DeepSeek V4.1 Flash (Venice) (thinking on) on Boat fuel range with a reserve over a planned trip
DDeepSeek V4.1 Flash (Venice)thinking on · venice-mini-agent v1.3 · cloud · 2026-10-21 · by @bobsiopener
- Estimated cost
- $0.0096
- Wall time
- 2m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Boat fuel range with a reserve over a planned trip `input.json` (synthetic; not for real trip planning): `tank_l`, `fuel_l` on board, `reserve_pct` (of the tank), `burn_table` (rows `rpm`, `speed_kn` through the water, `lph` litres per hour; not sorted), and `legs` (`distance_nm`, `rpm`, `current_kn`: positive = with the boat, negative = against). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh inputs are used too. ## