GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Lighthouse with a sweeping beam in one self-contained HTML file

OGPT-6.1 Soldefault · thinking off · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.05
Wall time: 54s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Lighthouse with a sweeping beam in one self-contained HTML file Make a night-time lighthouse with a sweeping beam over the sea. Style, colours and technique (CSS, SVG, canvas) are up to you. **Looks are not scored**; only the objective checks below are. Write one file, `lighthouse.html`, in the working directory. ## Checks (all must pass; partial credit = fraction passed) 1. `file_exists_under_size_limit`: `lighthouse.html` exists and is at most 100,000 bytes (100 KB). 2. `no_external_requests`: fully self-contained. No `http://`, `https://` or `//` URLs in `src`, `href`, `srcset`, `poster`, `action`, `data` or `url()`, no asset URLs anywhere, no `@import`, `fetch(`, `XMLHttpRequest`, `WebSocket`, `EventSource`, `sendBeacon`, JS `import`, `<script src>` or `<link href>` (inline everything; `data:` URIs are fine). When a browser is used, any non-file request the page attempts also fails this check. 3. `animation_present`: CSS `@keyframes` plus an `animation`/`animation-name` property, or `requestAnimationFrame(` / `setInterval(`. 4. `loops_forever`: an `infinite` CSS animation (`animation-iteration-count: infinite` or `infinite` in the `animation` shorthand), or rAF/`setInterval` with no `cancelAnimationFrame`/`clearInterval` anywhere; when a browser is used, the page must also still change between ~6 s and ~7 s after load. 5. `page_animates`: when a headless Chrome/Chromium is available, the page is opened once with the network blocked at 800x600, and screenshots at ~1.5 s and ~2.5 s after load must differ. Without a browser (or if it fails to start) this and the browser part of check 4 fall back to the static checks 3-4; the scorer reports `check_mode` (`headless-browser` or `static-fallback`). 6. `required_id_and_title`: an element with `id="lighthouse"` and a `<title>` containing "Lighthouse" (case-insensitive). No other files, no build step, no network. Run time limit for scoring: 120 s.

Objective only: self-containment under a network block, a real looping animation verified by screenshots in one page session, size limit, and a required id/title. Aesthetics are deliberately unscored (public poll judges looks). 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: 1. file_exists_under_size_limit 2. no_external_requests 3. animation_present 4. loops_forever 5. page_animates 6. required_id_and_title

Setup notes: Batch 11 case l06-lighthouse-beam (offline, open-ended creative brief). Prepared with batch11/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 case scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. A global user-level AGENTS.md was present on the agent machine and may have been in context. Scorer ran in headless-browser mode (Chrome over a CDP pipe, network blocked). The served model id is not echoed in the event log. Output rendered offline with batch11/render.py (network blocked): PNG 1280x800 at ~3 s after load; MP4 1280x720 H.264 ~6 s. 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 3 (1 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Metrics

Estimated cost: $0.05
Wall time: 54s
Human step-ins: 0
Turns: 1

Model usage
Model	Input tok	Output tok	Cost
OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking off	65,929	2,374	$0.05

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: 1. file_exists_under_size_limit 2. no_external_requests 3. animation_present 4. loops_forever 5. page_animates 6. required_id_and_title
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": "01a1580f-14cc-7823-af47-f3ef38ffac5d"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll create a self-contained HTML file with a lighthouse and a continuously sweeping beam, then check it against the requirements."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'lighthouse.html' -g 'package.json' .\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'lighthouse.html' -g 'package.json' .\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/l06/work\n", "exit_code": 1, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/l06/work/lighthouse.html", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/l06/work/lighthouse.html", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nfrom pathlib import Path\nimport re\np=Path('lighthouse.html'); s=p.read_text()\nassert p.stat().st_size <= 100000\nassert 'id=\\\"lighthouse\\\"' in s\nassert re.search(r'<title>[\"'^<]*Lighthouse'\"',s,re.I)\nassert '@keyframes' in s and 'animation:' in s and 'infinite' in s\nassert not re.search(r'https?://|@import|fetch\\\\(|XMLHttpRequest|WebSocket|EventSource|sendBeacon|<script\\\\s+src|<link\\\\s+href',s,re.I)\nprint(f'Checks passed; {p.stat().st_size} bytes. Continuous CSS animation present.')\nPY\ncommand -v chromium || command -v chromium-browser || command -v google-chrome || true\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nfrom pathlib import Path\nimport re\np=Path('lighthouse.html'); s=p.read_text()\nassert p.stat().st_size <= 100000\nassert 'id=\\\"lighthouse\\\"' in s\nassert re.search(r'<title>[\"'^<]*Lighthouse'\"',s,re.I)\nassert '@keyframes' in s and 'animation:' in s and 'infinite' in s\nassert not re.search(r'https?://|@import|fetch\\\\(|XMLHttpRequest|WebSocket|EventSource|sendBeacon|<script\\\\s+src|<link\\\\s+href',s,re.I)\nprint(f'Checks passed; {p.stat().st_size} bytes. Continuous CSS animation present.')\nPY\ncommand -v chromium || command -v chromium-browser || command -v google-chrome || true\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n/usr/bin/google-chrome\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nfrom pathlib import Path\nimport re\np=Path('lighthouse.html');s=p.read_text()\nassert p.stat().st_size<100000\nassert 'id=\\\"lighthouse\\\"' in s and '<title>Lighthouse' in s\nassert '@keyframes' in s and 'animation:' in s and 'infinite' in s\nassert not re.search(r'https?://|@import|fetch\\\\(|XMLHttpRequest|WebSocket|EventSource|sendBeacon|<script\\\\s+src|<link\\\\s+href',s,re.I)\nprint('Static checks passed:',p.stat().st_size,'bytes')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nfrom pathlib import Path\nimport re\np=Path('lighthouse.html');s=p.read_text()\nassert p.stat().st_size<100000\nassert 'id=\\\"lighthouse\\\"' in s and '<title>Lighthouse' in s\nassert '@keyframes' in s and 'animation:' in s and 'infinite' in s\nassert not re.search(r'https?://|@import|fetch\\\\(|XMLHttpRequest|WebSocket|EventSource|sendBeacon|<script\\\\s+src|<link\\\\s+href',s,re.I)\nprint('Static checks passed:',p.stat().st_size,'bytes')\nPY\"", "aggregated_output": "Static checks passed: 4339 bytes\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "Created [lighthouse.html](lighthouse.html) with an endlessly sweeping beam over the sea.\n\nSelf-contained, 4,339 bytes. Static checks passed; browser animation was not tested."}}
{"type": "turn.completed", "usage": {"input_tokens": 65929, "cached_input_tokens": 57216, "cache_write_input_tokens": 0, "output_tokens": 2374, "reasoning_output_tokens": 0}}