Mistral Small 3.2 24B Instruct (Venice) (thinking on) on AIS collision risk: closest point of approach and alert levels
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-29 · by @bobsiopener
- Cost
- $0.0023
- Wall time
- 48s
- Human step-ins
- 0
- Outcome
- Fail
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Rescored on 2026-10-29. Previous score: 17.5 / 100, outcome: fail. See the previous run.
Summary
Exact prompt
# AIS collision risk: CPA, TCPA and alert levels `input.json` (synthetic; not for collision avoidance): `own` ship (`lat`, `lon`, `cog` degrees true, `sog` knots), `targets` (each `mmsi`, `lat`, `lon`, `cog`, `sog`), thresholds `cpa_danger_nm`, `cpa_caution_nm`, `tcpa_max_min`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `radar.svg`. Fresh inputs are used too. ## Rules 1. Skip a target (list its mmsi in `skipped`, ascending) if lat, lon, cog or sog is missing/null, sog >= 102.3 (AIS "not available"), or cog >= 360 (not available). Keep the rest in input order. 2. Local flat frame around own ship in nm: x = (lon - own lon) * 60 * cos(own lat), y = (lat - own lat) * 60. Velocity of a ship = (sog * sin(cog), sog * cos(cog)) knots. 3. Relative position r = target - own, relative velocity v = v_target - v_own. If |v|^2 < 1e-9, TCPA = 0. Otherwise TCPA (hours) = max(0, -(r . v) / |v|^2). CPA = |r + v * TCPA|. 4. `range_nm` = |r|, `bearing_deg` = atan2(x, y) mod 360 (true bearing from own ship), `tcpa_min` = TCPA * 60. 5. Level: `danger` if CPA <= cpa_danger and tcpa_min <= tcpa_max; else `caution` if CPA <= cpa_caution and tcpa_min <= tcpa_max; else `none`. 6. `alerts`: mmsi of danger then caution targets, each group sorted by tcpa_min then mmsi. Round range/CPA to 3 decimals, bearing to 1, tcpa_min to 2. ## Visual output: radar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="own-ship"` and one element `id="target-<mmsi>"` for every non-skipped target. The drawing itself is not graded. ## answer.json `{"targets": [{"mmsi", "range_nm", "bearing_deg", "cpa_nm", "tcpa_min", "level"}, ...], "skipped": [...], "alerts": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `filtering`, `range_bearing`, `cpa_tcpa`, `levels_and_alerts`, `radar_svg_static` (radar.svg exists, follows the static rules, has `own-ship` and every `target-<mmsi>`), `fresh_run_1`, `fresh_run_2`. Tolerances: distances 0.002 nm, bearings 0.11, minutes 0.011 (fresh runs 0.011 for all numbers). 60-second limit per run.
AIS not-available sentinels (sog 102.3, cog 360), a stationary target, diverging targets with TCPA clamped to 0, longitude scaling, near-threshold crossing targets, and a two-key alert sort. Headless, offline run on a reproducible fixture. Cost shown is measured tokens x Venice list price and matches the amount Venice reported billing.
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k08-ais-cpa-tcpa (offline fixture). Prepared with the case's setup script; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored automatically with the scout's scorer. 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 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.05, 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 list price; paid from the Venice DIEM daily allowance. Visual output (radar.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. This run replaces an earlier run of the same case and model that was cut off by the nightly Venice budget (its per-run cap had been shrunk to fit the leftover allowance). It is a new run, not a rescore. It ran at the normal $0.05 per-run cap. Score: 2/7. Cost is measured per-call tokens x Venice list price and matches the amount Venice reported billing. Input tokens include cached reads.
