Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Boat fuel range with a reserve over a planned trip
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-26 · by @bobsiopener
- Estimated cost
- $0.0017
- Wall time
- 16s
- 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Superseded.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
# Boat fuel range with a reserve over a planned trip `input.json` (synthetic; not for real trip planning): `tank_l`, `fuel_l` on board, `reserve_pct` (of the tank), `burn_table` (rows `rpm`, `speed_kn` through the water, `lph` litres per hour; not sorted), and `legs` (`distance_nm`, `rpm`, `current_kn`: positive = with the boat, negative = against). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh inputs are used too. ## Rules 1. Speed and burn at an rpm: sort the table by rpm, interpolate each linearly, clamp to the first/last row outside the range. 2. Each leg in order: SOG = speed + current_kn. If SOG <= 0 the leg cannot be run: append `{"sog_kn", "hours": null, "fuel_l": null, "remaining_l"}` and stop processing legs (this leg is the dry leg if none yet, with `dry_after_nm_into_leg` 0 if none yet). Otherwise hours = distance / SOG, fuel = lph * hours, remaining -= fuel (remaining may go negative; keep going). 3. `reserve_l` = tank * pct / 100. `reserve_breach_leg` = index (0-based) of the first leg after which remaining < reserve, or null. `runs_dry_leg` = first leg whose fuel exceeds the fuel left at its start, and `dry_after_nm_into_leg` = fuel left at its start / lph * SOG; both null if never. 4. Economy: the table row with the greatest speed_kn / lph (compared at 6 decimals; ties -> lower rpm). `economy_range_nm` = max(0, fuel_l - reserve) * that ratio (still water). Round speeds and hours to 3, litres and nm to 2, `economy_nm_per_l` to 4. ## answer.json `{"reserve_l", "legs": [{"sog_kn", "hours", "fuel_l", "remaining_l"}, ...], "reserve_breach_leg", "runs_dry_leg", "dry_after_nm_into_leg", "economy_rpm", "economy_nm_per_l", "economy_range_nm"}` ## Scoring Hidden checks (partial credit; all must pass): `leg_speeds_and_burn` (first three legs), `remaining_fuel`, `reserve_and_dry`, `economy`, `fresh_run_1`, `fresh_run_2`. Tolerance 0.011 (0.0002 for nm per litre). 60-second limit per run.
Unsorted burn table with clamped interpolation, current added to speed (including a leg where the boat cannot make way), separate reserve-breach and run-dry legs, and the economy-rpm tie rule. Original benchmark task written for usecaserank.com (not based on a specific public post). 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. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 10 case k11-fuel-range-reserve (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 $0.0144, 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, $0.0144 run cap). Score: 1/6. 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
- $0.0017
- Wall time
- 16s
- Human step-ins
- 0
- Turns
- 7
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 14,637 | 1,241 | $0.0017 |
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. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 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
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"wall_seconds": 15.8,
