Basic Information
| Item | Details |
|---|
| Product Name | OpenClaw Benchmarks |
| Product Type | Performance Benchmarking and Evaluation |
| Benchmark Framework | OOLONG Benchmark, etc. |
| Latest Score | OpenClaw + lossless-claw: 74.8 points |
| Comparison Benchmark | Claude Code: 70.3 points |
| Relation to OpenClaw | Performance Evaluation and Optimization Reference |
Product Overview
OpenClaw Benchmarks is a collection of benchmark tests used to evaluate the performance, accuracy, and efficiency of OpenClaw. As OpenClaw becomes the most-watched AI agent platform, standardized performance benchmarks are crucial for user selection and project optimization.
OOLONG Benchmark
Test Results
| Solution | Score | Notes |
|---|
| OpenClaw + lossless-claw | 74.8 | Highest Score |
| Claude Code | 70.3 | Comparison Benchmark |
Test Dimensions
| Dimension | Description |
|---|
| Code Completion | Accuracy of code auto-completion |
| Multi-file Refactoring | Ability to refactor code across files |
| Spreadsheet Analysis | Data analysis and processing capabilities |
| Task Orchestration | Execution capability of multi-step tasks |
Benchmarking Practices
Custom Benchmarking Recommendations
- Head-to-Head Testing - Conduct comparative tests on actual workloads
- Standard Scoring - Use a unified scoring standard
- Multi-dimensional Evaluation - Code completion, multi-file refactoring, spreadsheet analysis
- Reproducibility - Ensure test results are reproducible
Performance Metrics
| Metric | Description | Importance |
|---|
| Response Latency | Time to first response | High |
| Task Completion Rate | Proportion of successfully completed tasks | High |
| Resource Consumption | CPU, memory, network usage | Medium |
| API Cost | LLM API call costs | Medium |
| Accuracy | Correctness of output results | High |
Resource Consumption Comparison
Resource Comparison with Alternatives
| Solution | Memory Usage | Features |
|---|
| OpenClaw | ~390MB | Full-featured |
| ZeroClaw (Rust) | <5MB | Extremely lightweight |
| NanoClaw | Low | 700 lines of code |
| Nanobot (Python) | Medium | 4,000 lines of code |
Optimization Strategies
| Strategy | Effect |
|---|
| Parallel Sub-agent Calls | Serial 45s -> Parallel <20s |
| Query Caching | Reduce 30-50% model call costs |
| Model Warm-up | Avoid first request latency spikes |
| Prompt Template Pre-compilation | Reduce repetitive computation overhead |
Benchmarking Challenges
Evaluation Difficulties
- AI agent outputs are non-deterministic, making precise comparisons difficult
- Performance varies significantly across different usage scenarios
- Performance differences between local LLM and cloud API
- Hardware configuration significantly impacts performance
Community Needs
- Standardized AI agent benchmarking framework
- Reproducible test environments and datasets
- Benchmarking covering security aspects
- Long-term operational stability testing
Sources
External References
Learn more from these authoritative sources: