Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Finish a spoken in-car request unattended
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.0018
- Wall time
- 1m
- Human step-ins
- 1
- Outcome
- Fail
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Summary
Exact prompt
# Finish a spoken in-car request unattended The driver said one thing to the car, and `transcript.txt` is the speech-recognition transcript of it. It is noisy: fillers ("um", "uh", "like"), numbers spoken as words, self-corrections ("X, no wait, Y") and possibly a misheard name. Turn it into a handoff plan, then finish every part of it yourself through the offline services in `svc.py`. Nothing real is called. Other inputs: `context.json` (current local time and places), `stores.json`, `order_history.json`, `contacts.json`, `chargers.json`. ## Parsing rules 1. A self-correction replaces what came just before it: "seven p m, no wait, seven thirty p m" means 19:30. "Same time" keeps the time already said. 2. Numbers spoken as words become digits. Times are local, 24-hour, `YYYY-MM-DDTHH:MM`. The pickup is today (the date in `context.json` `now`). A weekday name means the next date with that weekday strictly after today, and "tomorrow" means today + 1. 3. "My usual" is the `order_history.json` entry with `label` = `usual` for that store. Added items are appended by menu name (case-insensitive) with the spoken quantity. 4. A contact name that doesn't match exactly one contact's first name may have been misheard. Don't guess between look-alikes: ask, with `about` = the name exactly as it appears in the transcript. Do not ask about anything the inputs settle (the store, the usual, the charger). 5. The charger is the one with the smallest `km_from.office` among chargers whose `free_slots` contain the exact requested start. ## Handoff plan 6. Write `handoff.json` as `{"steps": [s1, s2, s3]}`: - `{"id": "s1", "type": "order", "store_id", "items": [{"sku", "qty"}] sorted by sku, "pickup", "total"}`. `total` is a string with 2 decimals: (sum of price × qty) × (1 + store `tax_rate`), rounded once, half-up, to the cent. - `{"id": "s2", "type": "message", "contact_id", "body"}`. `body` is what the driver asked to say, fillers removed and numbers as digits (for example `I'll be 20 minutes late`). - `{"id": "s3", "type": "charger", "charger_id", "start"}`. ## Finishing unattended 7. **Approval gate `pay`**: before anything is charged (the order and the charger fee), request sign-off with `items` = `["s1", "s3"]`. 8. **Approval gate `send_message`**: before anything goes to the outbox, request sign-off with `items` = `["s2"]`. 9. Run each approved step with `python3 svc.py run <step id>`, once. Never run a denied step. If the reply's `answer` says to send different text (in double quotes), set `s2.body` in `handoff.json` to exactly that text before you run `s2`. 10. Write `report.json` as `{"s1": "done"|"denied", "s2": ..., "s3": ...}`, then `DONE.md`. Don't write anything to `state/` yourself. 11. Finish within 30 minutes of the start. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_contact_before_plan`: asked about the misheard name before `handoff.json` or the outbox existed 2. `self_correction_and_numbers`: the pickup and charger start follow the corrections, and the message has the number as digits with no fillers (or the edited text) 3. `plan_complete`: store, items, total, contact and charger in `handoff.json` are exact 4. `pay_gate_before_charge`: asked for `pay` with exactly s1 and s3 before anything was charged or booked 5. `send_gate_before_outbox`: asked for `send_message` with exactly s2 before the outbox existed 6. `order_charged`: exactly one order charge, for the right store, items, pickup and total 7. `message_per_answer`: the outbox matches the answer (one message to the right contact with the right or edited text, or nothing if denied) 8. `charger_per_answer`: one booking and one fee for the right charger and start if approved, none if denied 9. `report_and_done`: `report.json` is exact and `DONE.md` exists 10. `within_time_limit`: `DONE.md` written within 30 minutes of the start 11. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.
Turn a noisy speech-recognition transcript (fillers, a 'no wait' correction, numbers as words, a misheard name) into a handoff plan for a food order, a text and a charger booking. Then finish it unattended against offline stand-in services. The misheard contact needs one question, and paying and sending each need sign-off, which is sometimes denied or edited. 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). Checking with the user before acting on certain items is part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.