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"result": "{\n \"tank_l\": 300,\n \"fuel_l\": 254,\n \"reserve_pct\": 20,\n \"burn_table\": [\n {\n \"rpm\": 1000,\n \"speed_kn\": 2.83,\n \"lph\": 4.9\n },\n {\n \"rpm\": 1400,\n \"speed_kn\": 3.96,\n \"lph\": 6.16\n },\n {\n \"rpm\": 3000,\n \"speed_kn\": 8.15,\n \"lph\": 19.66\n },\n {\n \"rpm\": 2200,\n \"speed_kn\": 6.22,\n \"lph\": 10.99\n },\n {\n \"rpm\": 2600,\n \"speed_kn\": 7.36,\n \"lph\": 14.79\n },\n {\n \"rpm\": 1800,\n \"speed_kn\": 5.09,\n \"lph\": 8.15\n },\n {\n \"rpm\": 3400,\n \"speed_kn\": 10.15,\n \"lph\": 25.68\n }\n ],\n \"legs\": [\n {\n \"distance_nm\": 17.7,\n \"rpm\": 2100,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 19.0,\n \"rpm\": 3300,\n \"current_kn\": 0.8\n },\n {\n \"distance_nm\": 20.1,\n \"rpm\": 2100,\n \"current_kn\": -1.0\n },\n {\n \"distance_nm\": 20.9,\n \"rpm\": 1800,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 27.4,\n \"rpm\": 2500,\n \"current_kn\": -1.1\n },\n {\n \"distance_nm\": 14.1,\n \"rpm\": 3000,\n \"current_kn\": -1.6\n },\n {\n \"distance_nm\": 23.6,\n \"rpm\": 2500,\n \"current_kn\": 1.1\n }\n ]\n}"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\r\\nimport sys\\r\\n\\r\\ndef interpolate_linearly(table, rpm):\\r\\n \\\"\\\"\\\"Sort the table by rpm and interpolate linearly.\\\"\\\"\\\"\\r\\n table_sorted = sorted(table, key=lambda x: x['rpm'])\\r\\n for i in range(len(table_sorted)):\\r\\n if table_sorted[i]['rpm'] == rpm:\\r\\n return table_sorted[i]['speed_kn'], table_sorted[i]['lph']\\r\\n # If rpm is not found, clamp to the first or last row\\r\\n if rpm < table_sorted[0]['rpm']:\\r\\n return table_sorted[0]['speed_kn'], table_sorted[0]['lph']\\r\\n else:\\r\\n return table_sorted[-1]['speed_kn'], table_sorted[-1]['lph']\\r\\n\\r\\ndef calculate_leg(fuel_remaining, distance_nm, rpm, current_kn, burn_table):\\r\\n \\\"\\\"\\\"Calculate the details for a single leg of the trip.\\\"\\\"\\\"\\r\\n speed_kn, lph = interpolate_linearly(burn_table, rpm)\\r\\n sog_kn = speed_kn + current_kn\\r\\n if sog_kn <= 0:\\r\\n return {\\\"sog_kn\\\": round(sog_kn, 3), \\\"hours\\\": None, \\\"fuel_l\\\": None, \\\"remaining_l\\\": fuel_remaining}\\r\\n hours = distance_nm / sog_kn\\r\\n fuel_l = lph * hours\\r\\n remaining_l = fuel_remaining - fuel_l\\r\\n return {\\\"sog_kn\\\": round(sog_kn, 3), \\\"hours\\\": round(hours, 3), \\\"fuel_l\\\": round(fuel_l, 2), \\\"remaining_l\\\": round(remaining_l, 2)}\\r\\n\\r\\ndef find_economy(burn_table):\\r\\n \\\"\\\"\\\"Find the most economical RPM in the burn table.\\\"\\\"\\\"\\r\\n max_ratio = -1\\r\\n best_rpm = None\\r\\n for row in burn_table:\\r\\n ratio = row['speed_kn'] / row['lph']\\r\\n if ratio > max_ratio:\\r\\n max_ratio = ratio\\r\\n best_rpm = row['rpm']\\r\\n return best_rpm, round(max_ratio, 4)\\r\\n\\r\\ndef main():\\r\\n \\\"\\\"\\\"Main function to read input, process data, and write output.\\\"\\\"\\\"\\r\\n with open(sys.argv[1], 'r') as f:\\r\\n data = json.load(f)\\r\\n\\r\\n tank_l = data['tank_l']\\r\\n fuel_l = data['fuel_l']\\r\\n reserve_pct = data['reserve_pct']\\r\\n burn_table = data['burn_table']\\r\\n legs = data['legs']\\r\\n\\r\\n reserve_l = round(tank_l * reserve_pct / 100, 2)\\r\\n fuel_remaining = fuel_l\\r\\n legs_result = []\\r\\n reserve_breach_leg = None\\r\\n runs_dry_leg = None\\r\\n dry_after_nm_into_leg = None\\r\\n\\r\\n for i, leg in enumerate(legs):\\r\\n result = calculate_leg(fuel_remaining, leg['distance_nm'], leg['rpm'], leg['current_kn'], burn_table)\\r\\n legs_result.append(result)\\r\\n if result['hours'] is None:\\r\\n runs_dry_leg = i\\r\\n dry_after_nm_into_leg = 0\\r\\n break\\r\\n fuel_remaining = result['remaining_l']\\r\\n if fuel_remaining < reserve_l and reserve_breach_leg is None:\\r\\n reserve_breach_leg = i\\r\\n\\r\\n economy_rpm, economy_nm_per_l = find_economy(burn_table)\\r\\n economy_range_nm = round(max(0, fuel_l - reserve_l) * economy_nm_per_l, 2)\\r\\n\\r\\n output = {\\r\\n \\\"reserve_l\\\": reserve_l,\\r\\n \\\"legs\\\": legs_result,\\r\\n \\\"reserve_breach_leg\\\": reserve_breach_leg,\\r\\n \\\"runs_dry_leg\\\": runs_dry_leg,\\r\\n \\\"dry_after_nm_into_leg\\\": dry_after_nm_into_leg,\\r\\n \\\"economy_rpm\\\": economy_rpm,\\r\\n \\\"economy_nm_per_l\\\": economy_nm_per_l,\\r\\n \\\"economy_range_nm\\\": economy_range_nm\\r\\n }\\r\\n\\r\\n with open(sys.argv[2], 'w') as f:\\r\\n json.dump(output, f, indent=4)\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n main()\"}"
