GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Earliest-arrival journey planner over a timetable (minimum change times, directed footpaths never chained, trip limit, after-midnight times)
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.07
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Earliest-arrival journey planner with change times and footpaths `transit.py` plans trips over our city's offline timetable (`stops.csv`, `timetable.csv`, `footpaths.csv` in a directory). The draft only finds direct trips. Rewrite it (Python 3 stdlib only) so that `Network(directory).query(src, dst, start, max_trips)` follows these rules exactly. ## Data 1. `stops.csv`: `stop,min_change` (seconds). `timetable.csv`: `trip_id,seq,stop,arr,dep`; a trip's stops are visited in increasing `seq` (rows may be in any order). Times are `HH:MM:SS` and hours may be 24 or more (after midnight of the same service day). `footpaths.csv`: `from,to,seconds`, **directed**; if a pair appears several times the smallest time counts; rows with `from == to` are ignored. ## Journeys 2. You are at `src` at time `start` (`HH:MM:SS`). You may **board** a trip at a stop at that row's `dep` if you are ready there at or before `dep`, ride it, and **alight** at any later stop of that trip at its `arr`. 3. **Ready times:** at `src` you are ready at `start`. After alighting at stop s at time a you are ready to board **another** trip at s at `a + min_change[s]`. After walking a footpath you are ready at the arrival time with no change time. 4. **Footpaths** may be walked from `src` before the first trip, after alighting from any trip, and to reach `dst` at the end, but **never two footpaths in a row** (no walking from a stop you reached on foot). 5. `max_trips` (integer >= 0) is the maximum number of trips boarded; with 0 only `src == dst` or a single footpath `src -> dst` works. 6. Reaching `dst` means alighting there or walking a footpath into it (no change time is added at the end). If `src == dst` the answer is `(start, 0)`. 7. **Answer:** `(arrival, trips)` with the earliest possible arrival at `dst` formatted `HH:MM:SS` (hours zero-padded to at least 2 digits, may be >= 24), and among journeys with that arrival the smallest number of trips; `None` if `dst` cannot be reached. An unknown stop name (not in `stops.csv`) raises `KeyError`. 8. **Speed:** on the network in this directory, building the `Network` and answering 400 random queries with `max_trips` up to 4 must take under 20 seconds in total on our shared test box. ## Scoring Hidden check groups (partial credit = fraction passed; all must pass), all on exact answers: `direct_and_parsing` (1-2, 7 on a small hand-made network), `change_times` (3), `footpath_rules` (3-4), `max_trips_and_ties` (5, 7), `after_midnight` (1, 7), `unreachable_and_errors` (6-7), `city_queries` (150 queries on this directory's network), `fresh_city_queries` (150 queries on a freshly generated network of the same kind), `speed` (8).
Change time applies only between trips at a stop, not after walking; no two footpaths in a row; trip-count limit with fewest-trips tie-break; hours >= 24. A hand-made network pins each rule, then 300 city queries on two networks. 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. direct_and_parsing 2. change_times 3. footpath_rules 4. max_trips_and_ties 5. after_midnight 6. unreachable_and_errors 7. city_queries 8. fresh_city_queries 9. speed (400 queries < 20 s)
Setup notes: Use Case Scout batch8 case i10-transit-earliest-arrival (offline fixture). Prepared with batch8/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: 9/9. 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 6 (2 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.07
- 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 | 127,732 | 3,585 | $0.07 |
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. direct_and_parsing 2. change_times 3. footpath_rules 4. max_trips_and_ties 5. after_midnight 6. unreachable_and_errors 7. city_queries 8. fresh_city_queries 9. speed (400 queries < 20 s)
- 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.
Was this useful?
