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Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Best fishing windows from tide, solunar and weather data

MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3 · cloud · 2026-10-21 · by @bobsiopener

Trust 80%Fully autonomouspartial · 0 retries
Estimated cost
$0.0039
Wall time
38s
Human step-ins
0
Outcome
Partial

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Rescored on 2026-10-28. Previous score: 57.5 / 100, outcome: partial. See the previous run.

Summary

Exact prompt (this run)

# Fishing trip planner from tide, solunar and weather data `input.json` (synthetic) covers 48 hours, hour index h = 0..47 meaning the hour [h, h+1). It has `tide_m` (49 hourly heights at h = 0..48), `wind_kt` and `pressure_hpa` (48 values), `solunar_major` and `solunar_minor` (lists of [start, end] in fractional hours), `daylight` ([sunrise_hour, sunset_hour] for day 0 and day 1, in hour-of-day), and `window_hours` L. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `windows.svg`. Fresh inputs are used too. ## Hourly score - If wind > 20 kt the hour is **unfishable**: score null, ignore the rest. - +3 if any major period overlaps the hour (start < h+1 and end > h); +1.5 if any minor period overlaps (both can apply). - Tide movement d = |tide[h+1] - tide[h]|, rounded to 6 decimals: +2 if d >= 0.3, else +1 if d >= 0.15. - Wind above 10 kt (and <= 20): -1. - Pressure trend dp = pressure[h] - pressure[h-3] (0 for h < 3), rounded to 6 decimals: +1 if dp <= -1.0, -1 if dp >= +1.0. - Outside daylight: -2, where daylight means sunrise <= (h mod 24) < sunset for day h // 24. Round each score to 2 decimals. ## Windows A window starting at s covers hours s..s+L-1, all fishable; its score is the sum, rounded to 2 decimals. Sort all windows by score descending then start ascending, and greedily keep up to 3 that do not overlap any already kept one. ## answer.json `{"hourly_scores": [48 values or null], "unfishable_hours": n, "windows": [{"start", "end" (= start + L), "score"}, ...]}` ## Visual output: windows.svg Static, self-contained SVG (root `<svg>`; no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, `href`/`src` except `#...` or `data:`, CSS `url(http...)`/`@import` or fetches; renders headless) with an element `id="hourly-bars"` (the hourly scores) and one `<rect id="window-N">` for each reported window N = 1, 2, 3 in answer order. The drawing itself is not graded. ## Scoring Hidden checks (partial credit; all must pass): `hourly_scores_day1` (hours 0-23), `hourly_scores_day2` (24-47 plus unfishable count), `top_windows`, `windows_svg_static` (windows.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Tolerance 0.001. 60-second limit per run.

Overlap tests on fractional periods, threshold boundaries placed exactly at 10 and 20 kt, a 3-hour pressure trend with float rounding, daylight by day index, and greedy non-overlapping window selection with ties. 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 the site's price table).

Benchmark code for comparing AI models, not navigation or safety software.

Success criteria: 1. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_svg_static 5. fresh_run_1 6. fresh_run_2

Setup notes: Use Case Scout batch 10 case k03-fishing-window-planner (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. Visual output (windows.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Harness: venice-mini-agent v1.3 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. Harness: venice-mini-agent v1.3 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 3/6. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (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
$0.0039
Wall time
38s
Human step-ins
0
Turns
10
Model usage
ModelInput tokOutput tokCost
MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on37,4941,498$0.0039

Reproduce this run

  1. Use the exact prompt shown above for this run (the use case page has the shared prompt).
  2. Same setup: Mistral Small 3.2 24B Instruct (Venice) (venice/mistral-small-3-2-24b-instruct, thinking on) via venice-mini-agent v1.3, cloud.
  3. It counts as a success only if it meets every success criterion: 1. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_svg_static 5. fresh_run_1 6. fresh_run_2
  4. 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).

