding, and more.
Daily Testing
Fresh benchmark results every day at midnight GMT.
9 Categories
Prompts organized across multiple difficulty levels and subject areas.
8 Time Periods
View rolling averages from 1 day to all-time performance.
Open Data
Leaderboard Metrics#
Each model is evaluated across multiple dimensions to give you a complete picture of performance:
| Metric | Description | Why It Matters |
|---|---|---|
| Quality Score | AI-evaluated response quality on a 1-5 scale | Measures how well the model addresses the prompt |
| Latency (ms) | Time to first response token | Important for real-time applications |
| Tokens/Second | Output generation speed | Determines how fast full responses are delivered |
| Cost per 1K | API cost per 1000 tokens | Helps optimize for budget constraints |
| Sample Count | Number of benchmark runs included | Higher counts mean more reliable averages |
Time Periods#
The leaderboard supports multiple time period views to help you understand both recent and long-term model performance:
| Period | Description | Best For |
|---|---|---|
| 1 Day | Most recent benchmark only | Seeing latest results |
| 1 Week | 7-day rolling average | Recent trends |
| 30 Days | Monthly rolling average | Stable performance view |
| 60 Days | Two-month average | Medium-term comparison |
| 90 Days | Quarterly average | Seasonal patterns |
| 180 Days | Six-month average | Long-term stability |
| 1 Year | Annual average | Year-over-year comparison |
| All Time | Complete historical average | Overall track record |
Choosing a Time Period
How Testing Works#
Our automated testing system ensures fair, consistent evaluation across all models:
Daily Prompt Selection
Parallel Execution
Metric Collection
Quality Scoring
Aggregation
Using Benchmark Data#
The benchmark data helps you make informed decisions about model selection for your reports:
Quality First
Sort by quality score to find models that produce the best outputs for your use case.
Speed Matters
For real-time applications, prioritize models with low latency and high tokens/second.
Budget Conscious
Compare cost efficiency to optimize for high-volume report generation.
Consistency
Use longer time periods to identify models with stable, reliable performance.
Pro Tips#
Best Practices
- Check the sample count - higher counts mean more reliable averages
- Use the daily results to see how models perform on specific prompt types
- Share benchmark results with your team using the social share buttons
- Combine benchmark data with your own testing for optimal model selection
- Revisit regularly - model performance can change with provider updates
The benchmarks page also includes individual daily result cards that let you dive into specific test days. Click on any daily benchmark to see the full comparison of how each model performed on that particular prompt.