Apex-Agents
Category
General Agents
Score type
percent
Scale
100%
higher is better
Models tested
12
What is Apex-Agents?
Apex-Agents is a benchmark for how well a model acts as a general-purpose agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.
What does it test?
Apex-Agents 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.
Frequently asked questions
What is the Apex-Agents benchmark?
Apex-Agents is a benchmark for how well a model acts as a general-purpose agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.
What does Apex-Agents test?
Apex-Agents 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 Apex-Agents 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 Apex-Agents 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/apex-agents/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 12 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
DeepSeek-V4.1-Flash-1017
DeepSeek
|
69.8%
|
07.11.2026 | Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro | Unverified |
| 2 |
Kimi K3.1 Pro
Moonshot AI
|
69.4%
|
07.11.2026 | Report | Verified |
| 3 |
Kimi K3
Moonshot AI
|
41%
|
18.08.2026 | Report | Verified |
| 4 |
DeepSeek-V4-Pro
DeepSeek
|
38.3%
|
26.06.2026 | Report | Verified |
| 5 |
GLM-5.3
Zhipu AI
|
38.1%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) | Verified |
| 6 |
Hy4 preview
Tencent
|
37.1%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 7 |
Qwen3.8-2.4T-A95B
Qwen
|
34%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) | Verified |
| 8 |
DeepSeek-V4-Flash
DeepSeek
|
33%
|
26.06.2026 | Report | Verified |
| 9 |
Kimi K2.6
Moonshot AI
|
27.9%
|
23.08.2026 | Report | Verified |
| 10 |
MiniMax-M3
MiniMaxAI
|
27.7%
|
11.06.2026 | MiniMax-M3 official evaluation metadata | Verified |
| 11 |
Hy3
Tencent
|
24.4%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 12 |
Kimi K2.5
Moonshot AI
|
11.5%
|
23.08.2026 | Report | Verified |