GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Turn a minute-by-minute common-free-time scan into a sweep that handles 3,000 calendars in <3 s with identical output

OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.06
Wall time: 55s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: make common_free fast `slots.py:common_free` is correct but far too slow for real calendars (it scans every minute). Rewrite it so that it returns exactly the same result (same list of tuples, same order) for every input, but runs fast: the grader times a call with 3,000 people x 60 busy intervals each over a horizon of 50,000,000 minutes and requires < 3 s wall-clock, and compares your output with the original on many random and edge-case inputs (touching/overlapping/empty intervals, intervals outside the window, min_len boundaries, start == end). Keep the function name and signature. Standard library only. No network.

The rewrite is short, but exact equality on edge cases (empty/inverted intervals, clipping, min_len=0, start==end, touching intervals) is where sloppy sweeps fail. 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. output_identical: output equals the original implementation on 11 edge cases and 400 seeded random inputs. 2. fast_enough: one call with 3,000 people x 60 intervals over a 50,000,000-minute horizon takes < 3.0 s (subprocess capped at 30 s; timeout = fail).

Setup notes: Use Case Scout batch5 case e06-perf-common-free-slots (offline fixture). Prepared with batch5/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. 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: 2/2 checks. 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 5 (1 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.06
Wall time: 55s
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 on	94,689	1,678	$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. output_identical: output equals the original implementation on 11 edge cases and 400 seeded random inputs. 2. fast_enough: one call with 3,000 people x 60 intervals over a 50,000,000-minute horizon takes < 3.0 s (subprocess capped at 30 s; timeout = fail).
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": "01a16430-922c-7660-b7fd-bf13c93bb698"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect `common_free` and replace the minute scan with an interval-based algorithm, then check it against the original and benchmark the large case.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g '*slots*' -g '*test*'\"", "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 '*slots*' -g '*test*'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/164_e06/work\nslots.py\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat slots.py'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat slots.py'", "aggregated_output": "\"\"\"Find meeting slots when everybody is free. Times are integer minutes; busy intervals are half-open [start, end).\"\"\"\n\ndef common_free(calendars, start, end, min_len=1):\n    \"\"\"calendars: list (one per person) of lists of (s, e) busy intervals, unsorted, may overlap,\n    may extend outside [start, end), may be empty (s >= e: ignored).\n    Returns a sorted list of maximal (s, e) tuples within [start, end) where nobody is busy and e - s >= min_len.\"\"\"\n    out = []\n    cur = None\n    t = start\n    while t < end:\n        busy = False\n        for cal in calendars:\n            for s, e in cal:\n                if s < e and s <= t < e:\n                    busy = True\n                    break\n            if busy:\n                break\n        if not busy:\n            if cur is None:\n                cur = t\n        else:\n            if cur is not None:\n                if t - cur >= min_len:\n                    out.append((cur, t))\n                cur = None\n        t += 1\n    if cur is not None and end - cur >= min_len:\n        out.append((cur, end))\n    return out\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'cp slots.py /tmp/common_free_original.py'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'cp slots.py /tmp/common_free_original.py'", "aggregated_output": "", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/164_e06/work/slots.py", "kind": "update"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/164_e06/work/slots.py", "kind": "update"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport importlib.util, random, time\nfrom slots import common_free\nspec = importlib.util.spec_from_file_location('original', '/tmp/common_free_original.py')\noriginal = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(original)\nrng = random.Random(42)\nfor case in range(25000):\n    start, end = rng.randint(-12, 12), rng.randint(-12, 20)\n    calendars = [[(rng.randint(-20, 25), rng.randint(-20, 25)) for _ in range(rng.randrange(8))] for _ in range(rng.randrange(7))]\n    min_len = rng.randint(-3, 25)\n    expected = original.common_free(calendars, start, end, min_len)\n    actual = common_free(calendars, start, end, min_len)\n    assert actual == expected, (calendars, start, end, min_len, expected, actual)\nprint('25,000 randomized comparisons passed')\n# Sparse disjoint intervals force the scan to process all 180,000 entries.