ClawProBench

Transparent live-first benchmark harness for evaluating model capability inside the OpenClaw runtime.
102 active scenarios, 162 catalog scenarios, deterministic grading, and OpenClaw-native coverage.

简体中文 README

ClawProBench focuses on real OpenClaw execution with deterministic grading, structured reports, and benchmark-profile selection. The default ranking path is the core profile; broader active coverage remains available through intelligence, coverage, native, and full.

The current worktree inventory reports 102 active scenarios and 162 total catalog scenarios (60 incubating) via python3 run.py inventory --json and python3 run.py inventory --benchmark-status all --json.

Leaderboard

Browse the public leaderboard and benchmark cases at suyoumo.github.io/bench.

Get Involved

We sincerely thank friends from Kimi and Qwen for their helpful feedback and improvement suggestions for the ClawProBench leaderboard.

We also thank LongCat, Kimi, Ant Ling, and MiMo for providing model access support, trial access, or platform resources. This support helped reduce evaluation costs and made it possible to cover more frontier and preview models in a transparent live-runtime setting.

If domestic third-party API gateway providers would like their served models, such as Claude 4.7 Opus or GPT-5.5, to appear on the leaderboard, please contact us. We can run the benchmark and publish reproducible results when the evaluation setup is stable.

To run ClawProBench, submit results, or discuss model evaluation for the leaderboard, contact: xyh920691910@outlook.com.

Blog

• ClawProBench Close DataSet LeadBoard Release：ClawProBench Closed Dataset Release and Analysis
• Safety Under Live Agent Work: What the ClawProBench Leaderboard Shows
• My Feelings During the Development of ClawProBench
• Open-sourcing ClawProBench: Bringing Agent Benchmarks Back to the Real Runtime

📢 Updates

• v2.0.0 - Released the closed-dataset leaderboard with 33 model results; added clickable closed-dataset model detail pages, closed-dataset visualization charts, and closed-dataset task browsing in Tasks.
• v1.1.6 - Added Shanghai AI Lab intern-s2-preview to the open-source model leaderboard.
• v1.1.5 - Added the ring-2.6-1T-xhigh leaderboard result; the leaderboard now includes 65 models.
• v1.1.4 - Added leaderboard results for Baidu ERNIE 5.1 and SenseTime Sensenova 6.7 Flash Lite. (We are glad to see ClawProBench attracting growing attention. Because the benchmark is fully open source, it cannot fully avoid vendors optimizing specifically for the public benchmark; a leaderboard based on a closed ClawProBench dataset will be released soon. The open-source dataset portion and evaluation harness for my coding benchmark are also expected to be open-sourced within the next 1-2 weeks. Stay tuned.)
• v1.1.3 - Added OpenAI gpt-5.5, gpt-5.4, and gpt-5.3-codex leaderboard results, which now rank 1-3; also added DeepSeek-R1 and kimi-for-coding-k2.6, and synced the latest live-runtime, custom-check loading, and scenario-grading fixes.
• v1.1.2 - Added leaderboard data for qwen3.5-397b-a17b, fixed pricing and release-date metadata for several models, and added ModelPK for detailed model-to-model comparison.
• v1.1.1 - Added leaderboard results for kimi-k2.6; the leaderboard now includes 57 models.
• v1.1.0 - Added leaderboard results for qwen3.6-27b, qwen3.6-35b-a3b, and qwen3.6-flash.
• v1.0.9 - Verified model-detail data across the leaderboard, fixed several data errors, added DeepSeeK-V4-Pro, DeepSeek-V4-Flash, LongCat-2.0-Preview, and Ling-2.6-1T, and introduced the new FinalScore metric based on pass^3, pass@3, and average_score.
• v1.0.8 - Added 6 new leaderboard models: qwen3.6-max-preview, mimo-v2.5, mimo-v2.5-pro, hunyuan-t1, hy3-preview, and Ling-2.6-Flash.
• v1.0.7 - Synced benchmark bug fixes from the latest harness line, including --exclude-scenario filtering, isolated live-run runtime hardening, and trace-argument compatibility fixes for custom scoring.
• v1.0.6 - Fixed the leaderboard sticky-header sync bug that could appear when dragging the horizontal scrollbar with a mouse. Added the qwen3.6-plus Token Plan result to the leaderboard.
• v1.0.5 - Fixed the qwen3.6-plus model detail bug where the Bailian and Qwen Coding Plan entries incorrectly showed duplicated per-task scores.
• v1.0.4 - Fixed isolated live-run log pollution that could cause false execution failures. Added kimi-k2.6-code-preview; the leaderboard now includes 43 model results.
• v1.0.3 - Reviewed leaderboard, detail, and raw-result consistency across 40+ benchmark models; fixed confirmed data mismatches for doubao-seed-code, qwen3.6-plus, qwen3-max-2026-01-23, astron-code-latest, and ERNIE-4.5-Turbo.
• v1.0.2 - Added kimi-for-coding, gemma4-31b, and kimi-k2-thinking; improved image download flows for easier mobile-device browsing.
• v1.0.1 - Added qwen3-coder-next, doubao-seed-code, qwen3-max-2026-01-23, and qwen3.6plus rerun with bailiancodingplan; added model image download and benchmark sharing to Twitter; fixed completed-report resume overwrite, tool_use_14 graceful fallback on skills inventory load failure, tool_use_17 invalid JSON and missing-file tolerance, and audit_scenario_quality.py compatibility.
• v1.0.0 - ClawProBench released with 102 tasks across 6 domains, with 3-try runs, checkpoint resume, and cross-environment resume support.

