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.
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 |
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 |