reasoning benchmark
AutomationBench is a tool-use benchmark that evaluates AI agents on automating real-world workflows.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score49.40% | Percentile100.00% | Participants18 | EvidenceC | Evaluated |
| Rank02 | ModelZA | Score48.80% | Percentile94.12% | Participants18 | EvidenceC | Evaluated |
| Rank03 | ModelZA | Score48.20% | Percentile88.24% | Participants18 | EvidenceC | Evaluated |
| Rank04 | ModelOP | Score41.40% | Percentile82.35% | Participants18 | EvidenceC | Evaluated |
| Rank05 | ModelTE | Score32.10% | Percentile76.47% | Participants18 | EvidenceC | Evaluated |
| Rank06 | ModelDE | Score31.80% | Percentile70.59% | Participants18 | EvidenceC | Evaluated |
| Rank07 | ModelAN | Score31.40% | Percentile64.71% | Participants18 | EvidenceC | Evaluated |
| Rank08 | ModelMA | Score30.80% | Percentile58.82% | Participants18 | EvidenceC | Evaluated |
| Rank09 | ModelGO | Score30.40% | Percentile52.94% | Participants18 | EvidenceC | Evaluated |
| Rank10 | ModelAC | Score27.30% | Percentile47.06% | Participants18 | EvidenceC | Evaluated |
| Rank11 | ModelAN | Score26.00% | Percentile41.18% | Participants18 | EvidenceC | Evaluated |
| Rank12 | ModelDE | Score25.70% | Percentile35.29% | Participants18 | EvidenceC | Evaluated |
| Rank13 | ModelDE | Score25.10% | Percentile29.41% | Participants18 | EvidenceC | Evaluated |
| Rank14 | ModelOP | Score18.10% | Percentile23.53% | Participants18 | EvidenceC | Evaluated |
| Rank15 | ModelAN | Score17.40% | Percentile17.65% | Participants18 | EvidenceC | Evaluated |
| Rank16 | ModelOP | Score15.20% | Percentile11.76% | Participants18 | EvidenceC | Evaluated |
| Rank17 | ModelOP | Score14.90% | Percentile5.88% | Participants18 | EvidenceC | Evaluated |
| Rank18 | ModelAN | Score13.50% | Percentile0.00% | Participants18 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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.
What AutomationBench measures and how its scores work.
AutomationBench is a tool-use benchmark for evaluating AI agents on real-world workflow automation.
It measures agents' ability to orchestrate tools and complete multi-step automation tasks using the Score metric in ratio units.
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 AutomationBench.
Muse Spark 1.3 is currently ranked first with 49.40%.
The current leaders are Muse Spark 1.3 (49.40%), GLM-5.3-Flash (48.80%), and GLM-5.3 (48.20%).
GLM-5.3-Flash has the lowest matched official input price at $0.08 input / $0.25 output per 1M tokens.
The fastest matched records are GLM-5.3 (472.69 tok/s via FriendliAI), Gemini 3.7 Flash (117.86 tok/s via Google), and GPT-5.6 Terra (99.35 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures agents' ability to orchestrate tools and complete multi-step automation tasks using the Score metric in ratio units.
Yes. Higher values rank better for this benchmark.
18 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis automationbench 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
Muse Spark 1.3 currently leads AutomationBench with 49.40%. 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.