GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Water-quality sensor anomaly flags and daily means
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.06
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Water-quality sensor anomaly flags `input.json` (synthetic buoy data) names `readings.csv` (`ts` "YYYY-MM-DDTHH:MM", `sensor`, `value`; rows are shuffled) and gives `stuck_n`, `window`, `spike_k`, and per-sensor `config` (`min`, `max`, `spike_floor`, `max_step`). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `chart.svg`. Fresh inputs are used too. ## Rules (per sensor, rows in ascending ts) 1. **missing**: value is blank or not a finite number (e.g. `err`, `nan`). It is skipped entirely (it does not break or extend a stuck run). 2. For other values, track the run of consecutive non-missing readings with exactly the same value (all flags included). Then the first match wins: - **range**: value < min or > max. - **stuck**: the current run length >= `stuck_n`. - **spike**: at least `window` ok values exist; with the last `window` ok values, med = median and MAD = median of |x - med|; flag if |v - med| > max(spike_k * MAD, spike_floor). - **rate**: |v - last ok value| > max_step. - else **ok**. Only ok values are used as "ok values", in daily means and in the chart. 3. `daily_means[sensor][date]` = mean of ok values that day (3 decimals); `counts[sensor]` has keys ok, missing, range, stuck, spike, rate; `flags` lists every non-ok reading `{"ts", "sensor", "flag"}` sorted by ts then sensor. ## Visual output: chart.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="series-<sensor>">` for each sensor (`series-temp_c`, `series-do_mgl`, `series-ph`) and an element `id="flag-summary"`. The drawing itself is not graded. ## answer.json `{"counts": {...}, "daily_means": {...}, "flags": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `missing_and_range`, `stuck_detection`, `spike_and_rate` (with ok counts), `flag_list`, `daily_means` (within 0.0011), `chart_svg_static` (chart.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Shuffled rows, a stuck run that ignores missing values but includes flagged ones, a robust median/MAD spike test with a floor that only uses ok history, a rate test against the last ok value, and flag precedence. 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).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2
Setup notes: Use Case Scout batch10 case k12-water-quality-anomalies (offline fixture). Prepared with batch10/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: `python 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. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. 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: 8/8. 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 8 (2 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.06
- Wall time
- 1m
- 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 on | 105,715 | 2,603 | $0.06 |
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 on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2
- 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": "01a16402-9406-7e33-aa44-74125b68c1ae"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll check the input files, implement the flagging rules, and verify the JSON and SVG outputs."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "total 28\ndrwxr-xr-x 6 bob bob 4096 Oct 22 10:27 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:27 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:27 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:27 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:27 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:27 .git\n-rw-r--r-- 1 bob bob 2101 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 353 Oct 22 10:08 input.json\n-rw-r--r-- 1 bob bob 12097 Oct 22 10:08 readings.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"rg --files -g 'AGENTS.md' -g 'input.json' -g '*.csv'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"rg --files -g 'AGENTS.md' -g 'input.json' -g '*.csv'\"", "aggregated_output": "readings.csv\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "{\n \"readings\": \"readings.csv\",\n \"stuck_n\": 4,\n \"window\": 6,\n \"spike_k\": 5,\n \"config\": {\n \"temp_c\": {\n \"min\": -2,\n \"max\": 35,\n \"spike_floor\": 2.0,\n \"max_step\": 1.0\n },\n \"do_mgl\": {\n \"min\": 0,\n \"max\": 20,\n \"spike_floor\": 2.0,\n \"max_step\": 1.0\n },\n \"ph\": {\n \"min\": 0,\n \"max\": 14,\n \"spike_floor\": 0.4,\n \"max_step\": 0.25\n }\n }\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'head -n 6 readings.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'head -n 6 readings.csv'", "aggregated_output": "ts,sensor,value\n2026-07-01T09:30,temp_c,19.01\n2026-07-02T14:30,do_mgl,7.43\n2026-07-03T16:00,do_mgl,6.39\n2026-07-01T18:30,do_mgl,6.59\n2026-07-02T03:30,ph,7.94\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/119_k12/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/119_k12/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc 'python3 solve.py input.json answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc 'python3 solve.py input.json answer.json'", "aggregated_output": "", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import classify\ns={'stuck_n':3,'window':3,'spike_k':3,'config':{'x':{'min':0,'max':100,'spike_floor':5,'max_step':2}}}\nvalues=['10','','10','nan','10','200','11','14','30','12','13','30']\nrows=[{'ts':f'2026-01-01T00:{i:02d}','sensor':'x','value':v} for