All benchmarks
Multimodal

GDP.pdf

100 tasks built from real professional PDFs, such as manuals, wiring diagrams, blueprints and contracts, across ten fields. The model must find the passage, table or diagram that matters and answer from it. Share of tasks that meet every criterion in their rubric.

Understanding photos, diagrams and documents alongside text. One of 4 comparable tests in multimodal, a category the Index averages.

Read with care. A task counts only when the answer meets every criterion in its rubric, so scores are low for every model. Written and graded by Surge AI.

Publisher: Surge AI

What a model is asked to do

Answer a question from real working documents: find the right page, table or diagram in a PDF and get every required fact right.

For exampleFrom the attached 60-page equipment manual, give the torque for the cover bolts on the 2019 revision and the tool the manual says to use, and nothing the manual does not say.

Why it matters. Most professional knowledge lives in PDFs. An answer that sounds right but is not in the document is the failure that matters at work.

The example is original and illustrative, not an item from the dataset.

Models scored here
37
Who produced the numbers
Official leaderboard
Items graded
n = 100 (1.0% each)
Best published result
35.0%
Newest release covered
October 12, 2026 (active)
License
CC BY 4.0 (via Epoch AI)
Direction
Higher is better
Scale
Published as a share of items solved. For the Index, each score is a share of the best published result (35.0%), so the frontier reads 100. Guessing earns nothing on this test, so no chance correction applies.
Provenance
Produced by the benchmark's own leaderboard or a third-party evaluator, not by the model's maker.

Snapshot November 10, 2026

Ranking on this test

37 models on GDP.pdf

Best published run per model, and how close each one comes to the best published result on this test (the frontier reads 100). Every number links to the publisher that produced it. Internal runs are marked and carry their run details on the model page. One item on this test is 1.0 points, so read gaps smaller than that as noise.

#ModelScoreProduced by
1GPT-6.1 SolOpenAI35.0%Leaderboard
2GPT-6 AstraOpenAI34.2%Leaderboard
3Claude Sonnet 5.5Anthropic31.4%Leaderboard
4Muse WonderMeta30.8%Leaderboard
5GPT-5.6 SolOpenAI30.7%Leaderboard
6Claude Opus 5.5Anthropic30.6%Leaderboard
7Claude Fable 5Anthropic30.0%Leaderboard
8Claude Fable 5.1Anthropic29.6%Leaderboard
9Gemini 4 ArgonGoogle28.4%Leaderboard
10Muse Spark 1.3Meta27.6%Leaderboard
11GPT-6 SolOpenAI26.4%Leaderboard
12GPT-5.5OpenAI26.0%Leaderboard
13GPT-5.6 TerraOpenAI24.7%Leaderboard
14Claude Opus 5Anthropic24.0%Leaderboard
15Claude Opus 4.8Anthropic24.0%Leaderboard
16Gemini 3.7 FlashGoogle23.8%Leaderboard
17Gemini 3.8 FlashGoogle23.4%Leaderboard
18Qwen3.8 MaxAlibaba23.2%Leaderboard
19GPT-6 LunaOpenAI23.0%Leaderboard
20Grok 4.7xAI22.8%Leaderboard
21GPT-5.6 LunaOpenAI22.7%Leaderboard
22Claude Opus 4.7Anthropic21.0%Leaderboard
23DeepSeek V4.1 FlashDeepSeek19.8%Leaderboard
24Kimi K3Moonshot AI19.0%Leaderboard
25Claude Sonnet 4.6Anthropic18.0%Leaderboard
26Grok 4.6xAI17.2%Leaderboard
27Gemini 3.1 ProGoogle17.0%Leaderboard
28Muse Spark 1.2Meta16.0%Leaderboard
29Muse Spark 1.1Meta15.0%Leaderboard
30Gemini 3.5 FlashGoogle14.0%Leaderboard
31Grok 4.5xAI14.0%Leaderboard
32GLM 5.3 FlashZhipu AI14.0%Leaderboard
33Gemini 3.6 FlashGoogle14.0%Leaderboard
34Kimi K2.6Moonshot AI12.0%Leaderboard
35Gemini 3 FlashGoogle10.0%Leaderboard
36Gemini 3.5 Flash-LiteGoogle10.0%Leaderboard
37Grok 4.3xAI8.0%Leaderboard
Theo

The best models, ranked here, working inside Theo.

29 of the models on this page run inside Theo today. Theo picks the right one for each step and always shows which engine answered.