
Claude Fable 5
via claude-code-cli 2.1.214model + harness
Run of 18 Jul 2026
2 format checks passed — no check of the result
2 render and interaction checks not run
Evaluation not published
Archived pilot · Historical run
built by GPT-5.6 Luna via codex-cli 0.144.5 model + harness
Rendered by agi.jetzt from the model-produced artifactRendered offline in an isolated renderer from the stored file (sha256 c791e011908a…). The file itself is not served.
Scroll sideways. Each run had its own runtime and limits — compare the artifacts, not a score.

via claude-code-cli 2.1.214model + harness
Run of 18 Jul 2026
2 format checks passed — no check of the result
2 render and interaction checks not run
Evaluation not published

via ollama 0.32.15model only
Run of 1 Oct 2026token limit 8192
1 of 2 format checks passed — no check of the result
2 without a recorded result
Evaluation not published

via ollama 0.32.15model only
Run of 30 Aug 2026token limit 16000
1 of 2 format checks passed — no check of the result
2 render and interaction checks not run
Evaluation not published

via ollama 0.32.15model only
Run of 3 Sept 2026
2 format checks passed — no check of the result
2 render and interaction checks not run
Evaluation not published
Automated checks of the stored HTML file, without running it in a browser. Details are the runner’s German originals. Format and presence checks say nothing about whether the result fulfils the brief.
| Check | Kind | Result |
|---|---|---|
| single_html5_filegenau eine HTML5-Datei | Format | Passed |
| viewport_metaViewport-Meta: 1 (erwartet 1) | Format | Passed |
| no_external_requestsSandbox not available — rendering skipped | Rendering | not run |
| renders_without_console_errorsSandbox not available — rendering skipped | Rendering | not run |
2 render and interaction checks were not run. Recorded reason: “Sandbox not available — rendering skipped”.
WhyThe v2 judge record of this run names the tested model as its own judge and lists no criteria. Its score is therefore not shown — nor the combined v2 score, which blends that judge in.
Baue als EINE einzelne HTML-Datei (kein Framework, kein externes Asset, keine externen Fonts) einen bildschirmfüllenden, animierten Hintergrund im Stil Biolumineszenz in der Tiefe: tiefes Petrol bis Schwarz, darüber schwebende, leuchtende Schlieren in Türkis, Cyan und einem kalten Magenta, die sich träge wie Quallen bewegen. Geheimnisvoll, ruhig, kühl. Helle, klare Wortmarke. für die Startseite eines Tiefsee-Forschungsprojekts.
Anforderungen:
- Ein atmosphärischer, animierter Verlauf (Aurora / Mesh-Gradient / fließende Farbwolken), der den gesamten Viewport füllt und sich langsam, organisch bewegt — via CSS-Animation oder Canvas, selbst erzeugt.
- Ruhige, hochwertige Bewegung mit weichem Easing; die Farben durchmischen sich stimmig, kein hartes Flackern, keine sichtbaren Kanten oder Banding-Streifen.
- Darüber eine knappe, zentrierte Bildmarke: „Abyssal" als elegante Wortmarke plus eine kurze, ruhige Zeile darunter — gut lesbar über dem bewegten Grund.
- `@media (prefers-reduced-motion: reduce)`: Bewegung anhalten, ein ebenso schöner statischer Verlauf bleibt.
- Bildschirmfüllend ohne Scrollbalken, kein horizontaler Overflow.From the task seed (German original); the actual prompts add output-format instructions and other wording.
Model The runner’s model identifier maps to GPT-5.6 Luna in the agi.jetzt register (verified mapping).
Not an AGI measureThis run shows one thing: what a model built as a single HTML file from a precise brief. It says nothing about general intelligence.Checked automatically in this run: 2 format checks passed — no check of the result · 2 render and interaction checks not run.
4 of 15 verified v2 runs of this task are published. 4 of the 6 models with recorded runs have a published run.Not published: Claude Haiku 4.5 (3 runs; 0 of 2 format checks passed); GPT-5.6 Sol (3 runs; 0 of 2 format checks passed).In addition, 1 published run has no selection record (not part of the v2 import).Source: the selection records of the published runs. They count the task across its variants and know only models with at least one published run.