Evidence log
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    "result": "# Fishing trip planner from tide, solunar and weather data\n\n`input.json` (synthetic) covers 48 hours, hour index h = 0..47 meaning the hour [h, h+1). It has `tide_m` (49 hourly heights at h = 0..48), `wind_kt` and `pressure_hpa` (48 values), `solunar_major` and `solunar_minor` (lists of [start, end] in fractional hours), `daylight` ([sunrise_hour, sunset_hour] for day 0 and day 1, in hour-of-day), and `window_hours` L. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `windows.svg`. Fresh inputs are used too.\n\n## Hourly score\n- If wind > 20 kt the hour is **unfishable**: score null, ignore the rest.\n- +3 if any major period overlaps the hour (start < h+1 and end > h); +1.5 if any minor period overlaps (both can apply).\n- Tide movement d = |tide[h+1] - tide[h]|, rounded to 6 decimals: +2 if d >= 0.3, else +1 if d >= 0.15.\n- Wind above 10 kt (and <= 20): -1.\n- Pressure trend dp = pressure[h] - pressure[h-3] (0 for h < 3), rounded to 6 decimals: +1 if dp <= -1.0, -1 if dp >= +1.0.\n- Outside daylight: -2, where daylight means sunrise <= (h mod 24) < sunset for day h // 24.\nRound each score to 2 decimals.\n\n## Windows\nA window starting at s covers hours s..s+L-1, all fishable; its score is the sum, rounded to 2 decimals. Sort all windows by score descending then start ascending, and greedily keep up to 3 that do not overlap any already kept one.\n\n## answer.json\n`{\"hourly_scores\": [48 values or null], \"unfishable_hours\": n, \"windows\": [{\"start\", \"end\" (= start + L), \"score\"}, ...]}`\n\n## Visual output: windows.svg\nStatic, self-contained SVG (root `<svg>`; no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, `href`/`src` except `#...` or `data:`, CSS `url(http...)`/`@import` or fetches; renders headless) with an element `id=\"hourly-bars\"` (the hourly scores) and one `<rect id=\"window-N\">` for each reported window N = 1, 2, 3 in answer order. The drawing itself is not graded.\n\n## Scoring\nHidden checks (partial credit; all must pass): `hourly_scores_day1` (hours 0-23), `hourly_scores_day2` (24-47 plus unfishable count), `top_windows`, `windows_svg_static` (windows.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Tolerance 0.001. 60-second limit per run.\n"
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    "result": "{\n \"window_hours\": 3,\n \"tide_m\": [\n  0.78,\n  0.48,\n  0.44,\n  0.66,\n  1.09,\n  1.63,\n  2.13,\n  2.48,\n  2.58,\n  2.41,\n  2.01,\n  1.48,\n  0.96,\n  0.58,\n  0.42,\n  0.54,\n  0.89,\n  1.4,\n  1.93,\n  2.36,\n  2.57,\n  2.51,\n  2.2,\n  1.71,\n  1.17,\n  0.72,\n  0.46,\n  0.46,\n  0.72,\n  1.18,\n  1.72,\n  2.2,\n  2.51,\n  2.57,\n  2.36,\n  1.93,\n  1.4,\n  0.89,\n  0.54,\n  0.42,\n  0.58,\n  0.97,\n  1.49,\n  2.01,\n  2.41,\n  2.58,\n  2.47,\n  2.13,\n  1.63\n ],\n \"wind_kt\": [\n  14.7,\n  15.1,\n  10.0,\n  16.0,\n  15.7,\n  14.0,\n  15.8,\n  16.8,\n  17.3,\n  17.4,\n  15.8,\n  12.4,\n  6.6,\n  3.9,\n  4.0,\n  4.0,\n  1.6,\n  1.6,\n  0,\n  2.1,\n  4.8,\n  1.9,\n  3.5,\n  6.7,\n  4.2,\n  0.6,\n  1.2,\n  0.7,\n  1.9,\n  0,\n  1.1,\n  0.8,\n  8.6,\n  24.5,\n  0,\n  1.1,\n  20.0,\n  0,\n  0,\n  0,\n  1.7,\n  1.0,\n  0,\n  1.6,\n  3.3,\n  5.1,\n  25.5,\n  5.3\n ],\n \"pressure_hpa\": [\n  1014.0,\n  1013.7,\n  1013.5,\n  1013.3,\n  1012.6,\n  1012.7,\n  1011.7,\n  1010.4,\n  1010.0,\n  1009.4,\n  1008.7,\n  1008.7,\n  1008.9,\n  1008.6,\n  1008.8,\n  1008.7,\n  1008.3,\n  1008.2,\n  1007.3,\n  1007.6,\n  1007.6,\n  1007.9,\n  1008.0,\n  1008.4,\n  1008.1,\n  1007.7,\n  1007.5,\n  1006.5,\n  1005.9,\n  1005.8,\n  1005.6,\n  1005.7,\n  1004.9,\n  1004.6,\n  1004.2,\n  1004.0,\n  1003.9,\n  1004.6,\n  1004.2,\n  1004.7,\n  1006.0,\n  1006.9,\n  1007.2,\n  1006.7,\n  1007.3,\n  1006.9,\n  1006.6,\n  1006.4\n ],\n \"solunar_major\": [\n  [\n   6.75,\n   8.75\n  ],\n  [\n   19.15,\n   21.15\n  ],\n  [\n   31.55,\n   33.55\n  ],\n  [\n   43.95,\n   45.95\n  ]\n ],\n \"solunar_minor\": [\n  [\n   12.95,\n   13.95\n  ],\n  [\n   25.35,\n   26.35\n  ],\n  [\n   37.75,\n   38.75\n  ]\n ],\n \"daylight\": [\n  [\n   6,\n   18\n  ],\n  [\n   5,\n   18\n  ]\n ]\n}"