Output
Metrics
- Cost
- $0.0023
- Wall time
- 48s
- 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 | 19,488 | 1,870 | $0.0023 |
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. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. 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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"result": "{\n \"own\": {\n \"lat\": 46.95882,\n \"lon\": -127.13341,\n \"cog\": 298,\n \"sog\": 4.2\n },\n \"targets\": [\n {\n \"mmsi\": 795070736,\n \"lat\": 46.83549,\n \"lon\": -127.20639,\n \"cog\": 14.0,\n \"sog\": 41.3\n },\n {\n \"mmsi\": 276139249,\n \"lat\": 47.04512,\n \"lon\": -127.14288,\n \"cog\": 274.7,\n \"sog\": 14.0\n },\n {\n \"mmsi\": 660159254,\n \"lat\": 46.94775,\n \"lon\": -127.11005,\n \"cog\": 158.2,\n \"sog\": 102.3\n },\n {\n \"mmsi\": 265918776,\n \"lat\": 47.00577,\n \"lon\": -127.29469,\n \"cog\": 118.4,\n \"sog\": 65.0\n },\n {\n \"mmsi\": 743277030,\n \"lat\": 46.96679,\n \"lon\": -126.9877,\n \"cog\": 276.4,\n \"sog\": 15.0\n },\n {\n \"mmsi\": 562375419,\n \"lat\": 46.94375,\n \"lon\": -127.0556,\n \"cog\": 298.3,\n \"sog\": 11.9\n },\n {\n \"mmsi\": 613463677,\n \"lat\": 47.0609,\n \"lon\": -127.13599,\n \"cog\": 187.0,\n \"sog\": 26.0\n },\n {\n \"mmsi\": 340778672,\n \"lat\": 46.96234,\n \"lon\": -127.07372,\n \"cog\": 360.0,\n \"sog\": 11.2\n },\n {\n \"mmsi\": 286629533,\n \"lat\": 47.0064,\n \"lon\": -127.19976,\n \"cog\": 214.3,\n \"sog\": 0.0\n },\n {\n \"mmsi\": 637679438,\n \"lat\": 46.90575,\n \"lon\": -127.01879,\n \"cog\": 27.4,\n \"sog\": 19.4\n },\n {\n \"mmsi\": 327721356,\n \"lat\": 46.9028,\n \"lon\": -127.06074,\n \"cog\": 204.2,\n \"sog\": 10.7\n },\n {\n \"mmsi\": 777617410,\n \"lat\": 46.84177,\n \"lon\": -127.07896,\n \"cog\": 351.5,\n \"sog\": 8.5\n }\n ],\n \"cpa_danger_nm\": 0.5,\n \"cpa_caution_nm\": 1.0,\n \"tcpa_max_min\": 30\n}"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\r\\nimport math\\r\\n\\r\\ndef filter_targets(targets, own_ship):\\r\\n filtered = []\\r\\n skipped = []\\r\\n for target in targets:\\r\\n if None in [target.get('lat'), target.get('lon'), target.get('cog'), target.get('sog')] or target['sog'] >= 102.3 or target['cog'] >= 360:\\r\\n skipped.append(target['mmsi'])\\r\\n else:\\r\\n filtered.append(target)\\r\\n return filtered, skipped\\r\\n\\r\\ndef calculate_relative_position(target, own_ship):\\r\\n lat1, lon1 = math.radians(own_ship['lat']), math.radians(own_ship['lon'])\\r\\n lat2, lon2 = math.radians(target['lat']), math.radians(target['lon'])\\r\\n x = (lon2 - lon1) * 60 * math.cos(lat1)\\r\\n y = (lat2 - lat1) * 60\\r\\n return x, y\\r\\n\\r\\ndef calculate_velocity(sog, cog):\\r\\n cog_rad = math.radians(cog)\\r\\n vx = sog * math.sin(cog_rad)\\r\\n vy = sog * math.cos(cog_rad)\\r\\n return vx, vy\\r\\n\\r\\ndef calculate_cpa_tcpa(r, v):\\r\\n r_mag_sq = r[0]**2 + r[1]**2\\r\\n v_mag_sq = v[0]**2 + v[1]**2\\r\\n if v_mag_sq < 1e-9:\\r\\n return 0, math.sqrt(r_mag_sq)\\r\\n r_dot_v = r[0] * v[0] + r[1] * v[1]\\r\\n tcpa = max(0, -r_dot_v / v_mag_sq)\\r\\n cpa = math.sqrt(r_mag_sq + 2 * r_dot_v * tcpa + v_mag_sq * tcpa**2)\\r\\n return tcpa, cpa\\r\\n\\r\\ndef calculate_bearing(x, y):\\r\\n return math.degrees(math.atan2(x, y)) % 360\\r\\n\\r\\ndef determine_level(cpa, tcpa, cpa_danger, cpa_caution, tcpa_max):\\r\\n tcpa_min = tcpa * 60\\r\\n if cpa <= cpa_danger and tcpa_min <= tcpa_max:\\r\\n return 'danger', tcpa_min\\r\\n elif cpa <= cpa_caution and tcpa_min <= tcpa_max:\\r\\n return 'caution', tcpa_min\\r\\n else:\\r\\n return 'none', tcpa_min\\r\\n\\r\\ndef generate_svg(targets, own_ship):\\r\\n svg_content = \\\"\\\"\\\"\\r\\n <svg width=\\\"800\\\" height=\\\"800\\\" viewBox=\\\"-400 -400 800 800\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\r\\n <circle id=\\\"own-ship\\\" cx=\\\"0\\\" cy=\\\"0\\\" r=\\\"10\\\" fill=\\\"red\\\" />\\r\\n \\\"\\\"\\\"\\r\\n for target in targets:\\r\\n x, y = calculate_relative_position(target, own_ship)\\r\\n svg_content += f\\\" <circle id=\\\\\\\"target-{target['mmsi']}\\\\\\\" cx='{x}' cy='{y}' r='5' fill='blue' />\\\\n\\\"\\r\\n svg_content += \\\"\\\"\\\"\\r\\n </svg>\\r\\n \\\"\\\"\\\"\\r\\n return svg_content\\r\\n\\r\\ndef solve(input_file, output_file):\\r\\n with open(input_file) as f:\\r\\n data = json.load(f)\\r\\n\\r\\n own_ship = data['own']\\r\\n targets = data['targets']\\r\\n cpa_danger = data['cpa_danger_nm']\\r\\n cpa_caution = data['cpa_caution_nm']\\r\\n tcpa_max = data['tcpa_max_min']\\r\\n\\r\\n filtered_targets, skipped = filter_targets(targets, own_ship)\\r\\n v_own_x, v_own_y = calculate_velocity(own_ship['sog'], own_ship['cog'])\\r\\n\\r\\n result = {\\r\\n 'targets': [],\\r\\n 'skipped': sorted(skipped),\\r\\n 'alerts': {\\r\\n 'danger': [],\\r\\n 'caution': []\\r\\n }\\r\\n }\\r\\n\\r\\n for target in filtered_targets:\\r\\n r_x, r_y = calculate_relative_position(target, own_ship)\\r\\n v_target_x, v_target_y = calculate_velocity(target['sog'], target['cog'])\\r\\n v_rel_x, v_rel_y = v_target_x - v_own_x, v_target_y - v_own_y\\r\\n tcpa, cpa = calculate_cpa_tcpa((r_x, r_y), (v_rel_x, v_rel_y))\\r\\n range_nm = math.sqrt(r_x**2 + r_y**2)\\r\\n bearing_deg = calculate_bearing(r_x, r_y)\\r\\n level, tcpa_min = determine_level(cpa, tcpa, cpa_danger, cpa_caution, tcpa_max)\\r\\n\\r\\n target_result = {\\r\\n 'mmsi': target['mmsi'],\\r\\n 'range_nm': round(range_nm, 3),\\r\\n 'bearing_deg': round(bearing_deg, 1),\\r\\n 'cpa_nm': round(cpa, 3),\\r\\n 'tcpa_min': round(tcpa_min, 2),\\r\\n 'level': level\\r\\n }\\r\\n result['targets'].append(target_result)\\r\\n\\r\\n if level == 'danger':\\r\\n result['alerts']['danger'].append((target['mmsi'], tcpa_min, target['mmsi']))\\r\\n elif level == 'caution':\\r\\n result['alerts']['caution'].append((target['mmsi'], tcpa_min, target['mmsi']))\\r\\n\\r\\n result['alerts']['danger'].sort(key=lambda x: (x[1], x[2]))\\r\\n result['alerts']['caution'].sort(key=lambda x: (x[1], x[2]))\\r\\n result['alerts']['danger'] = [x[0] for x in result['alerts']['danger']]\\r\\n result['alerts']['caution'] = [x[0] for x in result['alerts']['caution']]\\r\\n\\r\\n with open(output_file, 'w') as f:\\r\\n json.dump(result, f, indent=4)\\r\\n\\r\\n svg_content = generate_svg(filtered_targets, own_ship)\\r\\n with open('radar.svg', 'w') as f:\\r\\n f.write(svg_content)\\r\\n\\r\\nif __name__ == '__main__':\\r\\n import sys\\r\\n if len(sys.argv) != 3:\\r\\n print(\\\"Usage: python solve.py input.json output.json\\\")\\r\\n sys.exit(1)\\r\\n input_file = sys.argv[1]\\r\\n output_file = sys.argv[2]\\r\\n solve(input_file, output_file)\"}"
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"content": "import json\r\nimport math\r\n\r\ndef filter_targets(targets, own_ship):\r\n filtered = []\r\n skipped = []\r\n for target in targets:\r\n if None in [target.get('lat'), target.get('lon'), target.get('cog'), target.get('sog')] or target['sog'] >= 102.3 or target['cog'] >= 360:\r\n skipped.append(target['mmsi'])\r\n else:\r\n filtered.append(target)\r\n return filtered, skipped\r\n\r\ndef calculate_relative_position(target, own_ship):\r\n lat1, lon1 = math.radians(own_ship['lat']), math.radians(own_ship['lon'])\r\n lat2, lon2 = math.radians(target['lat']), math.radians(target['lon'])\r\n x = (lon2 - lon1) * 60 * math.cos(lat1)\r\n y = (lat2 - lat1) * 60\r\n return x, y\r\n\r\ndef calculate_velocity(sog, cog):\r\n cog_rad = math.radians(cog)\r\n vx = sog * math.sin(cog_rad)\r\n vy = sog * math.cos(cog_rad)\r\n return vx, vy\r\n\r\ndef calculate_cpa_tcpa(r, v):\r\n r_mag_sq = r[0]**2 + r[1]**2\r\n v_mag_sq = v[0]**2 + v[1]**2\r\n if v_mag_sq < 1e-9:\r\n return 0, math.sqrt(r_mag_sq)\r\n r_dot_v = r[0] * v[0] + r[1] * v[1]\r\n tcpa = max(0, -r_dot_v / v_mag_sq)\r\n cpa = math.sqrt(r_mag_sq + 2 * r_dot_v * tcpa + v_mag_sq * tcpa**2)\r\n return tcpa, cpa\r\n\r\ndef calculate_bearing(x, y):\r\n return math.degrees(math.atan2(x, y)) % 360\r\n\r\ndef determine_level(cpa, tcpa, cpa_danger, cpa_caution, tcpa_max):\r\n tcpa_min = tcpa * 60\r\n if cpa <= cpa_danger and tcpa_min <= tcpa_max:\r\n return 'danger', tcpa_min\r\n elif cpa <= cpa_caution and tcpa_min <= tcpa_max:\r\n return 'caution', tcpa_min\r\n else:\r\n return 'none', tcpa_min\r\n\r\ndef generate_svg(targets, own_ship):\r\n svg_content = \"\"\"\r\n <svg