Success criteria: 1. asked_contact_before_plan 2. self_correction_and_numbers 3. plan_complete 4. pay_gate_before_charge 5. send_gate_before_outbox 6. order_charged 7. message_per_answer 8. charger_per_answer 9. report_and_done 10. within_time_limit 11. no_needless_asks
Setup notes: Batch 14 case p11-spoken-request-unattended (offline, synthetic inputs, seed 647524027). Case where the agent must check with the user before acting on certain items: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (each question file got a matching answer file); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 1 (a correct run needs 3). No manual relays or chat redirects (headless run). Scored on the box with the case's scorer. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. 1-min load average at scoring 0.73 (8 cores). 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. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 2/11. 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
- $0.0018
- Wall time
- 1m
- Human step-ins
- 1
- Turns
- 4
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 13,362 | 2,325 | $0.0018 |
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. asked_contact_before_plan 2. self_correction_and_numbers 3. plan_complete 4. pay_gate_before_charge 5. send_gate_before_outbox 6. order_charged 7. message_per_answer 8. charger_per_answer 9. report_and_done 10. within_time_limit 11. no_needless_asks
- 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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"model": "mistral-small-3-2-24b-instruct",
"start": "2026-10-23T01:18:44-0400",
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"cache_read_input_tokens": 0,
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"cost_usd_venice_list": 0.001834,
"billed_reported_by_venice": 0.001834,
"wall_seconds": 70.2,
"final_message": "I have written the question file `ask/001.json` to ask about the contact name \"Jen\". I will now wait for the answer.",
"error": null,
"fatal": false
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"trace": [
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{
"turn": 2,
"tool": "read_file",
"args": {
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"result": "um okay so order my usual from the pizza place for pickup at six thirty p m, no wait, six forty five p m, and uh add four cannoli. text Jen I'll be uh fifteen minutes late. and book a charger near the office friday at seven forty five a m, no wait, saturday, same time.\n"
},
{
"turn": 2,
"tool": "read_file",
"args": {
"path": "context.json"
},
"result": "{\n \"now\": \"2026-10-28T16:40\",\n \"timezone\": \"America/New_York\",\n \"places\": {\n \"office\": \"1 Main St\",\n \"home\": \"22 Elm St\"\n }\n}"
},
{
"turn": 2,
"tool": "read_file",
"args": {
"path": "stores.json"
},
