Gemini 4 Argon (effort medium · thinking on) on Catch log checked against a made-up fishing regulations table
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.13
- Wall time
- 2m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Catch log checked against a made-up regulations table `input.json` names `regulations.json` and `catch_log.csv`. All rules, species codes and data are **fictional** (not real fishing regulations). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh files are used too. ## Data - `regulations.json`: `species` maps a code to `aliases`, `season` ["MM-DD", "MM-DD"] inclusive (if start > end the season wraps over New Year), `min_in`, `max_in` (null = no maximum), `bag` (legal kept fish per angler per day for that species); `aggregate_daily` = legal kept fish per angler per day, all species. - `catch_log.csv`: `date` (YYYY-MM-DD), `angler`, `species`, `length`, `unit` (`in` or `cm`, any case), `action` (`kept` or `released`, any case). Process rows in file order; row numbers start at 1 for the first data row. ## Rules (first match wins) 1. Species: trim, case-insensitive match to a code or alias; no match -> `unknown_species` (species null). 2. `released` -> status `released` (never a violation, never counted). 3. Kept fish: length in inches (cm / 2.54 rounded to 2 decimals). Then: not in season -> `out_of_season`; below min -> `undersize`; above max -> `oversize`; angler already has `bag` legal fish of that species that date -> `over_bag`; angler already has `aggregate_daily` legal fish that date -> `over_aggregate`; else `legal` (and it counts toward the limits). Boundaries are legal (equal to min or max, first/last season day). 4. Every status except `legal` and `released` is a violation for that angler. ## answer.json `{"rows": [{"row", "species", "status"}, ...], "violations_by_angler": {angler: n} (only anglers with >0, sorted by name), "legal_kept_by_species": {code: n} (only >0, sorted)}` ## Scoring Hidden checks (partial credit; all must pass): `species_and_release` (species codes and which rows are released), `row_statuses` (every row exact), `summaries`, `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Aliases with odd spacing and case, cm conversion near limits, a season that wraps New Year with boundary dates, slot maximum, and bag/aggregate limits that only count legal fish in log order. 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 Google account via Antigravity CLI, not billed per token).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. species_and_release 2. row_statuses 3. summaries 4. fresh_run_1 5. fresh_run_2
Setup notes: Use Case Scout batch10 case k04-catch-log-regulations (offline fixture). Prepared with batch10/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 5/5. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 8 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.13
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 7
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 102,819 | 6,775 | $0.13 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via
Antigravity CLI 1.4.5 (agy -p), cloud. - It counts as a success only if it meets every success criterion: 1. species_and_release 2. row_statuses 3. summaries 4. fresh_run_1 5. fresh_run_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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{"event": "step_update", "step_update": {"conversation_id": "4c7128a0-6cd5-4e4e-9da3-e93908b5ae25", "step_index": 15, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ightarrow$ `undersize` $"}}
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{"event": "step_update", "step_update": {"conversation_id": "4c7128a0-6cd5-4e4e-9da3-e93908b5ae25", "step_index": 15, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " `oversize` $\\"}}
{"event": "step_update", "step_update": {"conversation_id": "4c7128a0-6cd5-4e4e-9da3-e93908b5ae25", "step_index": 15, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rightarrow$ `over_bag` $"}}
{"event": "step_update", "step_update": {"conversation_id": "4c7128a0-6cd5-4e4e-9da3-e93908b5ae25", "step_index": 15, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\\rightarrow$ `over_aggre"}}
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{"event": "result", "result": {"conversation_id": "4c7128a0-6cd5-4e4e-9da3-e93908b5ae25", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k04/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k04/work/answer.json).\n\n### Summary of Implementation\n- [`solve`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k04/work/solve.py#L14-L114): Reads the input configuration, loads the fictional species regulations, normalizes codes/aliases for case-insensitive lookup, and evaluates each row of the catch log in order against the precedence rules (`unknown_species` $\\rightarrow$ `released` $\\rightarrow$ `out_of_season` $\\rightarrow$ `undersize` $\\rightarrow$ `oversize` $\\rightarrow$ `over_bag` $\\rightarrow$ `over_aggregate` $\\rightarrow$ `legal`).\n- [`in_season`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k04/work/solve.py#L7-L11): Evaluates `MM-DD` date ranges inclusively, including seasons wrapping across New Year.\n", "duration_seconds": 101.0277412, "num_turns": 1, "usage": {"input_tokens": 24939, "output_tokens": 6775, "thinking_tokens": 2786, "cache_read_tokens": 77880, "total_tokens": 31714}}}