What Is AI Benchmarking? How We Test AI Models
What Is AI Benchmarking?
AI benchmarking is the process of testing AI models on standardized tasks to compare their performance. But not all benchmarks are created equal.
Why Most Benchmarks Suck
Most AI benchmarks use:
- Synthetic tasks — Toy problems that don’t reflect real work
- Single-turn tests — Don’t measure sustained performance
- No cost analysis — Ignore real-world economics
How We’re Different
We test on:
- Real engineering tasks — Bug fixes, API integrations, docs
- Multi-turn scenarios — How models perform over time
- Full cost analysis — Including latency and token costs
Our Scoring Rubric
Each task scores 0-10 on:
- Correctness — Does it solve the problem?
- Efficiency — Is the solution minimal?
- Clarity — Is the explanation clear?
Key Metrics We Track
Performance
- Task completion rate
- Accuracy
- Quality scores
Cost
- Tokens used
- API cost per task
- Cost efficiency
Latency
- Time to first token
- Total response time
- Tokens per second
Reliability
- Failure rate
- Inconsistency
- Error types
Why This Matters
Benchmarks should help you:
- Pick the right model
- Understand tradeoffs
- Optimize costs
That’s what we deliver.