Performance Metrics
ModelBooth uses various metrics to evaluate and compare AI model performance. Understanding these metrics helps you make informed decisions.
Core Performance Metrics
Overall Performance Score
A composite score (0-100) based on:
- Benchmark test results
- User feedback and ratings
- Expert evaluations
- Real-world performance data
Speed Rating
Response time and throughput measurements:
- Tokens per second generation
- First token latency
- Total response time
- Batch processing speed
Category-Specific Metrics
Language Models
- MMLU: Massive Multitask Language Understanding
- HellaSwag: Common sense reasoning
- ARC: Abstract reasoning corpus
- TruthfulQA: Factual accuracy
- GSM8K: Mathematical reasoning
Reasoning Models
- MATH: Mathematical problem solving
- GPQA: Graduate-level reasoning
- AIME: Mathematical competition problems
- CodeForces: Programming contests
Code Generation
- HumanEval: Python programming tasks
- MBPP: Mostly basic programming problems
- CodeT: Code translation accuracy
- LiveCodeBench: Real-world coding tasks
Multimodal Models
- VQA: Visual question answering
- COCO: Image captioning accuracy
- OCR: Text recognition from images
- Chart QA: Chart and graph understanding
Quality Metrics
Output Quality
- Coherence and fluency
- Factual accuracy
- Relevance to prompts
- Creative quality
Safety and Alignment
- Harmful content detection
- Bias mitigation
- Instruction following
- Robustness to adversarial inputs
Efficiency Metrics
Cost Efficiency
- Performance per dollar
- Cost per task completion
- Value for money ratings
Resource Efficiency
- Memory usage
- Computational requirements
- Energy consumption
- Scalability characteristics
Real-World Performance
User Satisfaction
- Community ratings and reviews
- Adoption rates
- Retention metrics
- Developer feedback
Production Metrics
- Uptime and reliability
- API response consistency
- Error rates
- Support quality
How We Collect Data
Automated Testing
- Regular benchmark runs
- Performance monitoring
- API reliability testing
- Cost tracking
Community Input
- User ratings and reviews
- Developer feedback
- Use case studies
- Performance reports
Interpreting Scores
Score Ranges
- 90-100: Exceptional performance
- 80-89: Very good performance
- 70-79: Good performance
- 60-69: Adequate performance
- Below 60: Limited performance
Context Matters
Always consider:
- Your specific use case requirements
- Trade-offs between speed and quality
- Cost vs performance balance
- Integration complexity