GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Refactor a legacy freight tariff into structured output while preserving its quirks for old orders
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 56s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Refactor a legacy freight tariff without changing old invoices `legacy/tariff.py` is a 2019 script that prints one total per order from a pipe-separated file (`python legacy/tariff.py orders.txt`). Old invoices depend on its **exact** output, quirks included. Write `solve.py` (Python 3 standard library only): `python solve.py input.json answer.json`, where `input.json` has `orders` (each `id`, `date`, `zone`, `kg`, `declared`, `flags`; `orders.txt` holds the same orders in the legacy format). **The `legacy/` directory is deleted before scoring**, so `solve.py` must not import or run it. Fresh order sets are generated. ## Required behaviour 1. Orders dated **before 2027-01-01**: reproduce the legacy script exactly. That covers its `total` string and its `ERROR zone` cases, and every intermediate value as the legacy code computes it: billable kg, the rate it uses, base (after the express doubling), fuel, insurance, discount and the remote charge. Floating-point and formatting quirks included. 2. Orders dated **on or after 2027-01-01** follow the same legacy calculation, except: a. the zone rate is always the order's own zone rate (A, B or C; zone is case-insensitive), with no weight-based switch; any other zone is an error; b. flag `H` (hazmat) adds a flat 15.00 after everything else (not discounted). Before 2027 `H` is ignored, like any unknown flag. 3. Output per order, in input order: `{"id", "billable_kg", "rate", "base", "fuel", "insurance", "discount", "remote", "hazmat", "total"}` with numbers as JSON numbers and `total` as the string the legacy code would print (`"%.2f"`). An error order is `{"id", "error": "zone"}`. `hazmat` is 0 for orders without it, and `remote` is 9.99 or 0. ## answer.json `{"quotes": [...]}` ## Scoring Hidden checks (partial credit; all must pass; values must be equal exactly): `legacy_totals_preserved`, `legacy_breakdown` (pre-2027 orders), `new_rules_totals`, `new_rules_breakdown` (2027+ orders) on the given orders; `fresh_run_1`, `fresh_run_2` (all fields, all orders). 30-second limit per run.
A 'clean' rewrite with exact decimal rounding, or one that fixes the zone bug everywhere, breaks old totals; the legacy file is removed before scoring, so it must be reimplemented. 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. legacy_totals_preserved 2. legacy_breakdown 3. new_rules_totals 4. new_rules_breakdown 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch12 case m09-legacy-freight-refactor (offline fixture). Prepared with batch12/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: 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 4 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.05
- Wall time
- 56s
- 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 | 98,490 | 1,894 | $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 on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. legacy_totals_preserved 2. legacy_breakdown 3. new_rules_totals 4. new_rules_breakdown 5. fresh_run_1 6. 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.