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": "01a1645a-8cc4-7aa0-9e0a-b424c7d8643c"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the draft and timetable, implement the routing rules, then check correctness and query speed."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd; rg --files -g 'AGENTS.md' -g 'transit.py' -g '*.csv' -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 'transit.py' -g '*.csv' -g '*test*'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/201_i10/work\ntimetable.csv\ntransit.py\nfootpaths.csv\nstops.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat transit.py; wc -l stops.csv timetable.csv footpaths.csv; head -5 stops.csv; head -5 timetable.csv; head -5 footpaths.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat transit.py; wc -l stops.csv timetable.csv footpaths.csv; head -5 stops.csv; head -5 timetable.csv; head -5 footpaths.csv'", "aggregated_output": "\"\"\"Earliest-arrival journey planner over our offline timetable. Spec: TASK.md. Draft: only direct trips, ignores change times and footpaths.\"\"\"\nimport csv, os\ndef t2s(s): h, m, x = s.split(\":\"); return int(h) * 3600 + int(m) * 60 + int(x)\nclass Network:\n def __init__(self, d):\n self.rows = list(csv.DictReader(open(os.path.join(d, \"timetable.csv\"))))\n def query(self, src, dst, start, max_trips):\n t = t2s(start); best = None; by = {}\n for r in self.rows: by.setdefault(r[\"trip_id\"], []).append(r)\n for rows in by.values():\n st = [r[\"stop\"] for r in rows]\n if src in st and dst in st and st.index(src) < st.index(dst) and t2s(rows[st.index(src)][\"dep\"]) >= t:\n a = t2s(rows[st.index(dst)][\"arr\"])\n if best is None or a < best: best = a\n return None if best is None else (f\"{best // 3600:02d}:{best % 3600 // 60:02d}:{best % 60:02d}\", 1)\n 25 stops.csv\n 977 timetable.csv\n 26 footpaths.csv\n 1028 total\nstop,min_change\nS00,300\nS01,60\nS02,300\nS03,0\ntrip_id,seq,stop,arr,dep\nL0a01,1,S16,06:13:00,06:13:00\nL0a01,2,S17,06:17:00,06:17:30\nL0a01,3,S23,06:21:30,06:22:00\nL0a01,4,S09,06:26:00,06:26:30\nfrom,to,seconds\nS22,S15,180\nS15,S22,240\nS03,S10,120\nS11,S21,480\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "total 56\ndrwxr-xr-x 6 bob bob 4096 Oct 22 12:04 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 12:03 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 12:04 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 12:04 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 12:04 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 12:04 .git\n-rw-r--r-- 1 bob bob 2686 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 316 Oct 22 10:08 footpaths.csv\n-rw-r--r-- 1 bob bob 197 Oct 22 10:08 stops.csv\n-rw-r--r-- 1 bob bob 29305 Oct 22 10:08 timetable.csv\n-rw-r--r-- 1 bob bob 931 Oct 22 10:08 transit.py\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"cat > transit.py <<'PY'\n\\\"\\\"\\\"Earliest-arrival routing with bounded boardings and directed footpaths.\\\"\\\"\\\"\n\nimport csv\nimport os\n\n\ndef t2s(value):\n hours, minutes, seconds = map(int, value.split(\\\":\\\"))\n return hours * 3600 + minutes * 60 + seconds\n\n\ndef _format_time(seconds):\n return f\\\"{seconds // 3600:02d}:{seconds % 3600 // 60:02d}:{seconds % 60:02d}\\\"\n\n\nclass Network:\n def __init__(self, directory):\n def read(name):\n with open(os.path.join(directory, name), newline=\\\"\\\") as stream:\n return list(csv.DictReader(stream))\n\n stops = read(\\\"stops.csv\\\")\n self._indices = {row[\\\"stop\\\"]: i for i, row in enumerate(stops)}\n self._changes = [int(row[\\\"min_change\\\"]) for row in stops]\n indices = self._indices\n grouped = {}\n for row in read(\\\"timetable.csv\\\"):\n grouped.setdefault(row[\\\"trip_id\\\"], []).append(row)\n self._trips = [\n tuple((indices[row[\\\"stop\\\"]], t2s(row[\\\"arr\\\"]), t2s(row[\\\"dep\\\"]))\n for row in sorted(rows, key=lambda row: int(row[\\\"seq\\\"])))\n for rows in grouped.values()\n ]\n paths = [{} for _ in stops]\n for row in read(\\\"footpaths.csv\\\"):\n source, target = indices[row[\\\"from\\\"]], indices[row[\\\"to\\\"]]\n if source \"'!