\ncalendars = [[(i * 250, i * 250 + 100) for i in range(p * 60, (p + 1) * 60)] for p in range(3000)]\nrng.shuffle(calendars)\nfor cal in calendars:\n    rng.shuffle(cal)\nt0 = time.perf_counter()\nresult = common_free(calendars, 0, 50_000_000)\nelapsed = time.perf_counter() - t0\nassert len(result) == 180000\nassert result[0] == (100, 250)\nassert result[-1] == (44_999_850, 50_000_000)\nprint(f'3,000 x 60 intervals / 50,000,000 minutes: {elapsed:.3f}s; {len(result):,} free slots')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport importlib.util, random, time\nfrom slots import common_free\nspec = importlib.util.spec_from_file_location('original', '/tmp/common_free_original.py')\noriginal = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(original)\nrng = random.Random(42)\nfor case in range(25000):\n    start, end = rng.randint(-12, 12), rng.randint(-12, 20)\n    calendars = [[(rng.randint(-20, 25), rng.randint(-20, 25)) for _ in range(rng.randrange(8))] for _ in range(rng.randrange(7))]\n    min_len = rng.randint(-3, 25)\n    expected = original.common_free(calendars, start, end, min_len)\n    actual = common_free(calendars, start, end, min_len)\n    assert actual == expected, (calendars, start, end, min_len, expected, actual)\nprint('25,000 randomized comparisons passed')\n# Sparse disjoint intervals force the scan to process all 180,000 entries.\ncalendars = [[(i * 250, i * 250 + 100) for i in range(p * 60, (p + 1) * 60)] for p in range(3000)]\nrng.shuffle(calendars)\nfor cal in calendars:\n    rng.shuffle(cal)\nt0 = time.perf_counter()\nresult = common_free(calendars, 0, 50_000_000)\nelapsed = time.perf_counter() - t0\nassert len(result) == 180000\nassert result[0] == (100, 250)\nassert result[-1] == (44_999_850, 50_000_000)\nprint(f'3,000 x 60 intervals / 50,000,000 minutes: {elapsed:.3f}s; {len(result):,} free slots')\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport importlib.util, random, time\nfrom slots import common_free\nspec = importlib.util.spec_from_file_location('original', '/tmp/common_free_original.py')\noriginal = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(original)\nrng = random.Random(42)\nfor case in range(25000):\n    start, end = rng.randint(-12, 12), rng.randint(-12, 20)\n    calendars = [[(rng.randint(-20, 25), rng.randint(-20, 25)) for _ in range(rng.randrange(8))] for _ in range(rng.randrange(7))]\n    min_len = rng.randint(-3, 25)\n    expected = original.common_free(calendars, start, end, min_len)\n    actual = common_free(calendars, start, end, min_len)\n    assert actual == expected, (calendars, start, end, min_len, expected, actual)\nprint('25,000 randomized comparisons passed')\ncalendars = [[(i * 250, i * 250 + 100) for i in range(p * 60, (p + 1) * 60)] for p in range(3000)]\nrng.shuffle(calendars)\nfor cal in calendars:\n    rng.shuffle(cal)\nt0 = time.perf_counter()\nresult = common_free(calendars, 0, 50_000_000)\nelapsed = time.perf_counter() - t0\nassert len(result) == 180000\nassert result[0] == (100, 250)\nassert result[-1] == (44_999_850, 50_000_000)\nprint(f'3,000 x 60 intervals / 50,000,000 minutes: {elapsed:.3f}s; {len(result):,} free slots')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport importlib.util, random, time\nfrom slots import common_free\nspec = importlib.util.spec_from_file_location('original', '/tmp/common_free_original.py')\noriginal = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(original)\nrng = random.Random(42)\nfor case in range(25000):\n    start, end = rng.randint(-12, 12), rng.randint(-12, 20)\n    calendars = [[(rng.randint(-20, 25), rng.randint(-20, 25)) for _ in range(rng.randrange(8))] for _ in range(rng.randrange(7))]\n    min_len = rng.randint(-3, 25)\n    expected = original.common_free(calendars, start, end, min_len)\n    actual = common_free(calendars, start, end, min_len)\n    assert actual == expected, (calendars, start, end, min_len, expected, actual)\nprint('25,000 randomized comparisons passed')\ncalendars = [[(i * 250, i * 250 + 100) for i in range(p * 60, (p + 1) * 60)] for p in range(3000)]\nrng.shuffle(calendars)\nfor cal in calendars:\n    rng.shuffle(cal)\nt0 = time.perf_counter()\nresult = common_free(calendars, 0, 50_000_000)\nelapsed = time.perf_counter() - t0\nassert len(result) == 180000\nassert result[0] == (100, 250)\nassert result[-1] == (44_999_850, 50_000_000)\nprint(f'3,000 x 60 intervals / 50,000,000 minutes: {elapsed:.3f}s; {len(result):,} free slots')\nPY\"", "aggregated_output": "25,000 randomized comparisons passed\n3,000 x 60 intervals / 50,000,000 minutes: 0.141s; 180,000 free slots\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Updated [slots.py]( /home/bob/usecase-runs/batch/out/gpt-6.1-sol/164_e06/work/slots.py) to sort busy intervals and return gaps in their union. Runtime is now O(n log n), independent of horizon length.\n\nVerified against the original on 25,000 randomized inputs. The 3,000 \u00d7 60 interval benchmark over 50,000,000 minutes completed in **0.141 seconds**."}}
{"type": "turn.completed", "usage": {"input_tokens": 94689, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 1678, "reasoning_output_tokens": 68}}