Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Catch log checked against a made-up fishing regulations table
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-25 · by @bobsiopener
- Estimated cost
- $0.0025
- Wall time
- 35s
- Human step-ins
- 0
- Outcome
- Fail
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Superseded. This run was rescored on 2026-10-28 and replaced by a newer run. It no longer counts toward rankings, use-case tables or the data export; it is kept here for the record.
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. Inspired by https://x.com/woodpiratecode/status/2085783661774037111 Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x pricing.json list price).
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 batch 10 case k04-catch-log-regulations (offline fixture). Prepared with batch10/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Harness: venice-mini-agent v1.3.1 (/workspace/usecase-runner/venice_agent2.py) via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max_tokens 16000/call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.0312, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x venice_prices.json list price; paid from the Venice DIEM daily allowance. first_try_success corrected to false on 2026-10-25: a harness bug (venice_x/vcc.py finalize did not tie it to success) had set it true on failed runs; scores and measurements unchanged. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.0312 run cap). Score: 1/5. Cost is an ESTIMATE: measured per-call tokens x pricing.json list price (paid from Venice DIEM allowance; Venice billing not itemized). input_tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.0025
- Wall time
- 35s
- Human step-ins
- 0
- Turns
- 8
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 21,559 | 1,941 | $0.0025 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Mistral Small 3.2 24B Instruct (Venice) (venice/mistral-small-3-2-24b-instruct, thinking on) via
venice-mini-agent v1.3.1, 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.
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
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"result": "date,angler,species,length,unit,action\n2026-12-31,ben,blackfish,17.5,in,Kept\n2026-12-31,ana,Striped Bass,29,in,kept\n2026-12-31,ana,BSB,14.5,in,kept\n2026-12-31,ben,fluke,41.9,cm,Kept\n2026-12-31,ben,Summer Flounder,48.3,CM,Kept\n2026-12-31,ana,STB,29,in,Kept\n2026-12-31,ben,Striped Bass,26.5,in,kept\n2026-12-31,ana,blackfish,17,in,Kept\n2026-12-31,ben,Tautog,20,in,Kept\n2026-12-31,ana,BSB,33.0,cm,kept\n2026-09-30,ben,STB,32,in,released\n2026-09-30,ana,Summer Flounder,19.5,in,Kept\n2026-09-30,ana,bluegill,8,in,kept\n2026-09-30,ben,fluke,20,in,Kept\n2026-09-30,ben,blackfish,17.5,in,Kept\n2026-09-30,ben,BSB,14.5,in,released\n2026-09-30,ben,fluke,18,in,Kept\n2026-09-30,ben,blackfish,17,in,Kept\n2026-09-30,ben,BSB,35.6,CM,Kept\n2026-01-20,ana,sea bass,13,in,kept\n2026-01-20,ana,Summer Flounder,21.5,in,kept\n2026-01-20,ben,blackfish,20,in,kept\n2026-01-20,ana,sea bass,35.6,CM,kept\n2026-01-20,ana,bluegill,8,in,kept\n2026-01-20,ana,sea bass,14.5,in,kept\n2026-01-20,ana,blackfish,17.5,in,kept\n2026-01-20,ben,striper,74.9,cm,Kept\n2026-10-20,ben,Tautog,40.6,CM,Kept\n2026-10-20,ben,BSB,14.5,in,released\n2026-10-20,ben,fluke,22,in,kept\n2026-10-20,ana,sea bass,15,in,kept\n2026-10-20,ana, rockfish ,29,in,kept\n2026-10-20,ana,STB,29.5,in,Kept\n2026-10-20,ana,blackfish,17.5,in,kept\n2026-10-20,ben,Striped Bass,30,in,kept\n2026-10-20,ben,bluegill,8,in,kept\n2026-10-20,ana,STB,29.5,in,kept\n2026-10-20,ana,fluke,19.5,in,released\n2026-10-14,ben,STB,29,in,Kept\n2026-10-14,ben,Striped Bass,81.3,CM,Kept\n2026-10-14,ben,fluke,18,in,released\n2026-10-14,ben,sea bass,43.2,cm,released\n2026-10-14,ana,Summer Flounder,22,in,kept\n2026-10-14,ben,fluke,19.5,in,released\n2026-10-14,ben,fluke,18,in,kept\n2026-11-03,ben,fluke,19,in,Kept\n2026-11-03,ana,fluke,19,in,kept\n2026-11-03,ana,sea bass,11.5,in,Kept\n2026-11-03,ana, rockfish ,29.5,in,kept\n2026-11-03,ben,sea bass,13,in,released\n2026-11-03,ben,Tautog,17.5,in,Kept\n2026-11-03,ben,BSB,35.6,CM,kept\n2026-11-03,ben,Tautog,49.5,cm,released\n2026-11-03,ana,Summer Flounder,19,in,kept\n2026-11-03,ana,BSB,41.9,CM,Kept\n"
