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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 11 models tested
| # | 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 |