agents benchmark
Kimi Claw 24/7 Bench is Moonshot AI's in-house benchmark for evaluating long-horizon agentic performance in persistent, multi-day coworking tasks.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score46.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis kimi claw 24/7 bench AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi K2.7 Code currently leads Kimi Claw 24/7 Bench with 46.90%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What Kimi Claw 24/7 Bench measures and how its scores work.
Kimi Claw 24/7 Bench is an agents benchmark spanning 17 professional scenarios and 610 evaluation points, with tasks executed through the OpenClaw harness.
It measures long-horizon agentic performance across software engineering, ML research, recruiting, trading, and marketing tasks using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Kimi Claw 24/7 Bench.
Kimi K2.7 Code is currently ranked first with 46.90%.
The current leaders are Kimi K2.7 Code (46.90%).
Kimi K2.7 Code has the lowest matched official input price at $0.95 input / $4.0 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures long-horizon agentic performance across software engineering, ML research, recruiting, trading, and marketing tasks using the Score metric, reported as a ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.