Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Anchor drag alarm from a GPS track
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.0077
- Wall time
- 39s
- Human step-ins
- 0
- Outcome
- Partial
Compare with other models on this task →
Superseded.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
# Anchor drag alarm from a GPS track `input.json` (synthetic, not for real anchor watch): `anchor` [lat, lon], `rode_m`, `depth_m`, `bow_height_m`, `gps_offset_m`, `margin_m`, `consecutive` N, and `track` (CSV `t,lat,lon`, t in seconds). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `anchor.svg`. Fresh inputs are used too. ## Rules 1. Alarm radius = sqrt(rode^2 - (depth + bow_height)^2) (0 if rode <= depth + bow_height) + gps_offset + margin. 2. Track rows: skip the header. A row is **bad** (counted, skipped) if it has fewer than 3 fields, t is not an integer, lat/lon is not a finite number in range [-90, 90] / [-180, 180], or t is not greater than the last *valid* row's t. 3. Distance from the anchor (metres): R = 6371000; m = mean of the anchor and point latitudes; x = radians(dlon) * cos(m) * R (east), y = radians(dlat) * R (north); distance = hypot(x, y). A point is **outside** if distance > radius. 4. An **event** is a maximal run of consecutive valid points that are outside, with length >= N. Report `start_t` (first point), `trigger_t` (Nth point), `end_t` (last point), `max_dist_m`, and `bearing_deg` = atan2(x, y) mod 360 from the anchor to the run's last point. 5. Round the radius to 2 decimals and distances and bearings to 1. ## Visual output: anchor.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<circle id="swing-circle">`, `<polyline id="track">` and an element `id="anchor"`. The drawing itself is not graded. ## answer.json `{"radius_m", "valid_points", "bad_rows", "events": [...], "max_dist_m", "outside_points"}` ## Scoring Hidden checks (partial credit; all must pass): `swing_radius` (within 0.011), `track_cleaning` (exact), `distances`, `alarm_events` (times exact, values within 0.11), `anchor_svg_static` (anchor.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Catenary-free swing radius with offsets, rejecting bad and out-of-order rows against the last valid time, short excursions that must not trigger, and a real drag run with trigger time and bearing. 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. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k07-anchor-drag-alarm (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. Visual output (anchor.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. 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 $0.05, 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_prices.json list price; paid from the Venice DIEM daily allowance. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 4/7. 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.
Output
Metrics
- Estimated cost
- $0.0077
- Wall time
- 39s
- Human step-ins
- 0
- Turns
- 9
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 77,880 | 1,529 | $0.0077 |
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. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_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.