= target:\n seconds = int(row[\"seconds\"])\n paths[source][target] = min(paths[source].get(target, seconds), seconds)\n self._paths = [tuple(paths_from.items()) for paths_from in paths]\n\n def query(self, src, dst, start, max_trips):\n source, target = self._indices[src], self._indices[dst]\n start_time = t2s(start)\n if source == target:\n return _format_time(start_time), 0\n infinity = float(\"inf\")\n ready = [infinity] * len(self._changes)\n ready[source] = start_time\n best, best_count = infinity, 0\n for stop, seconds in self._paths[source]:\n ready[stop] = min(ready[stop], start_time + seconds)\n if stop == target:\n best = start_time + seconds\n\n for count in range(1, max_trips + 1):\n # Read only the previous round: every boarding consumes one trip.\n alight = [infinity] * len(ready)\n for trip in self._trips:\n aboard = False\n for stop, arrival, departure in trip:\n if aboard and arrival < alight[stop]:\n alight[stop] = arrival\n if ready[stop] <= departure:\n aboard = True\n if alight[target] < best:\n best, best_count = alight[target], count\n next_ready = ready.copy()\n for stop, arrival in enumerate(alight):\n if arrival == infinity:\n continue\n next_ready[stop] = min(next_ready[stop], arrival + self._changes[stop])\n # Walk only from an alighting, never from another walk.\n for end, seconds in self._paths[stop]:\n walked = arrival + seconds\n next_ready[end] = min(next_ready[end], walked)\n if end == target and walked < best:\n best, best_count = walked, count\n if next_ready == ready:\n break\n ready = next_ready\n return None if best == infinity else (_format_time(best), best_count)\nPY'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"cat > transit.py <<'PY'\n\\\"\\\"\\\"Earliest-arrival routing with bounded boardings and directed footpaths.\\\"\\\"\\\"\n\nimport csv\nimport os\n\n\ndef t2s(value):\n hours, minutes, seconds = map(int, value.split(\\\":\\\"))\n return hours * 3600 + minutes * 60 + seconds\n\n\ndef _format_time(seconds):\n return f\\\"{seconds // 3600:02d}:{seconds % 3600 // 60:02d}:{seconds % 60:02d}\\\"\n\n\nclass Network:\n def __init__(self, directory):\n def read(name):\n with open(os.path.join(directory, name), newline=\\\"\\\") as stream:\n return list(csv.DictReader(stream))\n\n stops = read(\\\"stops.csv\\\")\n self._indices = {row[\\\"stop\\\"]: i for i, row in enumerate(stops)}\n self._changes = [int(row[\\\"min_change\\\"]) for row in stops]\n indices = self._indices\n grouped = {}\n for row in read(\\\"timetable.csv\\\"):\n grouped.setdefault(row[\\\"trip_id\\\"], []).append(row)\n self._trips = [\n tuple((indices[row[\\\"stop\\\"]], t2s(row[\\\"arr\\\"]), t2s(row[\\\"dep\\\"]))\n for row in sorted(rows, key=lambda row: int(row[\\\"seq\\\"])))\n for rows in grouped.values()\n ]\n paths = [{} for _ in stops]\n for row in read(\\\"footpaths.csv\\\"):\n source, target = indices[row[\\\"from\\\"]], indices[row[\\\"to\\\"]]\n if source \"'!= target:\n seconds = int(row[\"seconds\"])\n paths[source][target] = min(paths[source].get(target, seconds), seconds)\n self._paths = [tuple(paths_from.items()) for paths_from in paths]\n\n def query(self, src, dst, start, max_trips):\n source, target = self._indices[src], self._indices[dst]\n start_time = t2s(start)\n if source == target:\n return _format_time(start_time), 0\n infinity = float(\"inf\")\n ready = [infinity] * len(self._changes)\n ready[source] = start_time\n best, best_count = infinity, 0\n for stop, seconds in self._paths[source]:\n ready[stop] = min(ready[stop], start_time + seconds)\n if stop == target:\n best = start_time + seconds\n\n for count in range(1, max_trips + 1):\n # Read only the previous round: every boarding consumes one trip.\n alight = [infinity] * len(ready)\n for trip in self._trips:\n aboard = False\n for stop, arrival, departure in trip:\n if aboard and arrival < alight[stop]:\n alight[stop] = arrival\n if ready[stop] <= departure:\n aboard = True\n if alight[target] < best:\n best, best_count = alight[target], count\n next_ready = ready.copy()\n for stop, arrival in enumerate(alight):\n if arrival == infinity:\n continue\n next_ready[stop] = min(next_ready[stop], arrival + self._changes[stop])\n # Walk only from an alighting, never from another walk.