claude-opus-4.8
Anthropic
Best at storyline
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
claude-opus-4.8
Anthropic
Best at storyline
gpt-5.2
OpenAI
Best at titles tell the story
grok-4.5
xAI
Best at conformance
All tested models
Scores are means across every filed run. Bad runs count.
| Rank | Model | Self-ID | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | claude-opus-4.8· api Anthropic · reasoning off · 2 runs | 4.8 | 5.0 | 5.0 | 5.0 | 4.5 | 4.0 | 5.0 | 5.0 | 4.5 | 100% | 8.0 | 0/2 |
2 | gpt-5.2· api OpenAI · reasoning on · 2 runs | 4.8 | 4.5 | 5.0 | 4.5 | 4.5 | 5.0 | 5.0 | 5.0 | 4.5 | 100% | 9.0 | 2/2✓ |
3 | grok-4.5· api xAI · reasoning on · 2 runs | 4.6 | 4.5 | 4.5 | 4.5 | 4.5 | 5.0 | 5.0 | 5.0 | 4.0 | 100% | 8.0 | 0/2 |
4 | deepseek-v4-pro· api DeepSeek · reasoning not recorded · 2 runs | 4.1 | 4.0 | 4.5 | 3.5 | 4.0 | 3.0 | 5.0 | 4.5 | 4.0 | 100% | 7.0 | 0/2 |
5 | gemini-3.1-pro-preview· api Google · reasoning on · 2 runs | 4.1 | 3.5 | 5.0 | 3.5 | 4.0 | 4.0 | 5.0 | 4.0 | 4.0 | 100% | 7.0 | 0/2 |
6 | gpt-5-mini· api OpenAI · reasoning on · 2 runs | 4.1 | 4.0 | 4.5 | 4.0 | 3.0 | 3.0 | 5.0 | 5.0 | 4.0 | 100% | 9.0 | 0/2 |
7 | mistral-large-2512· api Mistral AI · reasoning off · 2 runs | 3.9 | 3.0 | 4.0 | 3.5 | 4.0 | 4.0 | 5.0 | 5.0 | 3.0 | 100% | 10.0 | 0/2 |
8 | deepseek-v4-flash· api DeepSeek · reasoning off · 2 runs | 3.6 | 3.5 | 3.5 | 4.0 | 3.0 | 2.0 | 5.0 | 4.5 | 3.5 | 100% | 7.0 | 0/2 |
Dimension leaders
The models under test
DeepSeek
DeepSeek
4.1 / 5 · deepseek-v4-pro
Mistral
Mistral AI
3.9 / 5 · mistral-large-2512
Gemini
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.
Generated 2026-07-15 · 16 runs · bad runs count too