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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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\nimport math\\nimport sys\\n\\ndef calculate_swing_radius(rode_m, depth_m, bow_height_m, gps_offset_m, margin_m):\\n depth_plus_bow = depth_m + bow_height_m\\n if rode_m <= depth_plus_bow:\\n return 0.0\\n radius = math.sqrt(rode_m**2 - depth_plus_bow**2) + gps_offset_m + margin_m\\n return round(radius, 2)\\n\\ndef clean_track(track_data):\\n cleaned_track = []\\n last_valid_t = -math.inf\\n bad_rows = 0\\n for row in track_data[1:]:\\n try:\\n t, lat, lon = row.split(',')\\n t = int(t)\\n lat = float(lat)\\n lon = float(lon)\\n if not (-90 <= lat <= 90) or not (-180 <= lon <= 180):\\n bad_rows += 1\\n continue\\n if t <= last_valid_t:\\n bad_rows += 1\\n continue\\n last_valid_t = t\\n cleaned_track.append((t, lat, lon))\\n except:\\n bad_rows += 1\\n return cleaned_track, bad_rows\\n\\ndef calculate_distances(anchor, cleaned_track):\\n R = 6371000 # Earth radius in meters\\n distances = []\\n bearings = []\\n for t, lat, lon in cleaned_track:\\n dlat = math.radians(lat - anchor[0])\\n dlon = math.radians(lon - anchor[1])\\n m = math.radians((anchor[0] + lat) / 2)\\n x = dlon * math.cos(m) * R\\n y = dlat * R\\n distance = math.hypot(x, y)\\n distances.append(distance)\\n bearing = math.degrees(math.atan2(y, x)) % 360\\n bearings.append(bearing)\\n return distances, bearings\\n\\ndef find_events(distances, radius, consecutive):\\n events = []\\n i = 0\\n n = len(distances)\\n while i < n:\\n if distances[i] > radius:\\n start_i = i\\n while i < n and distances[i] > radius:\\n i += 1\\n if i - start_i >= consecutive:\\n event = {\\n 'start_t': cleaned_track[start_i][0],\\n 'trigger_t': cleaned_track[start_i + consecutive - 1][0],\\n 'end_t': cleaned_track[i - 1][0],\\n 'max_dist_m': round(max(distances[start_i:i]), 1),\\n 'bearing_deg': round(bearings[i - 1], 1)\\n }\\n events.append(event)\\n else:\\n i += 1\\n return events\\n\\ndef generate_svg(anchor, cleaned_track, events, radius):\\n svg_content = f\\\"\\\"\\\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n <svg width=\\\"400\\\" height=\\\"400\\\" viewBox=\\\"-200 -200 400 400\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n <circle id=\\\"swing-circle\\\" cx=\\\"0\\\" cy=\\\"0\\\" r=\\\"{radius}\\\" fill=\\\"none\\\" stroke=\\\"blue\\\" stroke-width=\\\"1\\\"/>\\n <polyline id=\\\"track\\\" fill=\\\"none\\\" stroke=\\\"green\\\" stroke-width=\\\"1\\\" points=\\\"\\\"\\n >\\n \\\"\\\"\\\"\\n for t, lat, lon in cleaned_track:\\n x = (lon - anchor[1]) * 100000\\n y = (lat - anchor[0]) * 100000\\n svg_content += f\\\"{x},{y} \\\"\\n svg_content += \\\"\\\"\\\"\\n </polyline>\\n <circle id=\\\"anchor\\\" cx=\\\"0\\\" cy=\\\"0\\\" r=\\\"5\\\" fill=\\\"red\\\"/>\\n </svg>\\n \\\"\\\"\\\"\\n return svg_content\\n\\nif __name__ == \\\"__main__\\\":\\n if len(sys.argv) != 3:\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\n sys.exit(1)\\n\\n with open(sys.argv[1], 'r') as f:\\n data = json.load(f)\\n\\n anchor = data['anchor']\\n rode_m = data['rode_m']\\n depth_m = data['depth_m']\\n bow_height_m = data['bow_height_m']\\n gps_offset_m = data['gps_offset_m']\\n margin_m = data['margin_m']\\n consecutive = data['consecutive']\\n track_file = data['track']\\n\\n with open(track_file, 'r') as f:\\n track_data = f.read().splitlines()\\n\\n radius = calculate_swing_radius(rode_m, depth_m, bow_height_m, gps_offset_m, margin_m)\\n cleaned_track, bad_rows = clean_track(track_data)\\n distances, bearings = calculate_distances(anchor, cleaned_track)\\n events = find_events(distances, radius, consecutive)\\n\\n max_dist_m = round(max(distances), 1) if distances else 0.0\\n outside_points = sum(1 for d in distances if d > radius)\\n\\n answer = {\\n 'radius_m': radius,\\n 'valid_points': len(cleaned_track),\\n 'bad_rows': bad_rows,\\n 'events': events,\\n 'max_dist_m': max_dist_m,\\n 'outside_points': outside_points\\n }\\n\\n with open(sys.argv[2], 'w') as f:\\n json.dump(answer, f)\\n\\n svg_content = generate_svg(anchor, cleaned_track, events, radius)\\n with open('anchor.svg', 'w') as f:\\n f.write(svg_content)\"}"
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