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"result": "{\n \"species\": {\n \"STB\": {\n \"aliases\": [\n \"striper\",\n \"Striped Bass\",\n \"rockfish\"\n ],\n \"season\": [\n \"04-15\",\n \"12-15\"\n ],\n \"min_in\": 28,\n \"max_in\": 31,\n \"bag\": 2\n },\n \"FLK\": {\n \"aliases\": [\n \"fluke\",\n \"summer flounder\"\n ],\n \"season\": [\n \"05-01\",\n \"09-30\"\n ],\n \"min_in\": 18,\n \"max_in\": null,\n \"bag\": 3\n },\n \"BSB\": {\n \"aliases\": [\n \"black sea bass\",\n \"sea bass\"\n ],\n \"season\": [\n \"05-15\",\n \"12-31\"\n ],\n \"min_in\": 13,\n \"max_in\": null,\n \"bag\": 2\n },\n \"TOG\": {\n \"aliases\": [\n \"tautog\",\n \"blackfish\"\n ],\n \"season\": [\n \"10-15\",\n \"01-31\"\n ],\n \"min_in\": 16,\n \"max_in\": null,\n \"bag\": 2\n }\n },\n \"aggregate_daily\": 3\n}"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\r\\nimport csv\\r\\nfrom datetime import datetime\\r\\n\\r\\ndef parse_date(date_str):\\r\\n return datetime.strptime(date_str, \\\"%Y-%m-%d\\\").date()\\r\\n\\r\\ndef is_in_season(date, season_start, season_end):\\r\\n start_month, start_day = map(int, season_start.split('-'))\\r\\n end_month, end_day = map(int, season_end.split('-'))\\r\\n \\r\\n # Create start and end dates for the season\\r\\n year = date.year\\r\\n season_start_date = datetime(year, start_month, start_day).date()\\r\\n season_end_date = datetime(year, end_month, end_day).date()\\r\\n \\r\\n # Handle the case where the season wraps over the new year\\r\\n if season_start_date > season_end_date:\\r\\n if date >= season_start_date or date <= season_end_date:\\r\\n return True\\r\\n else:\\r\\n if season_start_date <= date <= season_end_date:\\r\\n return True\\r\\n \\r\\n return False\\r\\n\\r\\ndef get_species_code(species_name, regulations):\\r\\n species_name = species_name.lower().strip()\\r\\n for code, data in regulations['species'].items():\\r\\n if code.lower() == species_name or species_name in map(str.lower, data['aliases']):\\r\\n return code\\r\\n return None\\r\\n\\r\\ndef process_catch_log(log_file, regulations):\\r\\n rows = []\\r\\n angler_kept = {}\\r\\n angler_kept_all = {}\\r\\n violations_by_angler = {}\\r\\n legal_kept_by_species = {}\\r\\n \\r\\n with open(log_file, mode='r') as csvfile:\\r\\n reader = csv.DictReader(csvfile)\\r\\n for row_num, row in enumerate(reader, start=1):\\r\\n date = parse_date(row['date'])\\r\\n angler = row['angler']\\r\\n species_name = row['species']\\r\\n length = float(row['length'])\\r\\n unit = row['unit'].lower()\\r\\n action = row['action'].lower()\\r\\n \\r\\n # Determine species code\\r\\n species_code = get_species_code(species_name, regulations)\\r\\n \\r\\n # Process released fish\\r\\n if action == 'released':\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'released'\\r\\n })\\r\\n continue\\r\\n \\r\\n # Convert length to inches\\r\\n if unit == 'cm':\\r\\n length_in = round(length / 2.54, 2)\\r\\n else:\\r\\n length_in = length\\r\\n \\r\\n # Get species data\\r\\n if species_code is None:\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': None,\\r\\n 'status': 'unknown_species'\\r\\n })\\r\\n continue\\r\\n \\r\\n species_data = regulations['species'][species_code]\\r\\n \\r\\n # Check season\\r\\n if not is_in_season(date, species_data['season'][0], species_data['season'][1]):\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'out_of_season'\\r\\n })\\r\\n continue\\r\\n \\r\\n # Check size limits\\r\\n if length_in < species_data['min_in']:\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'undersize'\\r\\n })\\r\\n continue\\r\\n \\r\\n if species_data['max_in'] is not None and length_in > species_data['max_in']:\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'oversize'\\r\\n })\\r\\n continue\\r\\n \\r\\n # Check bag limit for the species\\r\\n if angler not in angler_kept:\\r\\n angler_kept[angler] = {}\\r\\n \\r\\n if species_code not in angler_kept[angler]:\\r\\n angler_kept[angler][species_code] = 0\\r\\n \\r\\n if angler_kept[angler][species_code] >= species_data['bag']:\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'over_bag'\\r\\n })\\r\\n continue\\r\\n \\r\\n # Check aggregate daily limit\\r\\n if angler not in angler_kept_all:\\r\\n angler_kept_all[angler] = 0\\r\\n \\r\\n if angler_kept_all[angler] >= regulations['aggregate_daily']:\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'over_aggregate'\\r\\n })\\r\\n continue\\r\\n \\r\\n # Legal fish\\r\\n rows.append({\\r\\n 'row': row_num,\\r\\n 'species': species_code,\\r\\n 'status': 'legal'\\r\\n })\\r\\n \\r\\n # Update counts\\r\\n angler_kept[angler][species_code] += 1\\r\\n angler_kept_all[angler] += 1\\r\\n \\r\\n if species_code not in legal_kept_by_species:\\r\\n legal_kept_by_species[species_code] = 0\\r\\n \\r\\n legal_kept_by_species[species_code] += 1\\r\\n \\r\\n # Calculate violations by angler\\r\\n for angler, species_counts in angler_kept.items():\\r\\n violations = 0\\r\\n for species_code, count in species_counts.items():\\r\\n violations += count\\r\\n \\r\\n if violations > 0:\\r\\n violations_by_angler[angler] = violations\\r\\n \\r\\n # Sort violations by angler name\\r\\n violations_by_angler = dict(sorted(violations_by_angler.items()))\\r\\n \\r\\n # Sort legal kept by species code\\r\\n legal_kept_by_species = {k: legal_kept_by_species[k] for k in sorted(legal_kept_by_species)}\\r\\n \\r\\n return {\\r\\n 'rows': rows,\\r\\n 'violations_by_angler': violations_by_angler,\\r\\n 'legal_kept_by_species': legal_kept_by_species\\r\\n }\\r\\n\\r\\ndef main():\\r\\n import sys\\r\\n \\r\\n if len(sys.argv) != 3:\\r\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\r\\n sys.exit(1)\\r\\n \\r\\n input_file = sys.argv[1]\\r\\n output_file = sys.argv[2]\\r\\n \\r\\n with open(input_file, 'r') as f:\\r\\n input_data = json.load(f)\\r\\n \\r\\n with open(input_data['regulations'], 'r') as f:\\r\\n regulations = json.load(f)\\r\\n \\r\\n result = process_catch_log(input_data['log'], regulations)\\r\\n \\r\\n with open(output_file, 'w') as f:\\r\\n json.dump(result, f, indent=4)\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n main()\"}"
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"content": "import json\r\nimport csv\r\nfrom datetime import datetime\r\n\r\ndef parse_date(date_str):\r\n return datetime.strptime(date_str, \"%Y-%m-%d\").date()\r\n\r\ndef is_in_season(date, season_start, season_end):\r\n start_month, start_day = map(int, season_start.split('-'))\r\n end_month, end_day = map(int, season_end.split('-'))\r\n \r\n # Create start and end dates for the season\r\n year = date.year\r\n season_start_date = datetime(year, start_month, start_day).date()\r\n season_end_date = datetime(year, end_month, end_day).date()\r\n \r\n # Handle the case where the season wraps over the new year\r\n if season_start_date > season_end_date:\r\n if date >= season_start_date or date <= season_end_date:\r\n return True\r\n else:\r\n if season_start_date <= date <= season_end_date:\r\n return True\r\n \r\n return False\r\n\r\ndef get_species_code(species_name, regulations):\r\n species_name = species_name.lower().strip()\r\n for code, data in regulations['species'].items():\r\n if code.lower() == species_name or species_name in map(str.lower, data['aliases']):\r\n return code\r\n return None\r\n\r\ndef