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           "arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\nimport math\\nimport sys\\n\\ndef calculate_hourly_scores(input_data):\\n    hourly_scores = []\\n    unfishable_hours = 0\\n\\n    for h in range(48):\\n        # Check if the hour is unfishable due to wind\\n        if input_data['wind_kt'][h] > 20:\\n            hourly_scores.append(None)\\n            unfishable_hours += 1\\n            continue\\n\\n        score = 0.0\\n\\n        # Check for solunar major and minor periods\\n        for major in input_data['solunar_major']:\\n            if major[0] < h + 1 and major[1] > h:\\n                score += 3\\n        for minor in input_data['solunar_minor']:\\n            if minor[0] < h + 1 and minor[1] > h:\\n                score += 1.5\\n\\n        # Calculate tide movement\\n        if h < 47:\\n            d = abs(input_data['tide_m'][h + 1] - input_data['tide_m'][h])\\n            d_rounded = round(d, 6)\\n            if d_rounded >= 0.3:\\n                score += 2\\n            elif d_rounded >= 0.15:\\n                score += 1\\n\\n        # Check for wind above 10 kt\\n        if 10 < input_data['wind_kt'][h] <= 20:\\n            score -= 1\\n\\n        # Calculate pressure trend\\n        if h >= 3:\\n            dp = input_data['pressure_hpa'][h] - input_data['pressure_hpa'][h - 3]\\n            dp_rounded = round(dp, 6)\\n            if dp_rounded <= -1.0:\\n                score += 1\\n            elif dp_rounded >= 1.0:\\n                score -= 1\\n\\n        # Check for daylight\\n        day = h // 24\\n        hour_of_day = h % 24\\n        if not (input_data['daylight'][day][0] <= hour_of_day < input_data['daylight'][day][1]):\\n            score -= 2\\n\\n        # Round the score to 2 decimal places\\n        hourly_scores.append(round(score, 2))\\n\\n    return hourly_scores, unfishable_hours\\n\\ndef find_windows(hourly_scores, window_hours):\\n    windows = []\\n\\n    for s in range(48 - window_hours + 1):\\n        if None in hourly_scores[s:s + window_hours]:\\n            continue\\n\\n        window_score = sum(hourly_scores[s:s + window_hours])\\n        window_score_rounded = round(window_score, 2)\\n        windows.append({'start': s, 'end': s + window_hours, 'score': window_score_rounded})\\n\\n    # Sort windows by score descending and start ascending\\n    windows.sort(key=lambda x: (-x['score'], x['start']))\\n\\n    # Greedily select up to 3 non-overlapping windows\\n    selected_windows = []\\n    for window in windows:\\n        overlap = False\\n        for selected in selected_windows:\\n            if not (window['end'] <= selected['start'] or window['start'] >= selected['end']):\\n                overlap = True\\n                break\\n        if not overlap and len(selected_windows) < 3:\\n            selected_windows.append(window)\\n\\n    return selected_windows\\n\\ndef generate_svg(hourly_scores, windows):\\n    svg_content = \\\"\\\"\\\"\\n    <svg width=\\\"800\\\" height=\\\"200\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n    <rect id=\\\"hourly-bars\\\" x=\\\"0\\\" y=\\\"0\\\" width=\\\"800\\\" height=\\\"200\\\" fill=\\\"white\\\"/>\\n    \\\"\\\"\\\"\\n\\n    bar_width = 800 / 48\\n    for h in range(48):\\n        if hourly_scores[h] is not None:\\n            bar_height = 200 - (hourly_scores[h] * 2)\\n            svg_content += f\\\"<rect x='{h * bar_width}' y='{bar_height}' width='{bar_width}' height='{200 - bar_height}' fill='blue'/>\\\"\\n\\n    for i, window in enumerate(windows, start=1):\\n        window_start = window['start'] * bar_width\\n        window_width = (window['end'] - window['start']) * bar_width\\n        svg_content += f\\\"<rect id='window-{i}' x='{window_start}' y='0' width='{window_width}' height='200' fill='red' fill-opacity='0.3'/>\\\"\\n\\n    svg_content += \\\"\\\"\\\"\\n    </svg>\\n    \\\"\\\"\\\"\\n    return svg_content\\n\\ndef main():\\n    if len(sys.argv) != 3:\\n        print(\\\"Usage: python solve.py input.json answer.json\\\")\\n        sys.exit(1)\\n\\n    input_path = sys.argv[1]\\n    output_path = sys.argv[2]\\n\\n    with open(input_path, 'r') as f:\\n        input_data = json.load(f)\\n\\n    hourly_scores, unfishable_hours = calculate_hourly_scores(input_data)\\n    windows = find_windows(hourly_scores, input_data['window_hours'])\\n\\n    answer = {\\n        \\\"hourly_scores\\\": hourly_scores,\\n        \\\"unfishable_hours\\\": unfishable_hours,\\n        \\\"windows\\\": windows\\n    }\\n\\n    with open(output_path, 'w') as f:\\n        json.dump(answer, f)\\n\\n    svg_content = generate_svg(hourly_scores, windows)\\n    with open(\\\"windows.svg\\\", 'w') as f:\\n        f.write(svg_content)\\n\\nif __name__ == \\\"__main__\\\":\\n    main()\"}"
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