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": "01a1640b-9cbd-7a20-ae2e-beb3c7f53502"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the legacy calculation, reproduce its arithmetic and formatting in `solve.py`, and check the pre-2027 totals against the original script."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g '*tariff*' -g '*.json' -g '*orders*' -g '*.py'\"", "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 '*tariff*' -g '*.json' -g '*orders*' -g '*.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/128_m09/work\norders.txt\ninput.json\nlegacy/tariff.py\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat legacy/tariff.py && cat input.json && head -20 orders.txt'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat legacy/tariff.py && cat input.json && head -20 orders.txt'", "aggregated_output": "# freight tariff -- DO NOT TOUCH, invoices depend on this exact output (v1.7, 2019)\n# usage: python legacy/tariff.py orders.txt -> prints one \"id TOTAL x.xx\" line per order\nimport sys, math\n\nRATES = {'A': 4.10, 'B': 5.35, 'C': 7.80}\n\ndef billable(kg):\n kg = float(kg)\n if kg < 1: kg = 1\n return math.ceil(kg * 2) / 2.0\n\ndef money(x):\n return float('%.2f' % x)\n\ndef calc(line):\n parts = line.strip().split('|')\n oid, date, zone, kg, declared = parts[0], parts[1], parts[2].upper(), parts[3], parts[4]\n flags = parts[5].split(',') if len(parts) > 5 and parts[5] else []\n b = billable(kg)\n if zone == 'A':\n rate = RATES['A']\n elif zone == 'B' or b > 30:\n rate = RATES['B']\n elif zone == 'C':\n rate = RATES['C']\n else:\n return oid + ' ERROR zone'\n base = money(b * rate)\n if 'X' in flags:\n base = money(base * 2)\n fuel = money(base * 0.125)\n dec = float(declared or 0)\n ins = 0.0\n if dec > 100:\n ins = money(dec * 0.0035)\n if ins < 0.5: ins = 0.5\n sub = base + fuel\n disc = 0.0\n for f in flags:\n if f.startswith('D') and f[1:].isdigit():\n pct = int(f[1:])\n if 'X' in flags:\n disc = money((sub + ins) * pct / 100.0)\n else:\n disc = money(sub * pct / 100.0)\n tot = sub + ins - disc\n if 'R' in flags:\n tot = tot + 9.99\n return oid + ' TOTAL ' + ('%.2f' % tot)\n\nif __name__ == '__main__':\n for ln in open(sys.argv[1]):\n if ln.strip() and not ln.startswith('#'):\n print(calc(ln))\n{\n \"orders\": [\n {\n \"id\": \"S900-00\",\n \"date\": \"2026-07-13\",\n \"zone\": \"b\",\n \"kg\": \"0.999\",\n \"declared\": \"100.01\",\n \"flags\": [\n \"D10\"\n ]\n },\n {\n \"id\": \"S900-01\",\n \"date\": \"2026-02-01\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": [\n \"D15\",\n \"D10\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-02\",\n \"date\": \"2026-07-19\",\n \"zone\": \"C\",\n \"kg\": \"0.4\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-03\",\n \"date\": \"2026-11-13\",\n \"zone\": \"Z\",\n \"kg\": \"0.999\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-04\",\n \"date\": \"2026-11-20\",\n \"zone\": \"b\",\n \"kg\": \"29.73\",\n \"declared\": \"142.86\",\n \"flags\": [\n \"D10\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-05\",\n \"date\": \"2027-07-03\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-06\",\n \"date\": \"2026-07-03\",\n \"zone\": \"b\",\n \"kg\": \"40.2\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-07\",\n \"date\": \"2026-06-01\",\n \"zone\": \"b\",\n \"kg\": \"30.2\",\n \"declared\": \"4939.48\",\n \"flags\": []\n },\n {\n \"id\": \"S900-08\",\n \"date\": \"2027-07-04\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"142.86\",\n \"flags\": []\n },\n {\n \"id\": \"S900-09\",\n \"date\": \"2026-09-03\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"D5\",\n \"X\"\n ]\n },\n {\n \"id\": \"S900-10\",\n \"date\": \"2026-10-26\",\n \"zone\": \"A\",\n \"kg\": \"0.999\",\n \"declared\": \"0\",\n \"flags\": [\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-11\",\n \"date\": \"2026-12-19\",\n \"zone\": \"Z\",\n \"kg\": \"30\",\n \"declared\": \"357.15\",\n \"flags\": [\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-12\",\n \"date\": \"2026-05-24\",\n \"zone\": \"Z\",\n \"kg\": \"12.25\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"H\"\n ]\n },\n {\n \"id\": \"S900-13\",\n \"date\": \"2026-06-28\",\n \"zone\": \"b\",\n \"kg\": \"0.67\",\n \"declared\": \"100.01\",\n \"flags\": []\n },\n {\n \"id\": \"S900-14\",\n \"date\": \"2026-08-03\",\n \"zone\": \"C\",\n \"kg\": \"0.999\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-15\",\n \"date\": \"2026-04-24\",\n \"zone\": \"b\",\n \"kg\": \"0.999\",\n \"declared\": \"0\",\n \"flags\": [\n \"R\",\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-16\",\n \"date\": \"2027-01-15\",\n \"zone\": \"Z\",\n \"kg\": \"25.72\",\n \"declared\": \"0\",\n \"flags\": [\n \"H\"\n ]\n },\n {\n \"id\": \"S900-17\",\n \"date\": \"2026-05-05\",\n \"zone\": \"b\",\n \"kg\": \"0.999\",\n \"declared\": \"308.43\",\n \"flags\": []\n },\n {\n \"id\": \"S900-18\",\n \"date\": \"2026-06-16\",\n \"zone\": \"b\",\n \"kg\": \"17.93\",\n \"declared\": \"6378.73\",\n \"flags\": [\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-19\",\n \"date\": \"2026-11-14\",\n \"zone\": \"A\",\n \"kg\": \"12.25\",\n \"declared\": \"99.99\",\n \"flags\": []\n },\n {\n \"id\": \"S900-20\",\n \"date\": \"2026-04-09\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": [\n \"D10\"\n ]\n },\n {\n \"id\": \"S900-21\",\n \"date\": \"2026-03-01\",\n \"zone\": \"C\",\n \"kg\": \"36.9\",\n \"declared\": \"1214.88\",\n \"flags\": [\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-22\",\n \"date\": \"2026-09-18\",\n \"zone\": \"A\",\n \"kg\": \"30\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"X\",\n \"R\"\n ]\n },\n {\n \"id\": \"S900-23\",\n \"date\": \"2026-10-15\",\n \"zone\": \"Z\",\n \"kg\": \"30\",\n \"declared\": \"142.86\",\n \"flags\": []\n },\n {\n \"id\": \"S900-24\",\n \"date\": \"2026-06-17\",\n \"zone\": \"C\",\n \"kg\": \"0.999\",\n \"declared\": \"100.01\",\n \"flags\": []\n },\n {\n \"id\": \"S900-25\",\n \"date\": \"2026-12-28\",\n \"zone\": \"C\",\n \"kg\": \"0.62\",\n \"declared\": \"207.69\",\n \"flags\": [\n \"D15\",\n \"D10\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-26\",\n \"date\": \"2027-06-23\",\n \"zone\": \"b\",\n \"kg\": \"12.25\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"Q\",\n \"D15\",\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-27\",\n \"date\": \"2026-01-27\",\n \"zone\": \"b\",\n \"kg\": \"0.999\",\n \"declared\": \"323.6\",\n \"flags\": [\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-28\",\n \"date\": \"2026-02-28\",\n \"zone\": \"b\",\n \"kg\": \"30.2\",\n \"declared\": \"153.66\",\n \"flags\": [\n \"D10\"\n ]\n },\n {\n \"id\": \"S900-29\",\n \"date\": \"2026-11-01\",\n \"zone\": \"Z\",\n \"kg\": \"0.49\",\n \"declared\": \"100.01\",\n \"flags\": []\n },\n {\n \"id\": \"S900-30\",\n \"date\": \"2026-02-11\",\n \"zone\": \"Z\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": [\n \"X\",\n \"D10\"\n ]\n },\n {\n \"id\": \"S900-31\",\n \"date\": \"2027-01-08\",\n \"zone\": \"A\",\n \"kg\": \"25.69\",\n \"declared\": \"3499.33\",\n \"flags\": []\n },\n {\n \"id\": \"S900-32\",\n \"date\": \"2027-05-06\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"0\",\n \"flags\": [\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-33\",\n \"date\": \"2026-02-14\",\n \"zone\": \"C\",\n \"kg\": \"12.25\",\n \"declared\": \"100\",\n \"flags\": [\n \"H\"\n ]\n },\n {\n \"id\": \"S900-34\",\n \"date\": \"2026-10-18\",\n \"zone\": \"A\",\n \"kg\": \"30.2\",\n \"declared\": \"100\",\n \"flags\": []\n },\n {\n \"id\": \"S900-35\",\n \"date\": \"2026-03-05\",\n \"zone\": \"C\",\n \"kg\": \"16.33\",\n \"declared\": \"\",\n \"flags\": []\n },\n {\n \"id\": \"S900-36\",\n \"date\": \"2027-11-19\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": []\n },\n {\n \"id\": \"S900-37\",\n \"date\": \"2026-02-25\",\n \"zone\": \"Z\",\n \"kg\": \"12.25\",\n \"declared\": \"159.5\",\n \"flags\": [\n \"X\",\n \"D10\"\n ]\n },\n {\n \"id\": \"S900-38\",\n \"date\": \"2026-06-23\",\n \"zone\": \"A\",\n \"kg\": \"12.25\",\n \"declared\": \"100.01\",\n \"flags\": []\n },\n {\n \"id\": \"S900-39\",\n \"date\": \"2026-12-24\",\n \"zone\": \"A\",\n \"kg\": \"23.81\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-40\",\n \"date\": \"2026-11-05\",\n \"zone\": \"b\",\n \"kg\": \"5.49\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"D15\",\n \"X\"\n ]\n },\n {\n \"id\": \"S900-41\",\n \"date\": \"2026-05-15\",\n \"zone\": \"C\",\n \"kg\": \"0.87\",\n \"declared\": \"137.87\",\n \"flags\": [\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-42\",\n \"date\": \"2027-05-15\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"Q\",\n \"D10\",\n \"R\"\n ]\n },\n {\n \"id\": \"S900-43\",\n \"date\": \"2027-08-01\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"100.01\",\n \"flags\": [\n \"Q\",\n \"R\"\n ]\n },\n {\n \"id\": \"S900-44\",\n \"date\": \"2026-09-01\",\n \"zone\": \"C\",\n \"kg\": \"0.999\",\n \"declared\": \"296.13\",\n \"flags\": [\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-45\",\n \"date\": \"2027-05-15\",\n \"zone\": \"C\",\n \"kg\": \"0.98\",\n \"declared\": \"100\",\n \"flags\": [\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-46\",\n \"date\": \"2026-06-01\",\n \"zone\": \"Z\",\n \"kg\": \"0.999\",\n \"declared\": \"142.86\",\n \"flags\": []\n },\n {\n \"id\": \"S900-47\",\n \"date\": \"2026-10-17\",\n \"zone\": \"b\",\n \"kg\": \"30\",\n \"declared\": \"272.27\",\n \"flags\": []\n },\n {\n \"id\": \"S900-48\",\n \"date\": \"2026-03-11\",\n \"zone\": \"b\",\n \"kg\": \"0.19\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"D15\",\n \"R\",\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-49\",\n \"date\": \"2026-10-20\",\n \"zone\": \"C\",\n \"kg\": \"0.999\",\n \"declared\": \"\",\n \"flags\": [\n \"H\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-50\",\n \"date\": \"2026-02-12\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"0\",\n \"flags\": []\n },\n {\n \"id\": \"S900-51\",\n \"date\": \"2026-03-10\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"4474.11\",\n \"flags\": []\n },\n {\n \"id\": \"S900-52\",\n \"date\": \"2027-05-22\",\n \"zone\": \"A\",\n \"kg\": \"0.999\",\n \"declared\": \"99.99\",\n \"flags\": []\n },\n {\n \"id\": \"S900-53\",\n \"date\": \"2026-05-24\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"100\",\n \"flags\": [\n \"H\",\n \"D5\",\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-54\",\n \"date\": \"2027-01-16\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"4948.61\",\n \"flags\": [\n \"H\"\n ]\n },\n {\n \"id\": \"S900-55\",\n \"date\": \"2026-03-26\",\n \"zone\": \"Z\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": []\n },\n {\n \"id\": \"S900-56\",\n \"date\": \"2026-12-12\",\n \"zone\": \"Z\",\n \"kg\": \"7.11\",\n \"declared\": \"99.99\",\n \"flags\": []\n },\n {\n \"id\": \"S900-57\",\n \"date\": \"2026-01-17\",\n \"zone\": \"b\",\n \"kg\": \"12.25\",\n \"declared\": \"382.4\",\n \"flags\": [\n \"D5\",\n \"R\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-58\",\n \"date\": \"2027-09-05\",\n \"zone\": \"Z\",\n \"kg\": \"47.4\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"X\",\n \"R\",\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-59\",\n \"date\": \"2026-07-07\",\n \"zone\": \"C\",\n \"kg\": \"12.25\",\n \"declared\": \"0\",\n \"flags\": []\n },\n {\n \"id\": \"S900-60\",\n \"date\": \"2026-06-08\",\n \"zone\": \"C\",\n \"kg\": \"30.2\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-61\",\n \"date\": \"2026-08-26\",\n \"zone\": \"A\",\n \"kg\": \"0.999\",\n \"declared\": \"99.99\",\n \"flags\": [\n \"D15\"\n ]\n },\n {\n \"id\": \"S900-62\",\n \"date\": \"2027-01-10\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"142.86\",\n \"flags\": []\n },\n {\n \"id\": \"S900-63\",\n \"date\": \"2026-07-11\",\n \"zone\": \"C\",\n \"kg\": \"12.25\",\n \"declared\": \"99.99\",\n \"flags\": []\n },\n {\n \"id\": \"S900-64\",\n \"date\": \"2026-06-14\",\n \"zone\": \"Z\",\n \"kg\": \"0.9\",\n \"declared\": \"142.86\",\n \"flags\": []\n },\n {\n \"id\": \"S900-65\",\n \"date\": \"2026-08-23\",\n \"zone\": \"A\",\n \"kg\": \"0.53\",\n \"declared\": \"6690.24\",\n \"flags\": []\n },\n {\n \"id\": \"S900-66\",\n \"date\": \"2026-01-25\",\n \"zone\": \"C\",\n \"kg\": \"30\",\n \"declared\": \"0\",\n \"flags\": []\n },\n {\n \"id\": \"S900-67\",\n \"date\": \"2026-08-24\",\n \"zone\": \"C\",\n \"kg\": \"0.999\",\n \"declared\": \"0\",\n \"flags\": [\n \"Q\"\n ]\n },\n {\n \"id\": \"S900-68\",\n \"date\": \"2026-09-13\",\n \"zone\": \"b\",\n \"kg\": \"30.2\",\n \"declared\": \"7137.9\",\n \"flags\": [\n \"D5\"\n ]\n },\n {\n \"id\": \"S900-69\",\n \"date\": \"2027-01-01\",\n \"zone\": \"C\",\n \"kg\": \"40.4\",\n \"declared\": \"372.72\",\n \"flags\": []\n }\n ]\n}# id|date|zone|kg|declared|flags\nS900-00|2026-07-13|b|0.999|100.01|D10\nS900-01|2026-02-01|C|30|0|D15,D10,Q\nS900-02|2026-07-19|C|0.4||\nS900-03|2026-11-13|Z|0.999||\nS900-04|2026-11-20|b|29.73|142.86|D10,Q\nS900-05|2027-07-03|C|30||\nS900-06|2026-07-03|b|40.2||\nS900-07|2026-06-01|b|30.2|4939.48|\nS900-08|2027-07-04|C|30.2|142.86|\nS900-09|2026-09-03|C|30|99.99|D5,X\nS900-10|2026-10-26|A|0.999|0|Q\nS900-11|2026-12-19|Z|30|357.15|D5\nS900-12|2026-05-24|Z|12.25|99.99|H\nS900-13|2026-06-28|b|0.67|100.01|\nS900-14|2026-08-03|C|0.999||\nS900-15|2026-04-24|b|0.999|0|R,D15\nS900-16|2027-01-15|Z|25.72|0|H\nS900-17|2026-05-05|b|0.999|308.43|\nS900-18|2026-06-16|b|17.93|6378.73|D15\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/128_m09/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/128_m09/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "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_4", "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.completed", "item": {"id": "item_5", "type": "agent_message", "text": "The legacy code lets orders over 30 billable kg use rate B even with an invalid zone; the last discount flag also wins. I\u2019ve preserved those quirks for pre-2027 orders and kept the original rounding and arithmetic order. I\u2019ll verify with `python3`, since this environment has no `python` command."}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json\npython3 - <<'PY'\nimport json, random\nfrom pathlib import Path\nfrom solve import quote\nsource = Path('legacy/tariff.py').read_text().split(\\\"if __name__\\\")[0]\n# Expose the original locals without altering any calculation.