process_catch_log(log_file, regulations):\r\n rows = []\r\n angler_kept = {}\r\n angler_kept_all = {}\r\n violations_by_angler = {}\r\n legal_kept_by_species = {}\r\n \r\n with open(log_file, mode='r') as csvfile:\r\n reader = csv.DictReader(csvfile)\r\n for row_num, row in enumerate(reader, start=1):\r\n date = parse_date(row['date'])\r\n angler = row['angler']\r\n species_name = row['species']\r\n length = float(row['length'])\r\n unit = row['unit'].lower()\r\n action = row['action'].lower()\r\n \r\n # Determine species code\r\n species_code = get_species_code(species_name, regulations)\r\n \r\n # Process released fish\r\n if action == 'released':\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'released'\r\n })\r\n continue\r\n \r\n # Convert length to inches\r\n if unit == 'cm':\r\n length_in = round(length / 2.54, 2)\r\n else:\r\n length_in = length\r\n \r\n # Get species data\r\n if species_code is None:\r\n rows.append({\r\n 'row': row_num,\r\n 'species': None,\r\n 'status': 'unknown_species'\r\n })\r\n continue\r\n \r\n species_data = regulations['species'][species_code]\r\n \r\n # Check season\r\n if not is_in_season(date, species_data['season'][0], species_data['season'][1]):\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'out_of_season'\r\n })\r\n continue\r\n \r\n # Check size limits\r\n if length_in < species_data['min_in']:\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'undersize'\r\n })\r\n continue\r\n \r\n if species_data['max_in'] is not None and length_in > species_data['max_in']:\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'oversize'\r\n })\r\n continue\r\n \r\n # Check bag limit for the species\r\n if angler not in angler_kept:\r\n angler_kept[angler] = {}\r\n \r\n if species_code not in angler_kept[angler]:\r\n angler_kept[angler][species_code] = 0\r\n \r\n if angler_kept[angler][species_code] >= species_data['bag']:\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'over_bag'\r\n })\r\n continue\r\n \r\n # Check aggregate daily limit\r\n if angler not in angler_kept_all:\r\n angler_kept_all[angler] = 0\r\n \r\n if angler_kept_all[angler] >= regulations['aggregate_daily']:\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'over_aggregate'\r\n })\r\n continue\r\n \r\n # Legal fish\r\n rows.append({\r\n 'row': row_num,\r\n 'species': species_code,\r\n 'status': 'legal'\r\n })\r\n \r\n # Update counts\r\n angler_kept[angler][species_code] += 1\r\n angler_kept_all[angler] += 1\r\n \r\n if species_code not in legal_kept_by_species:\r\n legal_kept_by_species[species_code] = 0\r\n \r\n legal_kept_by_species[species_code] += 1\r\n \r\n # Calculate violations by angler\r\n for angler, species_counts in angler_kept.items():\r\n violations = 0\r\n for species_code, count in species_counts.items():\r\n violations += count\r\n \r\n if violations > 0:\r\n violations_by_angler[angler] = violations\r\n \r\n # Sort violations by angler name\r\n violations_by_angler = dict(sorted(violations_by_angler.items()))\r\n \r\n # Sort legal kept by species code\r\n legal_kept_by_species = {k: legal_kept_by_species[k] for k in sorted(legal_kept_by_species)}\r\n \r\n return {\r\n 'rows': rows,\r\n 'violations_by_angler': violations_by_angler,\r\n 'legal_kept_by_species': legal_kept_by_species\r\n }\r\n\r\ndef main():\r\n import sys\r\n \r\n if len(sys.argv) != 3:\r\n print(\"Usage: python solve.py input.json answer.json\")\r\n sys.exit(1)\r\n \r\n input_file = sys.argv[1]\r\n output_file = sys.argv[2]\r\n \r\n with open(input_file, 'r') as f:\r\n input_data = json.load(f)\r\n \r\n with open(input_data['regulations'], 'r') as f:\r\n regulations = json.load(f)\r\n \r\n result = process_catch_log(input_data['log'], regulations)\r\n \r\n with open(output_file, 'w') as f:\r\n json.dump(result, f, indent=4)\r\n\r\nif __name__ == \"__main__\":\r\n main()"
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