\n for end, seconds in self._paths[stop]:\n walked = arrival + seconds\n next_ready[end] = min(next_ready[end], walked)\n if end == target and walked < best:\n best, best_count = walked, count\n if next_ready == ready:\n break\n ready = next_ready\n return None if best == infinity else (_format_time(best), best_count)\nPY'", "aggregated_output": "", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "The implementation now scans trips by boarding count and keeps alighting times separate from boarding readiness. This applies change times at transfers while allowing one footpath after alighting. I\u2019m checking it against a slower reference solver on generated networks and the city timetable."}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"cat > /tmp/check_transit.py <<'PY'\nimport csv, random, tempfile, time\nfrom transit import Network, t2s, _format_time\n\n\ndef reference(net, src, dst, start, limit):\n s, d = net._indices[src], net._indices[dst]\n t = t2s(start)\n if s == d:\n return _format_time(t), 0\n # Explicit states distinguish origins that allow walking from foot arrivals.\n states = {(s, True): t}\n best = None\n for k in range(limit + 1):\n expanded = dict(states)\n for (stop, can_walk), at in states.items():\n if can_walk:\n for end, duration in net._paths[stop]:\n key = end, False\n expanded[key] = min(expanded.get(key, float('inf')), at + duration)\n for (stop, _), at in expanded.items():\n if stop == d:\n candidate = at, k\n if best is None or candidate < best:\n best = candidate\n following = {}\n for (stop, can_walk), at in expanded.items():\n ready = at + (net._changes[stop] if can_walk and k else 0)\n for trip in net._trips:\n for i, (board, _, dep) in enumerate(trip):\n if board == stop and dep >= ready:\n for end, arr, _ in trip[i+1:]:\n key = end, True\n following[key] = min(following.get(key, float('inf')), arr)\n states = following\n return None if best is None else (_format_time(best[0]), best[1])\n\nrng = random.Random(183)\nfor trial in range(50):\n with tempfile.TemporaryDirectory() as directory:\n def write(name, header, rows):\n with open(directory + '/' + name, 'w', newline='') as f:\n w = csv.writer(f); w.writerow(header); w.writerows(rows)\n write('stops.csv', ['stop','min_change'], [(str(i), rng.randrange(8)) for i in range(6)])\n rows = []\n for trip in range(10):\n now = 24*3600 + rng.randrange(80)\n for seq in range(rng.randrange(2, 7)):\n now += rng.randrange(6)\n arr = now\n now += rng.randrange(4)\n rows.append((trip, seq, str(rng.randrange(6)), _format_time(arr), _format_time(now)))\n rng.shuffle(rows)\n write('timetable.csv', ['trip_id','seq','stop','arr','dep'], rows)\n write('footpaths.csv', ['from','to','seconds'], [(str(rng.randrange(6)),str(rng.randrange(6)),rng.randrange(10)) for _ in range(14)])\n net = Network(directory)\n for _ in range(30):\n args = str(rng.randrange(6)), str(rng.randrange(6)), _format_time(86400+rng.randrange(70)), rng.randrange(5)\n actual, expected = net.query(*args), reference(net, *args)\n assert actual == expected, (trial, args, actual, expected)\n try:\n net.query('missing', 'missing', '00:00:00', 0)\n except KeyError:\n pass\n else:\n raise AssertionError('unknown stop accepted')\nprint('1500 generated-network queries matched reference')\nnet = Network('.')\nnames = list(net._indices)\nfor _ in range(30):\n args = rng.choice(names), rng.choice(names), _format_time(rng.randrange(5*3600, 26*3600)), rng.randrange(5)\n assert net.query(*args) == reference(net, *args), args\nprint('30 city queries matched reference')\nbegin = time.perf_counter()\nnet = Network('.')