General Agents Benchmark

GDPVal

GDPVal - agentic benchmark for evaluating general task completion.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

11

What is GDPVal?

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

What does it test?

GDPVal 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: GDPVal - agentic benchmark for evaluating general task completion.

Frequently asked questions

What is the GDPVal benchmark?

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

What does GDPVal test?

GDPVal 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 GDPVal 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 GDPVal 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/gdpval/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 Nanbeige4.3-33B
Nanbeige
84.8%
24.10.2026 Report Verified
2 MiniMax-M3
MiniMaxAI
74.8%
11.06.2026 MiniMax-M3 model card benchmark table Unverified
3 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
67.4%
21.10.2026 Report Unverified
4 Muse-Glimmer-30B
Meta Superintelligence Lab
67.3%
21.10.2026 Report Unverified
5 Raon-1 mini
KRAFTON AI
66%
21.10.2026 Report Verified
6 Qwen3.8-27B
Qwen Team (Alibaba Cloud)
63.5%
21.10.2026 Report Unverified
7 Gemma4-26B-A4B
Google
57.5%
21.10.2026 Report Unverified
8 DeepSeek-V4-Pro
DeepSeek
54.6%
04.06.2026 Report Unverified
9 Kimi K2.6
Moonshot AI
50.4%
04.06.2026 Report Unverified
10 DeepSeek-V4-Flash
DeepSeek
50.2%
04.06.2026 Report Unverified
11 NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
46.7%
04.06.2026 Report Verified

What now?

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