Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Water-quality sensor anomaly flags and daily means
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-27 · by @bobsiopener
- Estimated cost
- $0.0014
- Wall time
- 28s
- Human step-ins
- 0
- Outcome
- Fail
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Rescored on 2026-10-28. Previous score: 23.6 / 100, outcome: fail. See the previous run.
Summary
Exact prompt
# Water-quality sensor anomaly flags `input.json` (synthetic buoy data) names `readings.csv` (`ts` "YYYY-MM-DDTHH:MM", `sensor`, `value`; rows are shuffled) and gives `stuck_n`, `window`, `spike_k`, and per-sensor `config` (`min`, `max`, `spike_floor`, `max_step`). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `chart.svg`. Fresh inputs are used too. ## Rules (per sensor, rows in ascending ts) 1. **missing**: value is blank or not a finite number (e.g. `err`, `nan`). It is skipped entirely (it does not break or extend a stuck run). 2. For other values, track the run of consecutive non-missing readings with exactly the same value (all flags included). Then the first match wins: - **range**: value < min or > max. - **stuck**: the current run length >= `stuck_n`. - **spike**: at least `window` ok values exist; with the last `window` ok values, med = median and MAD = median of |x - med|; flag if |v - med| > max(spike_k * MAD, spike_floor). - **rate**: |v - last ok value| > max_step. - else **ok**. Only ok values are used as "ok values", in daily means and in the chart. 3. `daily_means[sensor][date]` = mean of ok values that day (3 decimals); `counts[sensor]` has keys ok, missing, range, stuck, spike, rate; `flags` lists every non-ok reading `{"ts", "sensor", "flag"}` sorted by ts then sensor. ## Visual output: chart.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="series-<sensor>">` for each sensor (`series-temp_c`, `series-do_mgl`, `series-ph`) and an element `id="flag-summary"`. The drawing itself is not graded. ## answer.json `{"counts": {...}, "daily_means": {...}, "flags": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `missing_and_range`, `stuck_detection`, `spike_and_rate` (with ok counts), `flag_list`, `daily_means` (within 0.0011), `chart_svg_static` (chart.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Shuffled rows, a stuck run that ignores missing values but includes flagged ones, a robust median/MAD spike test with a floor that only uses ok history, a rate test against the last ok value, and flag precedence. 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. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2
Setup notes: Use Case Scout batch 10 case k12-water-quality-anomalies (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. No visual was produced: the run never wrote the requested chart.svg (it wrote answer.svg instead), so nothing was rendered. 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 $0.0058, 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 v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.0058 run cap). Score: 2/8. 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.
Metrics
- Estimated cost
- $0.0014
- Wall time
- 28s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 10,882 | 1,507 | $0.0014 |
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. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. 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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"content": "import json\nimport csv\nfrom collections import defaultdict\nfrom datetime import datetime\nimport math\n\n\ndef process_readings(input_file, output_file):\n with open(input_file) as f:\n config = json.load(f)\n\n readings_file = config['readings']\n stuck_n = config['stuck_n']\n window = config['window']\n spike_k = config['spike_k']\n sensor_configs = config['config']\n\n counts = defaultdict(lambda: defaultdict(int))\n daily_means = defaultdict(lambda: defaultdict(float))\n flags = []\n\n sensor_data = defaultdict(list)\n\n with open(readings_file) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n ts = row['ts']\n sensor = row['sensor']\n value_str = row['value']\n\n try:\n value = float(value_str)\n except ValueError:\n counts[sensor]['missing'] += 1\n continue\n\n if math.isnan(value) or math.isinf(value):\n counts[sensor]['missing'] += 1\n continue\n\n date = ts.split('T')[0]\n\n config = sensor_configs[sensor]\n min_val = config['min']\n max_val = config['max']\n spike_floor = config['spike_floor']\n max_step = config['max_step']\n\n if value < min_val or value > max_val:\n counts[sensor]['range'] += 1\n flags.append({'ts': ts, 'sensor': sensor, 'flag': 'range'})\n continue\n\n sensor_data[sensor].append((ts, value))\n\n for sensor, data in sensor_data.items():\n data.sort() # Sort by timestamp\n stuck_count = 0\n ok_values = []\n last_ok_value = None\n\n for ts, value in data:\n if last_ok_value is not None and abs(value - last_ok_value) > max_step:\n counts[sensor]['rate'] += 1\n flags.append({'ts': ts, 'sensor': sensor, 'flag': 'rate'})\n stuck_count = 0\n continue\n\n if stuck_count >= stuck_n:\n counts[sensor]['stuck'] += 1\n flags.append({'ts': ts, 'sensor': sensor, 'flag': 'stuck'})\n continue\n\n if len(ok_values) >= window:\n med = median(ok_values[-window:])\n mad = median_abs_dev(ok_values[-window:], med)\n if abs(value - med) > max(spike_k * mad, spike_floor):\n counts[sensor]['spike'] += 1\n flags.append({'ts': ts, 'sensor': sensor, 'flag': 'spike'})\n stuck_count = 0\n continue\n\n counts[sensor]['ok'] += 1\n ok_values.append(value)\n last_ok_value = value\n stuck_count = 0\n\n date = ts.split('T')[0]\n daily_means[sensor][date] = round(sum(ok_values) / len(ok_values), 3) if ok_values else 0.0\n\n for sensor in sensor_configs:\n for date, mean in daily_means[sensor].items():\n daily_means[sensor][date] = round(mean, 3)\n\n flags.sort(key=lambda x: (x['ts'], x['sensor']))\n\n result = {\n 'counts': counts,\n 'daily_means': daily_means,\n 'flags': flags\n }\n\n with open(output_file, 'w') as f:\n json.dump(result, f, indent=4)\n\n generate_chart(sensor_data, output_file.replace('.json', '.svg'))\n\ndef median(lst):\n s = sorted(lst)\n return s[len(s) // 2]\n\ndef median_abs_dev(lst, med):\n return median([abs(x - med) for x in lst])\n\ndef generate_chart(sensor_data, output_file):\n svg_content = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n <!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n <svg width=\"800\" height=\"600\" xmlns=\"http://www.w3.org/2000/svg\">\n <g id=\"flag-summary\">\n <text x=\"10\" y=\"20\" font-family=\"Verdana\" font-size=\"15\" fill=\"black\">Flag Summary</text>\n </g>\n \"\"\"\n\n for sensor, data in sensor_data.items():\n points = []\n for ts, value in data:\n date = datetime.strptime(ts, \"%Y-%m-%dT%H:%M\")\n x = (date - datetime(2023, 1, 1)).total_seconds() / 3600 # Convert to hours since start of year\n y = value\n points.append(f\"{x},{y}\")\n svg_content += f\" <polyline id='series-{sensor}' points='{' '.join(points)}' style='fill:none;stroke:black;' />\\n\"\n\n svg_content += \" </svg>\"\n\n with open(output_file, 'w') as f:\n f.write(svg_content)\n\nif __name__ == '__main__':\n import sys\n if len(sys.argv) != 3:\n print(\"Usage: python solve.py input.json output.json\")\n sys.exit(1)\n \n input_file = sys.argv[1]\n output_file = sys.argv[2]\n process_readings(input_file, output_file)"
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