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Gemini 4 Argon (effort medium · thinking on) on Animate rain running down a window over blurred city lights in one self-contained HTML file

GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.08
Wall time
1m
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Animate rain running down a window over blurred city lights in one self-contained HTML file Make a small animated scene: a rainy window. Write one file, `out/scene.html`, that works opened straight from disk with no network access. `spec.json` gives the page title, the stage size and the scene parameters. **Looks are not scored**: the page is opened in a headless browser (800x600 window, UTC time zone, network blocked) and checked by objective tests only. ## Scene contract - Draw the scene on one `<canvas>` or `<svg>` stage of `width` x `height` CSS pixels (spec.json), visible on load, with `<title>` containing spec.json `title`. - Animate continuously from load (`requestAnimationFrame` or timers); no click is needed unless stated below. - Expose `window.sceneState()`, returning the scene's current state in stage pixel coordinates (origin top-left of the stage): `{drops: [{x, y}, ...], lights: [{x, y}, ...]}`. Extra fields are fine. It must describe what is actually drawn. - Drops may restart at the top; keep each drop at the same array index. - The page must log no errors and throw no exceptions. Keep the file at most 200,000 bytes; no external scripts, fonts, images or URLs (inline everything). ## Checks (partial credit = share passed) 1. `self_contained`: `out/scene.html` exists, is at most 200,000 bytes, references no http(s) or protocol-relative URL, and requests nothing from the network when opened. 2. `has_canvas_or_svg`: a visible `<canvas>` or `<svg>` of at least 300x200 CSS pixels is on the page. 3. `frames_change`: over 3 s, some later screenshot differs from the first in at least 0.1% of pixels, and at least 3 of 30 screenshots differ. 4. `rain_runs_down`: `sceneState().drops` has at least `min_drops` entries and at least 60% of them move down (y increases by more than 1 px) between samples 1.5 s apart. 5. `city_lights`: `sceneState().lights` has exactly spec.json `lights` entries, all at distinct positions inside the stage. 6. `no_console_errors`: no uncaught exception, `console.error` or browser error is logged during the run. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a Google account via Antigravity CLI, not billed per token).

Success criteria: self_contained has_canvas_or_svg frames_change rain_runs_down city_lights no_console_errors

Setup notes: Use Case Scout batch28 case d05-rain-on-window-city-lights (offline fixture). Prepared with batch28/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `MEDIA_DIR=<dir> python3 score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Gallery media (3 s headless-Chrome capture: poster.png + clip.mp4, network blocked) written to a fresh MEDIA_DIR by the scorer; SHA-256 in evidence.media. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 3 (0 failed). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Metrics

Estimated cost
$0.08
Wall time
1m
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on42,5724,615$0.08

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via Antigravity CLI 1.4.5 (agy -p), cloud.
  3. It counts as a success only if it meets every success criterion: self_contained has_canvas_or_svg frames_change rain_runs_down city_lights no_console_errors
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Evidence log
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