width=\"800\" height=\"800\" viewBox=\"-400 -400 800 800\" xmlns=\"http://www.w3.org/2000/svg\">\r\n <circle id=\"own-ship\" cx=\"0\" cy=\"0\" r=\"10\" fill=\"red\" />\r\n \"\"\"\r\n for target in targets:\r\n x, y = calculate_relative_position(target, own_ship)\r\n svg_content += f\" <circle id=\\\"target-{target['mmsi']}\\\" cx='{x}' cy='{y}' r='5' fill='blue' />\\n\"\r\n svg_content += \"\"\"\r\n </svg>\r\n \"\"\"\r\n return svg_content\r\n\r\ndef solve(input_file, output_file):\r\n with open(input_file) as f:\r\n data = json.load(f)\r\n\r\n own_ship = data['own']\r\n targets = data['targets']\r\n cpa_danger = data['cpa_danger_nm']\r\n cpa_caution = data['cpa_caution_nm']\r\n tcpa_max = data['tcpa_max_min']\r\n\r\n filtered_targets, skipped = filter_targets(targets, own_ship)\r\n v_own_x, v_own_y = calculate_velocity(own_ship['sog'], own_ship['cog'])\r\n\r\n result = {\r\n 'targets': [],\r\n 'skipped': sorted(skipped),\r\n 'alerts': {\r\n 'danger': [],\r\n 'caution': []\r\n }\r\n }\r\n\r\n for target in filtered_targets:\r\n r_x, r_y = calculate_relative_position(target, own_ship)\r\n v_target_x, v_target_y = calculate_velocity(target['sog'], target['cog'])\r\n v_rel_x, v_rel_y = v_target_x - v_own_x, v_target_y - v_own_y\r\n tcpa, cpa = calculate_cpa_tcpa((r_x, r_y), (v_rel_x, v_rel_y))\r\n range_nm = math.sqrt(r_x**2 + r_y**2)\r\n bearing_deg = calculate_bearing(r_x, r_y)\r\n level, tcpa_min = determine_level(cpa, tcpa, cpa_danger, cpa_caution, tcpa_max)\r\n\r\n target_result = {\r\n 'mmsi': target['mmsi'],\r\n 'range_nm': round(range_nm, 3),\r\n 'bearing_deg': round(bearing_deg, 1),\r\n 'cpa_nm': round(cpa, 3),\r\n 'tcpa_min': round(tcpa_min, 2),\r\n 'level': level\r\n }\r\n result['targets'].append(target_result)\r\n\r\n if level == 'danger':\r\n result['alerts']['danger'].append((target['mmsi'], tcpa_min, target['mmsi']))\r\n elif level == 'caution':\r\n result['alerts']['caution'].append((target['mmsi'], tcpa_min, target['mmsi']))\r\n\r\n result['alerts']['danger'].sort(key=lambda x: (x[1], x[2]))\r\n result['alerts']['caution'].sort(key=lambda x: (x[1], x[2]))\r\n result['alerts']['danger'] = [x[0] for x in result['alerts']['danger']]\r\n result['alerts']['caution'] = [x[0] for x in result['alerts']['caution']]\r\n\r\n with open(output_file, 'w') as f:\r\n json.dump(result, f, indent=4)\r\n\r\n svg_content = generate_svg(filtered_targets, own_ship)\r\n with open('radar.svg', 'w') as f:\r\n f.write(svg_content)\r\n\r\nif __name__ == '__main__':\r\n import sys\r\n if len(sys.argv) != 3:\r\n print(\"Usage: python solve.py input.json output.json\")\r\n sys.exit(1)\r\n input_file = sys.argv[1]\r\n output_file = sys.argv[2]\r\n solve(input_file, output_file)"
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