General Agents Benchmark

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.

Model scores 12 models tested

Reset
# 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

What now?

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