\nfor _ in range(400):\n net.query(rng.choice(names), rng.choice(names), _format_time(rng.randrange(5*3600,26*3600)), rng.randrange(5))\nprint(f'Build plus 400 queries: {time.perf_counter()-begin:.3f}s')\nPY\nPYTHONPATH=. python /tmp/check_transit.py\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"cat > /tmp/check_transit.py <<'PY'\nimport csv, random, tempfile, time\nfrom transit import Network, t2s, _format_time\n\n\ndef reference(net, src, dst, start, limit):\n s, d = net._indices[src], net._indices[dst]\n t = t2s(start)\n if s == d:\n return _format_time(t), 0\n # Explicit states distinguish origins that allow walking from foot arrivals.\n states = {(s, True): t}\n best = None\n for k in range(limit + 1):\n expanded = dict(states)\n for (stop, can_walk), at in states.items():\n if can_walk:\n for end, duration in net._paths[stop]:\n key = end, False\n expanded[key] = min(expanded.get(key, float('inf')), at + duration)\n for (stop, _), at in expanded.items():\n if stop == d:\n candidate = at, k\n if best is None or candidate < best:\n best = candidate\n following = {}\n for (stop, can_walk), at in expanded.items():\n ready = at + (net._changes[stop] if can_walk and k else 0)\n for trip in net._trips:\n for i, (board, _, dep) in enumerate(trip):\n if board == stop and dep >= ready:\n for end, arr, _ in trip[i+1:]:\n key = end, True\n following[key] = min(following.get(key, float('inf')), arr)\n states = following\n return None if best is None else (_format_time(best[0]), best[1])\n\nrng = random.Random(183)\nfor trial in range(50):\n with tempfile.TemporaryDirectory() as directory:\n def write(name, header, rows):\n with open(directory + '/' + name, 'w', newline='') as f:\n w = csv.writer(f); w.writerow(header); w.writerows(rows)\n write('stops.csv', ['stop','min_change'], [(str(i), rng.randrange(8)) for i in range(6)])\n rows = []\n for trip in range(10):\n now = 24*3600 + rng.randrange(80)\n for seq in range(rng.randrange(2, 7)):\n now += rng.randrange(6)\n arr = now\n now += rng.randrange(4)\n rows.append((trip, seq, str(rng.randrange(6)), _format_time(arr), _format_time(now)))\n rng.shuffle(rows)\n write('timetable.csv', ['trip_id','seq','stop','arr','dep'], rows)\n write('footpaths.csv', ['from','to','seconds'], [(str(rng.randrange(6)),str(rng.randrange(6)),rng.randrange(10)) for _ in range(14)])\n net = Network(directory)\n for _ in range(30):\n args = str(rng.randrange(6)), str(rng.randrange(6)), _format_time(86400+rng.randrange(70)), rng.randrange(5)\n actual, expected = net.query(*args), reference(net, *args)\n assert actual == expected, (trial, args, actual, expected)\n try:\n net.query('missing', 'missing', '00:00:00', 0)\n except KeyError:\n pass\n else:\n raise AssertionError('unknown stop accepted')\nprint('1500 generated-network queries matched reference')\nnet = Network('.')\nnames = list(net._indices)\nfor _ in range(30):\n args = rng.choice(names), rng.choice(names), _format_time(rng.randrange(5*3600, 26*3600)), rng.randrange(5)\n assert net.query(*args) == reference(net, *args), args\nprint('30 city queries matched reference')\nbegin = time.perf_counter()\nnet = Network('.')\nfor _ in range(400):\n net.query(rng.choice(names), rng.choice(names), _format_time(rng.randrange(5*3600,26*3600)), rng.randrange(5))\nprint(f'Build plus 400 queries: {time.perf_counter()-begin:.3f}s')\nPY\nPYTHONPATH=. python /tmp/check_transit.py\"", "aggregated_output": "/bin/bash: line 80: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc 'PYTHONPATH=. python3 /tmp/check_transit.py'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc 'PYTHONPATH=. python3 /tmp/check_transit.py'", "aggregated_output": "1500 generated-network queries matched reference\n30 city queries matched reference\nBuild plus 400 queries: 0.036s\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_8", "type": "agent_message", "text": "Rewrote [transit.py](/home/bob/usecase-runs/batch/out/gpt-6.1-sol/201_i10/work/transit.py) with transfers, change times, directed footpaths, trip limits, arrival ties, and after-midnight times.\n\nValidation: 1,500 generated-network queries and 30 city queries matched a reference solver. Building the network plus 400 queries took **0.036 seconds** locally."}}
{"type": "turn.completed", "usage": {"input_tokens": 127732, "cached_input_tokens": 114560, "cache_write_input_tokens": 0, "output_tokens": 3585, "reasoning_output_tokens": 256}}