i,v in enumerate(values)]\nr,_=classify(s,reversed(rows))\nassert [x['flag'] for x in r['flags']]==['missing','missing','stuck','range','rate','rate','spike'],r\nassert r['counts']['x']['ok']==5,r\nroot=ET.parse('chart.svg').getroot()\nns={'s':'http://www.w3.org/2000/svg'}\nfor sensor in ('temp_c','do_mgl','ph'):\n assert root.find(f\\\"s:polyline[@id='series-{sensor}']\\\",ns) is not None\nassert root.find(\\\"s:text[@id='flag-summary']\\\",ns) is not None\nassert all(e.tag.split('}')[-1] not in ('script','image','a') for e in root.iter())\nprint(json.load(open('answer.json'))['counts'])\nprint('Synthetic precedence, missing/stuck continuity, ok history, and SVG checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import classify\ns={'stuck_n':3,'window':3,'spike_k':3,'config':{'x':{'min':0,'max':100,'spike_floor':5,'max_step':2}}}\nvalues=['10','','10','nan','10','200','11','14','30','12','13','30']\nrows=[{'ts':f'2026-01-01T00:{i:02d}','sensor':'x','value':v} for i,v in enumerate(values)]\nr,_=classify(s,reversed(rows))\nassert [x['flag'] for x in r['flags']]==['missing','missing','stuck','range','rate','rate','spike'],r\nassert r['counts']['x']['ok']==5,r\nroot=ET.parse('chart.svg').getroot()\nns={'s':'http://www.w3.org/2000/svg'}\nfor sensor in ('temp_c','do_mgl','ph'):\n assert root.find(f\\\"s:polyline[@id='series-{sensor}']\\\",ns) is not None\nassert root.find(\\\"s:text[@id='flag-summary']\\\",ns) is not None\nassert all(e.tag.split('}')[-1] not in ('script','image','a') for e in root.iter())\nprint(json.load(open('answer.json'))['counts'])\nprint('Synthetic precedence, missing/stuck continuity, ok history, and SVG checks passed.')\nPY\"", "aggregated_output": "Traceback (most recent call last):\n File \"<stdin>\", line 8, in <module>\nAssertionError: {'counts': {'x': {'ok': 5, 'missing': 2, 'range': 1, 'stuck': 1, 'spike': 2, 'rate': 1}}, 'daily_means': {'x': {'2026-01-01': 11.2}}, 'flags': [{'ts': '2026-01-01T00:01', 'sensor': 'x', 'flag': 'missing'}, {'ts': '2026-01-01T00:03', 'sensor': 'x', 'flag': 'missing'}, {'ts': '2026-01-01T00:04', 'sensor': 'x', 'flag': 'stuck'}, {'ts': '2026-01-01T00:05', 'sensor': 'x', 'flag': 'range'}, {'ts': '2026-01-01T00:07', 'sensor': 'x', 'flag': 'rate'}, {'ts': '2026-01-01T00:08', 'sensor': 'x', 'flag': 'spike'}, {'ts': '2026-01-01T00:11', 'sensor': 'x', 'flag': 'spike'}]}\n", "exit_code": 1, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_9", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import classify\ns={'stuck_n':3,'window':3,'spike_k':3,'config':{'x':{'min':0,'max':100,'spike_floor':5,'max_step':2}}}\nvalues=['10','','10','nan','10','200','11','14','30','12','13','30']\nrows=[{'ts':f'2026-01-01T00:{i:02d}','sensor':'x','value':v} for i,v in enumerate(values)]\nr,_=classify(s,reversed(rows))\nassert [x['flag'] for x in r['flags']]==['missing','missing','stuck','range','rate','spike','spike']\nassert r['counts']['x']['ok']==5\nassert r['daily_means']['x']['2026-01-01']==11.2\nroot=ET.parse('chart.svg').getroot()\nns={'s':'http://www.w3.org/2000/svg'}\nfor sensor in ('temp_c','do_mgl','ph'):\n assert root.find(f\\\"s:polyline[@id='series-{sensor}']\\\",ns) is not None\nassert root.find(\\\"s:text[@id='flag-summary']\\\",ns) is not None\nassert all(e.tag.split('}')[-1] not in ('script','image','a') for e in root.iter())\nprint(json.load(open('answer.json'))['counts'])\nprint('Synthetic classification and SVG checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_9", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import classify\ns={'stuck_n':3,'window':3,'spike_k':3,'config':{'x':{'min':0,'max':100,'spike_floor':5,'max_step':2}}}\nvalues=['10','','10','nan','10','200','11','14','30','12','13','30']\nrows=[{'ts':f'2026-01-01T00:{i:02d}','sensor':'x','value':v} for i,v in enumerate(values)]\nr,_=classify(s,reversed(rows))\nassert [x['flag'] for x in r['flags']]==['missing','missing','stuck','range','rate','spike','spike']\nassert r['counts']['x']['ok']==5\nassert r['daily_means']['x']['2026-01-01']==11.2\nroot=ET.parse('chart.svg').getroot()\nns={'s':'http://www.w3.org/2000/svg'}\nfor sensor in ('temp_c','do_mgl','ph'):\n assert root.find(f\\\"s:polyline[@id='series-{sensor}']\\\",ns) is not None\nassert root.find(\\\"s:text[@id='flag-summary']\\\",ns) is not None\nassert all(e.tag.split('}')[-1] not in ('script','image','a') for e in root.iter())\nprint(json.load(open('answer.json'))['counts'])\nprint('Synthetic classification and SVG checks passed.')\nPY\"", "aggregated_output": "{'temp_c': {'ok': 134, 'missing': 3, 'range': 1, 'stuck': 2, 'spike': 4, 'rate': 0}, 'do_mgl': {'ok': 131, 'missing': 3, 'range': 1, 'stuck': 3, 'spike': 3, 'rate': 3}, 'ph': {'ok': 135, 'missing': 2, 'range': 1, 'stuck': 3, 'spike': 2, 'rate': 1}}\nSynthetic classification and SVG checks passed.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_10", "type": "agent_message", "text": "Implemented [solve.py](solve.py) and generated `answer.json` and `chart.svg`.\n\nVerified flag precedence, missing/stuck continuity, ok-only history, daily means, and required SVG elements.\n\nRun with:\n```bash\npython3 solve.py input.json answer.json\n```\n\nThis environment has `python3`; `python` is unavailable."}}
{"type": "turn.completed", "usage": {"input_tokens": 105715, "cached_input_tokens": 92928, "cache_write_input_tokens": 0, "output_tokens": 2603, "reasoning_output_tokens": 9}}