ununPaper

Which AI writes the best decks?

Text models only, ranked on how well they follow the unPaper prompt and how good the resulting deck becomes.

Edition 2026-07-15 · 6 makers · 16 runs

The podium

1

claude-opus-4.8

Anthropic

4.8/ 5 overall

Best at storyline

2

gpt-5.2

OpenAI

4.8/ 5 overall

Best at titles tell the story

3

grok-4.5

xAI

4.6/ 5 overall

Best at conformance

Ranked models

All tested models

Scores are means across every filed run. Bad runs count.

Text models ranked across eight unPaper deck-quality dimensions
RankModelSelf-ID
1

claude-opus-4.8· api

Anthropic · reasoning off · 2 runs

4.8
5.05.05.04.54.05.05.04.5100%8.00/2
2

gpt-5.2· api

OpenAI · reasoning on · 2 runs

4.8
4.55.04.54.55.05.05.04.5100%9.02/2✓
3

grok-4.5· api

xAI · reasoning on · 2 runs

4.6
4.54.54.54.55.05.05.04.0100%8.00/2
4

deepseek-v4-pro· api

DeepSeek · reasoning not recorded · 2 runs

4.1
4.04.53.54.03.05.04.54.0100%7.00/2
5

gemini-3.1-pro-preview· api

Google · reasoning on · 2 runs

4.1
3.55.03.54.04.05.04.04.0100%7.00/2
6

gpt-5-mini· api

OpenAI · reasoning on · 2 runs

4.1
4.04.54.03.03.05.05.04.0100%9.00/2
7

mistral-large-2512· api

Mistral AI · reasoning off · 2 runs

3.9
3.04.03.54.04.05.05.03.0100%10.00/2
8

deepseek-v4-flash· api

DeepSeek · reasoning off · 2 runs

3.6
3.53.54.03.02.05.04.53.5100%7.00/2

Dimension leaders

Story · claude-opus-4.8Titles · claude-opus-4.8Pattern · claude-opus-4.8Visual · claude-opus-4.8Confm · gpt-5.2Convert · claude-opus-4.8Native · claude-opus-4.8Source · claude-opus-4.8

The models under test

DeepSeek

DeepSeek

4.1 / 5 · deepseek-v4-pro

Mistral

Mistral AI

3.9 / 5 · mistral-large-2512

Gemini

Google

4.1 / 5 · gemini-3.1-pro-preview

Grok

xAI

4.6 / 5 · grok-4.5

ChatGPT

OpenAI

4.8 / 5 · gpt-5.2

Claude

Anthropic

4.8 / 5 · claude-opus-4.8

How the score is built

Each text model gets the real unPaper prompt and the same brief. The saved HTML and converted PowerPoint are scored 1 to 5 on eight fixed dimensions. Overall is the mean. Conformance is auto-scored; the rest are hand-scored against published anchors. The sample is deliberately small, so runs are shown and no confidence intervals are invented.

Self-ID counts the runs where the model, asked to stamp a machine-readable note naming its generator, named itself plausibly — hover the cell for what each model actually called itself. Runs driven over an API carry no system prompt telling the model who it is, so this column measures self-knowledge, not deception.

Variety counts how many distinct exhibit families a deck uses — charts with editable data, real tables, stat rows, matrices, flows, timelines, scorecards, quotes, dividers, vector graphics, layout grids and the signature consulting exhibits — detected mechanically, out of 14. It measures compositional range, not correctness; the Pattern-fit score judges whether the choices were right.

Every row ships its receipts: expand a model to open the exact deck it produced — untouched — and the converted PowerPoint. The full prompts the models received: 2026-07-15-exec-macro-briefing · 2026-07-15-market-entry.

StorylineStory
Answer first, grouped support, a closing ask — not a content dump.
Titles tell the storyTitles
Read only the titles: full-sentence findings, not labels.
Pattern fitPattern
The right element per idea — table for a comparison, chart for a trend.
Visual disciplineVisual
One accent, visual anchors, a signature moment, nothing crowded.
ConformanceConfm
Verbatim style/@page/script, look untouched, notes on every slide, complete file. Auto-checked.
ConversionConvert
Share of the deck that converts to native PowerPoint objects, not images.
Native editabilityNative
Everything movable, charts data-editable, theme on-brand in PowerPoint.
Source disciplineSource
Numbers correct and sourced, no hallucinated precision.
Read the scoring protocol

Generated 2026-07-15 · 16 runs · bad runs count too