AgentWorldBench Overall
AgentWorldBench Overall - mean across all 7 agent interaction domains (MCP, Search, Terminal, SWE, Android, Web, OS). Five-dimensional rubric, normalized to 0-100.
Category
General Agents
Score type
percent
Scale
100%
higher is better
Models tested
4
What is AgentWorldBench Overall?
AgentWorldBench Overall is a benchmark for how well a model acts as a general-purpose agent. The official description says: "AgentWorldBench Overall - mean across all 7 agent interaction domains (MCP, Search, Terminal, SWE, Android, Web, OS)". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
AgentWorldBench Overall 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: AgentWorldBench Overall - mean across all 7 agent interaction domains (MCP, Search, Terminal, SWE, Android, Web, OS). Five-dimensional rubric, normalized to 0-100.
Frequently asked questions
What is the AgentWorldBench Overall benchmark?
AgentWorldBench Overall is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'AgentWorldBench Overall - mean across all 7 agent interaction domains (MCP, Search, Terminal, SWE, Android, Web, OS)'. Below we describe in simple words what benchmarks of this type generally examine.
What does AgentWorldBench Overall test?
AgentWorldBench Overall 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 AgentWorldBench Overall 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 AgentWorldBench Overall data and scores?
The official source is https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B. 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/agentworldbench-overall/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.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
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 4 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen-AgentWorld-35B-A3B
Qwen Team (Alibaba Cloud)
|
56.39%
|
24.06.2026 | Qwen Team (official model card) | Verified |
| 2 |
Kimi K2.6
Moonshot AI
|
53.42%
|
24.06.2026 | Qwen Team (official model card - comparison) | Unverified |
| 3 |
DeepSeek-V4-Pro
DeepSeek
|
52.97%
|
24.06.2026 | Qwen Team (official model card - comparison) | Unverified |
| 4 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
47.73%
|
24.06.2026 | Qwen Team (official model card - comparison) | Unverified |