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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Model scores 17 models tested
| # | 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 |