Evaluation Logic

• Default ranking path: core
• Extended active capability suite: intelligence
• Native-only slice: native
• Multi-trial runs are supported via --trials N
• Key leaderboard metrics now include pass^3, pass@3, average_score, and FinalScore
• FinalScore = 100 × S^0.40 × r_all^0.45 × r_any^0.15, where S = average_score, r_all = (pass^3)^(1/3), and r_any = 1 - (1 - pass@3)^(1/3)
• This is intended to weight stable repeated success most heavily, while still preserving overall quality and upside from best-of-3 performance
• Reports expose avg_score, max_score, coverage-aware summaries, cost, latency, and resume metadata
• Interrupted runs can continue with --continue or --resume-from, and execution failures can be re-queued with --rerun-execution-failures

Quick Start

We recommend using uv for fast, reliable Python environment setup:

pip install uv
uv venv --python 3.11
source .venv/bin/activate
uv pip install -r requirements.txt

Before running the benchmark, make sure your local OpenClaw runtime is available:

openclaw --help
openclaw agents list --json

Inspect the benchmark catalog and validate the scenario set:

python3 run.py inventory
python3 run.py inventory --json
python3 run.py dry

Run a one-trial smoke on the default ranking benchmark:

python3 run.py run \
  --model '<MODEL>' \
  --execution-mode live \
  --benchmark-profile core \
  --trials 1 \
  --cleanup-agents

Run the full default benchmark:

python3 run.py run \
  --model '<MODEL>' \
  --execution-mode live \
  --benchmark-profile core \
  --trials 3 \
  --cleanup-agents

Compare generated reports:

python3 run.py compare --results-dir results

For isolated same-host runs, the harness also supports:

• --openclaw-profile
• --openclaw-state-dir
• --openclaw-config-path
• --openclaw-gateway-port
• --openclaw-binary

Benchmark Profiles

Profile	Active scenarios	Purpose
core	26	Default ranking suite
intelligence	95	Extended active capability benchmark
coverage	7	Lower-stakes breadth and regression slice
native	36	Active OpenClaw-native slice only
full	102	Union of all active scenarios

The benchmark catalog also includes 60 incubating scenarios that can be inspected with --benchmark-status all.

OpenClaw Runtime

Live runs expect a working local openclaw CLI plus the auth and config required by the surfaces exercised by the selected scenarios. If your binary is not on PATH, set OPENCLAW_BINARY or pass --openclaw-binary.

config/openclaw.json.template is provided as a reference template for local OpenClaw configuration and isolated-run setups.

Repo Map

• run.py: CLI entrypoint for inventory, dry, run, and compare
• harness/: loader, runner, scoring, reporting, and live OpenClaw bridge
• scenarios/: benchmark tasks in YAML
• datasets/: seeded live-task data and optional setup / teardown scripts
• custom_checks/: scenario-specific grading logic
• tests/: regression coverage for loader, runner, scoring, and reporting
• docs/: public assets plus evaluation validation and benchmark-profile policy

Generated Output

Benchmark reports are written to results/. They are generated runtime artifacts and are intentionally ignored by version control in this repo layout.

Citation

If you use ClawProBench in your research, please cite:

@misc{clawprobench2026,
  title={ClawProBench — a transparent benchmark for true intelligence in real-world AI agents.},
  author={suyoumo},
  year={2026},
  url={https://github.com/suyoumo/ClawProBench}
}

Contribution

We welcome issues, documentation fixes, scenario improvements, grader hardening, and benchmark-engine contributions. See CONTRIBUTING.md for setup and validation guidance.

Acknowledgements

This project was informed by prior open-source work on agent evaluation, benchmark design, and real-world task assessment.

We drew ideas from projects such as PinchBench, Claw-Eval, AgencyBench, and related agent-benchmark efforts, especially in areas like task design, evaluation methodology, harness structure, and public benchmark presentation.

Some tasks in this repository are adapted and reworked from earlier public benchmark-style task sets into the OpenClaw runtime and grading framework.

Contributors

Public contributor list: waiting.