Agent Benchmarking
Learn to measure and compare AI agent performance using standardized benchmarks
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0 / 5 completedUnderstanding Benchmark Results
Raw benchmark scores tell part of the story, but interpretation reveals insights. A 78% pass rate means nothing without context: Is that good? How does it compare to competitors? What's the cost-performance trade-off? Learn to analyze results holistically and make data-driven improvement decisions.
Interactive: Leaderboard Analyzer
Compare agents across multiple dimensions. Sort by different metrics to find trade-offs:
| Rank | Agent | Pass Rate | Latency | Cost/Task | Reliability |
|---|---|---|---|---|---|
| #1 | GPT-4 | 87.2% | 3.2s | $0.15 | 94% |
| #2 | Claude 3 | 84.5% | 2.8s | $0.12 | 92% |
| #3 | Gemini Pro | 81.7% | 2.5s | $0.10 | 90% |
| #4 | Your Agent(You) | 78.5% | 2.1s | $0.08 | 88% |
| #5 | GPT-3.5 | 72.3% | 1.5s | $0.04 | 85% |
Key Insights from Results
- •Trade-offs Exist: GPT-4 leads in accuracy but costs 2x more than your agent
- •Your Position: Middle of the pack on accuracy, but fastest and cheapest option
- •Improvement Path: Focus on accuracy—you're 9% behind top performer (GPT-4)
- •Competitive Edge: Your speed and cost advantage could win price-sensitive users
Action Plan Based on Results
Analyze failed test cases to find common error patterns. Focus on top 3 failure categories that account for most errors.
Improve prompt engineering and add validation logic. Target 85% pass rate to be competitive with top-tier agents.
Fine-tune model on domain-specific data. Maintain cost advantage while reaching 90%+ pass rate for market leadership.
Don't obsess over being #1 on every benchmark. A coding agent doesn't need to beat GPT-4 on general knowledge. Focus on benchmarks your users care about, and optimize for the right trade-offs (accuracy vs cost, speed vs reliability). Being the best fit for your use case beats being the best overall.