GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Animate rain running down a window over blurred city lights in one self-contained HTML file
OGPT-6.1 Soldefault · thinking off · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.03
- Wall time
- 28s
- 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 ChatGPT subscription, not billed).
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 were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `MEDIA_DIR=<dir> python3 score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 2 (0 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.03
- Wall time
- 28s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking off | 48,873 | 1,069 | $0.03 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking off) via
Codex CLI 0.163.0 (codex exec --json), cloud. - 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
- 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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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a16460-9cf0-7d62-9b45-732a7cc18c83"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll read `spec.json` and create a self-contained canvas animation in `out/scene.html`."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'cat spec.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'cat spec.json'", "aggregated_output": "{\n \"title\": \"Rainy Window 554\",\n \"width\": 700,\n \"height\": 450,\n \"file\": \"out/scene.html\",\n \"min_drops\": 31,\n \"lights\": 14\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/scene.html <<'EOF'\n<\"'!doctype html>\n<html lang=\"en\">\n<head>\n<meta charset=\"utf-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n<title>Rainy Window 554</title>\n<style>\nhtml,body{margin:0;min-height:100%;background:#080e19}body{display:flex;justify-content:center;align-items:flex-start;padding-top:35px;box-sizing:border-box}canvas{display:block;width:700px;height:450px;background:#121e30;border-radius:8px}\n</style>\n</head>\n<body>\n<canvas id=\"scene\" width=\"700\" height=\"450\" aria-label=\"Rain running down a window over blurred city lights\"></canvas>\n<script>\n'\"'use strict';\nconst canvas=document.getElementById('scene');\nconst ctx=canvas.getContext('2d');\nconst W=700,H=450;\nconst lights=Array.from({length:14},(_,i)=>({x:30+i*49,y:210+((i*71)%195),radius:13+(i%4)*5,color:i%3===0?'255,166,83':i%3===1?'113,192,247':'255,221,150'}));\nconst drops=Array.from({length:62},(_,i)=>({x:12+((i*113)%676),y:(i*137)%450,speed:19+(i%9)*2.2,length:13+(i%5)*5,radius:2+(i%3)*0.7}));\nconst background=document.createElement('canvas');background.width=W;background.height=H;\nconst b=background.getContext('2d');\nconst sky=b.createLinearGradient(0,0,0,H);sky.addColorStop(0,'#0b1427');sky.addColorStop(1,'#253442');b.fillStyle=sky;b.fillRect(0,0,W,H);\nfor(let i=0;i<18;i++){b.fillStyle=i%2?'#121e2b':'#182432';b.fillRect(i*43,100+(i*53)%145,47,H);}\nfor(const l of lights){const g=b.createRadialGradient(l.x,l.y,0,l.x,l.y,l.radius*3);g.addColorStop(0,\"'`rgba(${l.color},.95)`);g.addColorStop(.22,`rgba(${l.color},.65)`);g.addColorStop(1,`rgba(${l.color},0)`);b.fillStyle=g;b.fillRect(l.x-l.radius*3,l.y-l.radius*3,l.radius*6,l.radius*6);}\nfunction draw(){ctx.drawImage(background,0,0);ctx.fillStyle='\"'rgba(130,170,190,.055)';ctx.fillRect(0,0,W,H);\nfor(const d of drops){ctx.lineCap='round';ctx.lineWidth=d.radius*2;ctx.strokeStyle='rgba(6,22,36,.7)';ctx.beginPath();ctx.moveTo(d.x,d.y-d.length);ctx.lineTo(d.x,d.y);ctx.stroke();ctx.lineWidth=1.25;ctx.strokeStyle='rgba(194,226,242,.66)';ctx.beginPath();ctx.moveTo(d.x-1,d.y-d.length);ctx.lineTo(d.x-1,d.y-2);ctx.stroke();ctx.beginPath();ctx.fillStyle='rgba(190,222,239,.75)';ctx.ellipse(d.x,d.y,d.radius,d.radius*1.5,0,0,Math.PI*2);ctx.fill();}\n}\nwindow.sceneState=()=>({drops:drops.map(d=>({x:d.x,y:d.y})),lights:lights.map(l=>({x:l.x,y:l.y}))});\nlet