Agentic & Tool Use benchmark

# GDP.pdf leaderboard

> GDP.pdf results for 36 AI models, led by GPT-6 Astra at 34.2%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/gdp-pdf
- Last updated: 2026-10-10
- Title: GDP.pdf Leaderboard (October 2026): Scores by Model

As of October 2026, GPT-6 Astra has the highest published GDP.pdf score on Noometry at 34.2%, out of 36 models with results.

Last verified October 10, 2026

## About GDP.pdf

A description with primary sources is being prepared for this benchmark.

- **Category:** [Agentic & Tool Use](https://noometry.com/best/agentic)
- **Introduced:** 2026
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [epoch.ai](https://epoch.ai/benchmarks)

## Top 15 models

Top models on GDP.pdf

1.  GPT-6 Astra 34.2%
2.  GPT-6.1 Sol 32%
3.  GPT-5.6 Sol 30.7%
4.  Claude Opus 5.5 30.6%
5.  Claude Fable 5 30%
6.  Claude Fable 5.1 29.6%
7.  Muse Spark 1.3 27.6%
8.  GPT-6 Sol 26.4%
9.  GPT-5.5 26%
10.  GPT-5.6 Terra 24.7%
11.  Claude Opus 4.8 24%
12.  Claude Opus 5 24%
13.  Gemini 3.7 Flash 23.8%
14.  Gemini 3.8 Flash 23.4%
15.  Qwen3.8 Max 23.2%
16.  20253035

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

GDP.pdf results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 34.2% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 2 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 32% | high | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| 3 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 30.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 4 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 30.6% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 5 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 30% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 6 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 29.6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 7 | [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 27.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 8 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 26.4% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 9 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 26% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 10 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 24.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 11 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 24% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 12 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 24% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 13 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 23.8% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 14 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 23.4% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 15 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 23.2% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 16 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 23% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 17 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 22.8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 18 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 22.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 19 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 21% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 20 | [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 19.8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 21 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 19% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 22 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 18% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 23 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 17.2% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 24 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 25 | [Qwen3.8 Flash](https://noometry.com/models/qwen3-8-flash) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 16.6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 26 | [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 16% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 27 | [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 28 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 14% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 29 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 14% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 30 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 14% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 31 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 14% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 32 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 33 | [Gemini 3.5 Flash Lite](https://noometry.com/models/gemini-3-5-flash-lite) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 10% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 34 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 35 | [Grok 4.3](https://noometry.com/models/grok-4-3) | [xAI](https://noometry.com/providers/xai) | 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 36 | [Nova 2.0 Pro Preview](https://noometry.com/models/nova-2-0-pro-preview) | [Amazon](https://noometry.com/providers/amazon) | 2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## Compare the leaders

-   [GPT-6 Astra vs GPT-6.1 Sol](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-astra)
-   [GPT-6 Astra vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-astra)
-   [GPT-6 Astra vs Claude Opus 5.5](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-astra)
-   [GPT-6 Astra vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-gpt-6-astra)
-   [GPT-6.1 Sol vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Claude Opus 5.5](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-1-sol)

## Other agentic & tool use benchmarks

-   [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench)
-   [APEX-Agents](https://noometry.com/benchmarks/apex-agents)
-   [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl)
-   [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2)
-   [GDPval](https://noometry.com/benchmarks/gdpval)
-   [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index)
-   [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company)
-   [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline)
-   [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking)
-   [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail)
-   [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom)
-   [Cybench](https://noometry.com/benchmarks/cybench)

## Frequently asked questions

### Which model has the highest GDP.pdf score?

As of October 2026, GPT-6 Astra has the highest published GDP.pdf score on Noometry at 34.2%, out of 36 models with results.

### What is the best open-weight model on GDP.pdf?

DeepSeek V4.1 Flash has the highest GDP.pdf accuracy among open-weight models at 19.8%, ranking 20 of 36 overall.

### Cite this page

Noometry. (2026). GDP.pdf leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/gdp-pdf

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/gdp-pdf.md).