GDPVal-AA v2
GDPVal-AA v2 - Artificial Analysis GDPVal v2 agentic benchmark for evaluating general task completion. Score is an index value (not percentage).
Category
General Agents
Score type
index
Scale
relative
higher is better
Models tested
25
What is GDPVal-AA v2?
GDPVal-AA v2 is a variant of GDPVal, OpenAI's benchmark of real, paid knowledge-work tasks from almost 50 professions (reports, analyses, presentations). Artifacts are judged against professional deliverables; the score is an index, not a percentage. It measures usefulness for actual work — not just exam-style questions.
What does it test?
GDPVal-AA v2 measures how well a model acts as a general-purpose agent. In detail, it checks:
- realistic professional work products
- deliverables that meet expert-defined quality criteria
- long tasks with documents, data and constraints
- breadth across many different occupations
How to read the score
The score is a composite index published by the benchmark organiser rather than a plain percentage. Interpret it only against the other models in the table — higher is better.
Frequently asked questions
What is the GDPVal-AA v2 benchmark?
GDPVal-AA v2 is a variant of GDPVal, OpenAI's benchmark of real, paid knowledge-work tasks from almost 50 professions (reports, analyses, presentations). Artifacts are judged against professional deliverables; the score is an index, not a percentage. It measures usefulness for actual work — not just exam-style questions.
What does GDPVal-AA v2 test?
GDPVal-AA v2 mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: realistic professional work products; deliverables that meet expert-defined quality criteria; long tasks with documents, data and constraints.
How is the GDPVal-AA v2 score calculated?
The score is a composite index published by the benchmark organiser rather than a plain percentage. Interpret it only against the other models in the table — higher is better.
Where do I get GDPVal-AA v2 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-aa-v2/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 25 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
GLM-5.3-Flash
Zhipu AI
|
1773 idx
|
27.08.2026 | Report | Verified |
| 2 |
GLM-5.3
Zhipu AI
|
1769 idx
|
28.08.2026 | Report | Verified |
| 3 |
Qwen3.8-2.4T-A95B
Qwen
|
1739 idx
|
28.08.2026 | Report | Verified |
| 4 |
Kimi K3
Moonshot AI
|
1686 idx
|
18.08.2026 | Report | Verified |
| 5 |
Hy4 preview
Tencent
|
1678 idx
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Elo) | Verified |
| 6 |
DeepSeek-V4-Pro-0813
DeepSeek
|
1590 idx
|
28.08.2026 | Report | Verified |
| 7 |
DeepSeek-V4-Pro
DeepSeek
|
1554 idx
|
26.06.2026 | Report | Verified |
| 8 |
GLM-5.2
Zhipu AI
|
1504 idx
|
27.08.2026 | Report | Verified |
| 9 |
Kimi K2.6
Moonshot AI
|
1482 idx
|
26.06.2026 | Report | Verified |
| 10 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
1401 idx
|
15.10.2026 | Report | Verified |
| 11 |
DeepSeek-V4-Flash
DeepSeek
|
1395 idx
|
26.06.2026 | Report | Verified |
| 12 |
Hy3
Tencent
|
1213 idx
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Elo) | Verified |
| 13 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
1141 idx
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Unverified |
| 14 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
953 idx
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Verified |
| 15 |
Gemma4-31B-it
Google
|
811 idx
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Unverified |
| 16 |
NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB
NVIDIA
|
28.47 idx
|
15.10.2026 | Report | Verified |
| 17 |
MiniCPM5-2B
OpenBMB
|
19.6 idx
|
— | Report | Verified |
| 18 |
Qwen3.5-4B
Qwen
|
11.7 idx
|
— | Report | Verified |
| 19 |
LFM2.5-2.6B
LiquidAI
|
4.5 idx
|
— | Report | Verified |
| 20 |
Qwen3.5-2B
Qwen Team (Alibaba Cloud)
|
0 idx
|
— | Report | Verified |
| 21 |
Gemma4-E2B
Google
|
0 idx
|
— | Report | Verified |
| 22 |
granite-4.2-3B
IBM
|
0 idx
|
— | Report | Verified |
| 23 |
Nemotron-3-Nano-4B
NVIDIA
|
0 idx
|
— | Report | Verified |
| 24 |
Gemma4-E4B
Google
|
0 idx
|
— | Report | Verified |
| 25 |
LFM2.5-8B-A1B
LiquidAI
|
0 idx
|
— | Report | Verified |