last=performance.now();\nfunction animate(now){const dt=Math.min((now-last)/1000,.1);last=now;for(const d of drops){d.y+=d.speed*dt;if(d.y>H+d.length)d.y=-2;}draw();requestAnimationFrame(animate);}\ndraw();requestAnimationFrame(animate);\n</script>\n</body>\n</html>\nEOF\nwc -c out/scene.html\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/scene.html <<'EOF'\n<\"'!doctype html>\n<html lang=\"en\">\n<head>\n<meta charset=\"utf-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n<title>Rainy Window 554</title>\n<style>\nhtml,body{margin:0;min-height:100%;background:#080e19}body{display:flex;justify-content:center;align-items:flex-start;padding-top:35px;box-sizing:border-box}canvas{display:block;width:700px;height:450px;background:#121e30;border-radius:8px}\n</style>\n</head>\n<body>\n<canvas id=\"scene\" width=\"700\" height=\"450\" aria-label=\"Rain running down a window over blurred city lights\"></canvas>\n<script>\n'\"'use strict';\nconst canvas=document.getElementById('scene');\nconst ctx=canvas.getContext('2d');\nconst W=700,H=450;\nconst lights=Array.from({length:14},(_,i)=>({x:30+i*49,y:210+((i*71)%195),radius:13+(i%4)*5,color:i%3===0?'255,166,83':i%3===1?'113,192,247':'255,221,150'}));\nconst drops=Array.from({length:62},(_,i)=>({x:12+((i*113)%676),y:(i*137)%450,speed:19+(i%9)*2.2,length:13+(i%5)*5,radius:2+(i%3)*0.7}));\nconst background=document.createElement('canvas');background.width=W;background.height=H;\nconst b=background.getContext('2d');\nconst sky=b.createLinearGradient(0,0,0,H);sky.addColorStop(0,'#0b1427');sky.addColorStop(1,'#253442');b.fillStyle=sky;b.fillRect(0,0,W,H);\nfor(let i=0;i<18;i++){b.fillStyle=i%2?'#121e2b':'#182432';b.fillRect(i*43,100+(i*53)%145,47,H);}\nfor(const l of lights){const g=b.createRadialGradient(l.x,l.y,0,l.x,l.y,l.radius*3);g.addColorStop(0,\"'`rgba(${l.color},.95)`);g.addColorStop(.22,`rgba(${l.color},.65)`);g.addColorStop(1,`rgba(${l.color},0)`);b.fillStyle=g;b.fillRect(l.x-l.radius*3,l.y-l.radius*3,l.radius*6,l.radius*6);}\nfunction draw(){ctx.drawImage(background,0,0);ctx.fillStyle='\"'rgba(130,170,190,.055)';ctx.fillRect(0,0,W,H);\nfor(const d of drops){ctx.lineCap='round';ctx.lineWidth=d.radius*2;ctx.strokeStyle='rgba(6,22,36,.7)';ctx.beginPath();ctx.moveTo(d.x,d.y-d.length);ctx.lineTo(d.x,d.y);ctx.stroke();ctx.lineWidth=1.25;ctx.strokeStyle='rgba(194,226,242,.66)';ctx.beginPath();ctx.moveTo(d.x-1,d.y-d.length);ctx.lineTo(d.x-1,d.y-2);ctx.stroke();ctx.beginPath();ctx.fillStyle='rgba(190,222,239,.75)';ctx.ellipse(d.x,d.y,d.radius,d.radius*1.5,0,0,Math.PI*2);ctx.fill();}\n}\nwindow.sceneState=()=>({drops:drops.map(d=>({x:d.x,y:d.y})),lights:lights.map(l=>({x:l.x,y:l.y}))});\nlet last=performance.now();\nfunction animate(now){const dt=Math.min((now-last)/1000,.1);last=now;for(const d of drops){d.y+=d.speed*dt;if(d.y>H+d.length)d.y=-2;}draw();requestAnimationFrame(animate);}\ndraw();requestAnimationFrame(animate);\n</script>\n</body>\n</html>\nEOF\nwc -c out/scene.html\"", "aggregated_output": "2550 out/scene.html\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "agent_message", "text": "Created [out/scene.html](out/scene.html) with a 700\u00d7450 canvas, 62 animated raindrops, 14 distinct city lights, and `window.sceneState()`. All assets are inline for offline use."}}
{"type": "turn.completed", "usage": {"input_tokens": 48873, "cached_input_tokens": 40320, "cache_write_input_tokens": 0, "output_tokens": 1069, "reasoning_output_tokens": 0}}