\nsource = source.replace(\\\"return oid + ' ERROR zone'\\\", \\\"return {'id': oid, 'error': 'zone'}\\\")\nsource = source.replace(\\\"return oid + ' TOTAL ' + ('%.2f' % tot)\\\", \\\"return dict(id=oid, billable_kg=b, rate=rate, base=base, fuel=fuel, insurance=ins, discount=disc, remote=9.99 if 'R' in flags else 0, hazmat=0, total='%.2f' % tot)\\\")\nold = {}; exec(source, old)\nnew_source = source.replace(\\\"elif zone == 'B' or b > 30:\\\", \\\"elif zone == 'B':\\\")\nnew_source = new_source.replace(\\\" return dict(id=oid\\\", \\\" if 'H' in flags: tot = tot + 15.00\\\\n return dict(id=oid\\\")\nnew_source = new_source.replace('hazmat=0,', \\\"hazmat=15.00 if 'H' in flags else 0,\\\")\nnew = {}; exec(new_source, new)\nrng = random.Random(941)\norders = json.loads(Path('input.json').read_text())['orders']\nfor i in range(10000):\n orders.append(dict(id=str(i), date=rng.choice(['2026-12-31','2027-01-01']), zone=rng.choice(['A','b','C','Z']), kg=rng.choice(['30','30.001','0.999',str(rng.uniform(-2,100))]), declared=rng.choice(['','100','100.01','142.86',str(rng.uniform(0,10000))]), flags=rng.choices(['X','R','H','D0','D5','D10','D15','D100','D120','Q','d10','D-1'], k=rng.randrange(8))))\nfor order in orders:\n line = '|'.join([order['id'],order['date'],order['zone'],order['kg'],order['declared'],','.join(order['flags'])])\n reference = new if order['date'] >= '2027-01-01' else old\n expected = reference['calc'](line)\n assert quote(order) == expected, (order, quote(order), expected)\nprint('All fields matched the instrumented reference for',len(orders),'orders.')\nassert len(json.loads(Path('answer.json').read_text())['quotes']) == 70\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json\npython3 - <<'PY'\nimport json, random\nfrom pathlib import Path\nfrom solve import quote\nsource = Path('legacy/tariff.py').read_text().split(\\\"if __name__\\\")[0]\n# Expose the original locals without altering any calculation.\nsource = source.replace(\\\"return oid + ' ERROR zone'\\\", \\\"return {'id': oid, 'error': 'zone'}\\\")\nsource = source.replace(\\\"return oid + ' TOTAL ' + ('%.2f' % tot)\\\", \\\"return dict(id=oid, billable_kg=b, rate=rate, base=base, fuel=fuel, insurance=ins, discount=disc, remote=9.99 if 'R' in flags else 0, hazmat=0, total='%.2f' % tot)\\\")\nold = {}; exec(source, old)\nnew_source = source.replace(\\\"elif zone == 'B' or b > 30:\\\", \\\"elif zone == 'B':\\\")\nnew_source = new_source.replace(\\\" return dict(id=oid\\\", \\\" if 'H' in flags: tot = tot + 15.00\\\\n return dict(id=oid\\\")\nnew_source = new_source.replace('hazmat=0,', \\\"hazmat=15.00 if 'H' in flags else 0,\\\")\nnew = {}; exec(new_source, new)\nrng = random.Random(941)\norders = json.loads(Path('input.json').read_text())['orders']\nfor i in range(10000):\n orders.append(dict(id=str(i), date=rng.choice(['2026-12-31','2027-01-01']), zone=rng.choice(['A','b','C','Z']), kg=rng.choice(['30','30.001','0.999',str(rng.uniform(-2,100))]), declared=rng.choice(['','100','100.01','142.86',str(rng.uniform(0,10000))]), flags=rng.choices(['X','R','H','D0','D5','D10','D15','D100','D120','Q','d10','D-1'], k=rng.randrange(8))))\nfor order in orders:\n line = '|'.join([order['id'],order['date'],order['zone'],order['kg'],order['declared'],','.join(order['flags'])])\n reference = new if order['date'] >= '2027-01-01' else old\n expected = reference['calc'](line)\n assert quote(order) == expected, (order, quote(order), expected)\nprint('All fields matched the instrumented reference for',len(orders),'orders.')\nassert len(json.loads(Path('answer.json').read_text())['quotes']) == 70\nPY\"", "aggregated_output": "All fields matched the instrumented reference for 10070 orders.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Created `solve.py` and generated `answer.json`.\n\nVerified all fields against an instrumented legacy reference across 10,070 supplied and randomized orders, including the 2027 rules. `solve.py` uses only the standard library and has no dependency on `legacy/`.\n\nRun: `python3 solve.py input.json answer.json`"}}
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