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"content": "import json\r\nimport sys\r\n\r\ndef interpolate_linearly(table, rpm):\r\n \"\"\"Sort the table by rpm and interpolate linearly.\"\"\"\r\n table_sorted = sorted(table, key=lambda x: x['rpm'])\r\n for i in range(len(table_sorted)):\r\n if table_sorted[i]['rpm'] == rpm:\r\n return table_sorted[i]['speed_kn'], table_sorted[i]['lph']\r\n # If rpm is not found, clamp to the first or last row\r\n if rpm < table_sorted[0]['rpm']:\r\n return table_sorted[0]['speed_kn'], table_sorted[0]['lph']\r\n else:\r\n return table_sorted[-1]['speed_kn'], table_sorted[-1]['lph']\r\n\r\ndef calculate_leg(fuel_remaining, distance_nm, rpm, current_kn, burn_table):\r\n \"\"\"Calculate the details for a single leg of the trip.\"\"\"\r\n speed_kn, lph = interpolate_linearly(burn_table, rpm)\r\n sog_kn = speed_kn + current_kn\r\n if sog_kn <= 0:\r\n return {\"sog_kn\": round(sog_kn, 3), \"hours\": None, \"fuel_l\": None, \"remaining_l\": fuel_remaining}\r\n hours = distance_nm / sog_kn\r\n fuel_l = lph * hours\r\n remaining_l = fuel_remaining - fuel_l\r\n return {\"sog_kn\": round(sog_kn, 3), \"hours\": round(hours, 3), \"fuel_l\": round(fuel_l, 2), \"remaining_l\": round(remaining_l, 2)}\r\n\r\ndef find_economy(burn_table):\r\n \"\"\"Find the most economical RPM in the burn table.\"\"\"\r\n max_ratio = -1\r\n best_rpm = None\r\n for row in burn_table:\r\n ratio = row['speed_kn'] / row['lph']\r\n if ratio > max_ratio:\r\n max_ratio = ratio\r\n best_rpm = row['rpm']\r\n return best_rpm, round(max_ratio, 4)\r\n\r\ndef main():\r\n \"\"\"Main function to read input, process data, and write output.\"\"\"\r\n with open(sys.argv[1], 'r') as f:\r\n data = json.load(f)\r\n\r\n tank_l = data['tank_l']\r\n fuel_l = data['fuel_l']\r\n reserve_pct = data['reserve_pct']\r\n burn_table = data['burn_table']\r\n legs = data['legs']\r\n\r\n reserve_l = round(tank_l * reserve_pct / 100, 2)\r\n fuel_remaining = fuel_l\r\n legs_result = []\r\n reserve_breach_leg = None\r\n runs_dry_leg = None\r\n dry_after_nm_into_leg = None\r\n\r\n for i, leg in enumerate(legs):\r\n result = calculate_leg(fuel_remaining, leg['distance_nm'], leg['rpm'], leg['current_kn'], burn_table)\r\n legs_result.append(result)\r\n if result['hours'] is None:\r\n runs_dry_leg = i\r\n dry_after_nm_into_leg = 0\r\n break\r\n fuel_remaining = result['remaining_l']\r\n if fuel_remaining < reserve_l and reserve_breach_leg is None:\r\n reserve_breach_leg = i\r\n\r\n economy_rpm, economy_nm_per_l = find_economy(burn_table)\r\n economy_range_nm = round(max(0, fuel_l - reserve_l) * economy_nm_per_l, 2)\r\n\r\n output = {\r\n \"reserve_l\": reserve_l,\r\n \"legs\": legs_result,\r\n \"reserve_breach_leg\": reserve_breach_leg,\r\n \"runs_dry_leg\": runs_dry_leg,\r\n \"dry_after_nm_into_leg\": dry_after_nm_into_leg,\r\n \"economy_rpm\": economy_rpm,\r\n \"economy_nm_per_l\": economy_nm_per_l,\r\n \"economy_range_nm\": economy_range_nm\r\n }\r\n\r\n with open(sys.argv[2], 'w') as f:\r\n json.dump(output, f, indent=4)\r\n\r\nif __name__ == \"__main__\":\r\n main()"
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