General Agents Benchmark

JobBench

JobBench - in-house benchmark for evaluating professional job task completion.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

11

What is JobBench?

JobBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "JobBench - in-house benchmark for evaluating professional job task completion". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

JobBench 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: JobBench - in-house benchmark for evaluating professional job task completion.

Frequently asked questions

What is the JobBench benchmark?

JobBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'JobBench - in-house benchmark for evaluating professional job task completion'. Below we describe in simple words what benchmarks of this type generally examine.

What does JobBench test?

JobBench 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 JobBench score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get JobBench 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/jobbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 11 models tested

Reset
# Model Score Date Source Verified
1 Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
61.8%
15.10.2026 Report Verified
2 Hy4 preview
Tencent
61.7%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
3 GLM-5.3
Zhipu AI
58.2%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) Verified
4 Qwen3.8-Flash-Next
Qwen Team (Alibaba)
55.7%
26.08.2026 Report Verified
5 Kimi K3
Moonshot AI
54.3%
18.08.2026 Report Verified
6 Qwen3.8-2.4T-A95B
Qwen
53.4%
18.08.2026 Report Verified
7 Nanbeige4.3-33B
Nanbeige
44.8%
24.10.2026 Report Verified
8 DeepSeek-V4-Flash-0731
DeepSeek
41.3%
26.08.2026 Report Unverified
9 Hy3
Tencent
34.6%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
10 Qwen3.8-27B
Qwen Team (Alibaba Cloud)
33.4%
18.08.2026 Report Verified
11 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
21.8%
18.08.2026 Report Unverified

What now?

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