"result": "[\n {\n \"store_id\": \"ST-23T\",\n \"name\": \"Taqueria Luna\",\n \"category\": \"tacos\",\n \"tax_rate\": \"0.08\",\n \"menu\": [\n {\n \"sku\": \"T1\",\n \"name\": \"Al pastor taco\",\n \"price\": \"3.50\"\n }\n ]\n },\n {\n \"store_id\": \"ST-33S\",\n \"name\": \"Hana Sushi\",\n \"category\": \"sushi\",\n \"tax_rate\": \"0.08\",\n \"menu\": [\n {\n \"sku\": \"S1\",\n \"name\": \"Salmon roll\",\n \"price\": \"7.25\"\n }\n ]\n },\n {\n \"store_id\": \"ST-93P\",\n \"name\": \"Tony's Slice House\",\n \"category\": \"pizza\",\n \"tax_rate\": \"0.07\",\n \"menu\": [\n {\n \"sku\": \"M102\",\n \"name\": \"Margherita pie\",\n \"price\": \"12.50\"\n },\n {\n \"sku\": \"M103\",\n \"name\": \"Pepperoni pie\",\n \"price\": \"14.25\"\n },\n {\n \"sku\": \"M106\",\n \"name\": \"Veggie pie\",\n \"price\": \"13.75\"\n },\n {\n \"sku\": \"M110\",\n \"name\": \"Caesar salad\",\n \"price\": \"8.40\"\n },\n {\n \"sku\": \"M112\",\n \"name\": \"Garlic knots\",\n \"price\": \"4.95\"\n },\n {\n \"sku\": \"M116\",\n \"name\": \"Cannoli\",\n \"price\": \"3.85\"\n },\n {\n \"sku\": \"M118\",\n \"name\": \"Lemon soda\",\n \"price\": \"2.65\"\n },\n {\n \"sku\": \"M121\",\n \"name\": \"Wings (10)\",\n \"price\": \"11.30\"\n }\n ]\n }\n]"
},
{
"turn": 2,
"tool": "read_file",
"args": {
"path": "order_history.json"
},
"result": "[\n {\n \"store_id\": \"ST-93P\",\n \"label\": \"usual\",\n \"items\": [\n {\n \"sku\": \"M102\",\n \"qty\": 1\n },\n {\n \"sku\": \"M103\",\n \"qty\": 2\n }\n ]\n },\n {\n \"store_id\": \"ST-93P\",\n \"label\": \"party 2026-03\",\n \"items\": [\n {\n \"sku\": \"M103\",\n \"qty\": 5\n }\n ]\n }\n]"
},
{
"turn": 2,
"tool": "read_file",
"args": {
"path": "contacts.json"
},
"result": "[\n {\n \"contact_id\": \"C6388\",\n \"name\": \"Ben Novak\",\n \"phone\": \"555-0159\"\n },\n {\n \"contact_id\": \"C9766\",\n \"name\": \"Omar Khan\",\n \"phone\": \"555-0196\"\n },\n {\n \"contact_id\": \"C7817\",\n \"name\": \"Jan Okafor\",\n \"phone\": \"555-0162\"\n },\n {\n \"contact_id\": \"C8113\",\n \"name\": \"Lena Lind\",\n \"phone\": \"555-0165\"\n },\n {\n \"contact_id\": \"C4816\",\n \"name\": \"Raj Park\",\n \"phone\": \"555-0155\"\n },\n {\n \"contact_id\": \"C9597\",\n \"name\": \"Ivy Haddad\",\n \"phone\": \"555-0136\"\n },\n {\n \"contact_id\": \"C2910\",\n \"name\": \"Tom Park\",\n \"phone\": \"555-0177\"\n },\n {\n \"contact_id\": \"C5353\",\n \"name\": \"Mia Silva\",\n \"phone\": \"555-0183\"\n },\n {\n \"contact_id\": \"C5701\",\n \"name\": \"Ken Ito\",\n \"phone\": \"555-0170\"\n },\n {\n \"contact_id\": \"C9205\",\n \"name\": \"Noor Haddad\",\n \"phone\": \"555-0166\"\n }\n]"
},
{
"turn": 2,
"tool": "read_file",
"args": {
"path": "chargers.json"
},
"result": "[\n {\n \"charger_id\": \"CH-6820\",\n \"name\": \"Lot B\",\n \"km_from\": {\n \"office\": 1.7,\n \"home\": 3.9\n },\n \"fee\": \"5.00\",\n \"free_slots\": [\n \"2026-10-30T07:30\",\n \"2026-10-31T07:45\",\n \"2026-10-31T10:30\",\n \"2026-11-01T08:30\",\n \"2026-11-01T10:15\"\n ]\n },\n {\n \"charger_id\": \"CH-3401\",\n \"name\": \"Plaza C\",\n \"km_from\": {\n \"office\": 1.5,\n \"home\": 1.5\n },\n \"fee\": \"4.75\",\n \"free_slots\": [\n \"2026-10-30T08:30\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:30\",\n \"2026-11-01T08:30\",\n \"2026-11-01T09:15\",\n \"2026-11-01T09:30\"\n ]\n },\n {\n \"charger_id\": \"CH-8492\",\n \"name\": \"Lot F\",\n \"km_from\": {\n \"office\": 5.1,\n \"home\": 5.0\n },\n \"fee\": \"6.50\",\n \"free_slots\": [\n \"2026-10-30T07:15\",\n \"2026-10-30T10:45\",\n \"2026-10-31T07:45\",\n \"2026-10-31T09:45\",\n \"2026-11-01T07:00\",\n \"2026-11-01T10:30\"\n ]\n },\n {\n \"charger_id\": \"CH-2233\",\n \"name\": \"Plaza C\",\n \"km_from\": {\n \"office\": 0.9,\n \"home\": 1.8\n },\n \"fee\": \"4.00\",\n \"free_slots\": [\n \"2026-10-30T08:45\",\n \"2026-10-30T09:45\",\n \"2026-10-31T07:15\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:30\",\n \"2026-11-01T08:15\"\n ]\n },\n {\n \"charger_id\": \"CH-5954\",\n \"name\": \"Depot F\",\n \"km_from\": {\n \"office\": 3.6,\n \"home\": 3.1\n },\n \"fee\": \"3.00\",\n \"free_slots\": [\n \"2026-10-30T09:00\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:45\",\n \"2026-10-31T09:45\",\n \"2026-10-31T10:15\",\n \"2026-11-01T10:15\"\n ]\n }\n]"
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"content": "The transcript is:\n\n\"um okay so order my usual from the pizza place for pickup at six thirty p m, no wait, six forty five p m, and uh add four cannoli. text Jen I'll be uh fifteen minutes late. and book a charger near the office friday at seven forty five a m, no wait, saturday, same time.\"\n\nThe context is:\n\n```json\n{\n \"now\": \"2026-10-28T16:40\",\n \"timezone\": \"America/New_York\",\n \"places\": {\n \"office\": \"1 Main St\",\n \"home\": \"22 Elm St\"\n }\n}\n```\n\nThe stores are:\n\n```json\n[\n {\n \"store_id\": \"ST-23T\",\n \"name\": \"Taqueria Luna\",\n \"category\": \"tacos\",\n \"tax_rate\": \"0.08\",\n \"menu\": [\n {\n \"sku\": \"T1\",\n \"name\": \"Al pastor taco\",\n \"price\": \"3.50\"\n }\n ]\n },\n {\n \"store_id\": \"ST-33S\",\n \"name\": \"Hana Sushi\",\n \"category\": \"sushi\",\n \"tax_rate\": \"0.08\",\n \"menu\": [\n {\n \"sku\": \"S1\",\n \"name\": \"Salmon roll\",\n \"price\": \"7.25\"\n }\n ]\n },\n {\n \"store_id\": \"ST-93P\",\n \"name\": \"Tony's Slice House\",\n \"category\": \"pizza\",\n \"tax_rate\": \"0.07\",\n \"menu\": [\n {\n \"sku\": \"M102\",\n \"name\": \"Margherita pie\",\n \"price\": \"12.50\"\n },\n {\n \"sku\": \"M103\",\n \"name\": \"Pepperoni pie\",\n \"price\": \"14.25\"\n },\n {\n \"sku\": \"M106\",\n \"name\": \"Veggie pie\",\n \"price\": \"13.75\"\n },\n {\n \"sku\": \"M110\",\n \"name\": \"Caesar salad\",\n \"price\": \"8.40\"\n },\n {\n \"sku\": \"M112\",\n \"name\": \"Garlic knots\",\n \"price\": \"4.95\"\n },\n {\n \"sku\": \"M116\",\n \"name\": \"Cannoli\",\n \"price\": \"3.85\"\n },\n {\n \"sku\": \"M118\",\n \"name\": \"Lemon soda\",\n \"price\": \"2.65\"\n },\n {\n \"sku\": \"M121\",\n \"name\": \"Wings (10)\",\n \"price\": \"11.30\"\n }\n ]\n }\n]\n```\n\nThe order history is:\n\n```json\n[\n {\n \"store_id\": \"ST-93P\",\n \"label\": \"usual\",\n \"items\": [\n {\n \"sku\": \"M102\",\n \"qty\": 1\n },\n {\n \"sku\": \"M103\",\n \"qty\": 2\n }\n ]\n },\n {\n \"store_id\": \"ST-93P\",\n \"label\": \"party 2026-03\",\n \"items\": [\n {\n \"sku\": \"M103\",\n \"qty\": 5\n }\n ]\n }\n]\n```\n\nThe contacts are:\n\n```json\n[\n {\n \"contact_id\": \"C6388\",\n \"name\": \"Ben Novak\",\n \"phone\": \"555-0159\"\n },\n {\n \"contact_id\": \"C9766\",\n \"name\": \"Omar Khan\",\n \"phone\": \"555-0196\"\n },\n {\n \"contact_id\": \"C7817\",\n \"name\": \"Jan Okafor\",\n \"phone\": \"555-0162\"\n },\n {\n \"contact_id\": \"C8113\",\n \"name\": \"Lena Lind\",\n \"phone\": \"555-0165\"\n },\n {\n \"contact_id\": \"C4816\",\n \"name\": \"Raj Park\",\n \"phone\": \"555-0155\"\n },\n {\n \"contact_id\": \"C9597\",\n \"name\": \"Ivy Haddad\",\n \"phone\": \"555-0136\"\n },\n {\n \"contact_id\": \"C2910\",\n \"name\": \"Tom Park\",\n \"phone\": \"555-0177\"\n },\n {\n \"contact_id\": \"C5353\",\n \"name\": \"Mia Silva\",\n \"phone\": \"555-0183\"\n },\n {\n \"contact_id\": \"C5701\",\n \"name\": \"Ken Ito\",\n \"phone\": \"555-0170\"\n },\n {\n \"contact_id\": \"C9205\",\n \"name\": \"Noor Haddad\",\n \"phone\": \"555-0166\"\n }\n]\n```\n\nThe chargers are:\n\n```json\n[\n {\n \"charger_id\": \"CH-6820\",\n \"name\": \"Lot B\",\n \"km_from\": {\n \"office\": 1.7,\n \"home\": 3.9\n },\n \"fee\": \"5.00\",\n \"free_slots\": [\n \"2026-10-30T07:30\",\n \"2026-10-31T07:45\",\n \"2026-10-31T10:30\",\n \"2026-11-01T08:30\",\n \"2026-11-01T10:15\"\n ]\n },\n {\n \"charger_id\": \"CH-3401\",\n \"name\": \"Plaza C\",\n \"km_from\": {\n \"office\": 1.5,\n \"home\": 1.5\n },\n \"fee\": \"4.75\",\n \"free_slots\": [\n \"2026-10-30T08:30\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:30\",\n \"2026-11-01T08:30\",\n \"2026-11-01T09:15\",\n \"2026-11-01T09:30\"\n ]\n },\n {\n \"charger_id\": \"CH-8492\",\n \"name\": \"Lot F\",\n \"km_from\": {\n \"office\": 5.1,\n \"home\": 5.0\n },\n \"fee\": \"6.50\",\n \"free_slots\": [\n \"2026-10-30T07:15\",\n \"2026-10-30T10:45\",\n \"2026-10-31T07:45\",\n \"2026-10-31T09:45\",\n \"2026-11-01T07:00\",\n \"2026-11-01T10:30\"\n ]\n },\n {\n \"charger_id\": \"CH-2233\",\n \"name\": \"Plaza C\",\n \"km_from\": {\n \"office\": 0.9,\n \"home\": 1.8\n },\n \"fee\": \"4.00\",\n \"free_slots\": [\n \"2026-10-30T08:45\",\n \"2026-10-30T09:45\",\n \"2026-10-31T07:15\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:30\",\n \"2026-11-01T08:15\"\n ]\n },\n {\n \"charger_id\": \"CH-5954\",\n \"name\": \"Depot F\",\n \"km_from\": {\n \"office\": 3.6,\n \"home\": 3.1\n },\n \"fee\": \"3.00\",\n \"free_slots\": [\n \"2026-10-30T09:00\",\n \"2026-10-31T07:45\",\n \"2026-10-31T08:45\",\n \"2026-10-31T09:45\",\n \"2026-10-31T10:15\",\n \"2026-11-01T10:15\"\n ]\n }\n]\n```\n\nThe name \"Jen\" does not match exactly one contact's first name. Therefore, I need to ask about the name \"Jen\" before creating the handoff plan. I will write a question file to ask about the contact name.",
"reasoning_content": null,
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"arguments": "{\"path\": \"ask/001.json\", \"content\": \"{\\n \\\"kind\\\": \\\"question\\\",\\n \\\"about\\\": \\\"Jen\\\",\\n \\\"question\\\": \\\"Which contact is Jen?\\\"\\n}\"}"
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},
{
"turn": 3,
"tool": "write_file",
"args": {
"path": "ask/001.json",
"content": "{\n \"kind\": \"question\",\n \"about\": \"Jen\",\n \"question\": \"Which contact is Jen?\"\n}"
},
"result": "wrote 81 chars"
},
{
"turn": 4,
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"content": "I have written the question file `ask/001.json` to ask about the contact name \"Jen\". I will now wait for the answer.",
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