General Agents Benchmark

Automation-Bench

Automation-Bench - evaluated on the 600-task public subset. Pass@1 score.

Category

General Agents

Score type

percent

Scale

relative

higher is better

Models tested

17

What is Automation-Bench?

Automation-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "Automation-Bench - evaluated on the 600-task public subset". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

Automation-Bench measures how well a model acts as a general-purpose agent. In detail, it checks:

  • solving long, multi-step tasks with many intermediate decisions
  • using tools correctly (browsers, terminals, APIs, calendars, other apps)
  • adapting when something unexpected happens mid-task
  • staying on goal over long task horizons instead of wandering off

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: Automation-Bench - evaluated on the 600-task public subset. Pass@1 score.

Frequently asked questions

What is the Automation-Bench benchmark?

Automation-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'Automation-Bench - evaluated on the 600-task public subset'. Below we describe in simple words what benchmarks of this type generally examine.

What does Automation-Bench test?

Automation-Bench mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: solving long, multi-step tasks with many intermediate decisions; using tools correctly (browsers, terminals, APIs, calendars, other apps); adapting when something unexpected happens mid-task.

How is the Automation-Bench score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Where do I get Automation-Bench data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/automation-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 17 models tested

Reset
# Model Score Date Source Verified
1 MiniMax-M3.1-Flash
MiniMaxAI
62.8%
19.10.2026 Report Verified
2 DeepSeek-V4.1-Flash-1017
DeepSeek
60.5%
17.10.2026 Report Verified
3 DeepSeek-V4.1-Flash
DeepSeek
54.8%
10.09.2026 Report Verified
4 Nanbeige4.3-33B
Nanbeige
54.2%
24.10.2026 Report Verified
5 GLM-5.3-Flash
Zhipu AI
48.8%
27.08.2026 Report Verified
6 GLM-5.3
Zhipu AI
48.2%
28.08.2026 Report Verified
7 Hy4 preview
Tencent
32.1%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
8 DeepSeek-V4-Pro-0813
DeepSeek
31.8%
13.08.2026 Report Verified
9 Kimi K3
Moonshot AI
30.8%
18.08.2026 Report Verified
10 LFM2.5-24B-A2B
LiquidAI
27.69%
13.10.2026 Report Verified
11 Qwen3.8-2.4T-A95B
Qwen
27.3%
18.08.2026 Report Verified
12 DeepSeek-V4-Flash-Vision-Exp
DeepSeek
25.7%
08.09.2026 Report Verified
13 DeepSeek-V4-Flash-0731
DeepSeek
25.1%
31.07.2026 Report Verified
14 Hy3
Tencent
16.1%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
15 GLM-5.2
Zhipu AI
12.9%
31.07.2026 Report Verified
16 DeepSeek-V4-Pro
DeepSeek
12.8%
13.08.2026 Report Verified
17 DeepSeek-V4-Flash
DeepSeek
10.8%
13.08.2026 Report Verified

What now?

You've seen how models score on Automation-Bench — here's what to do next.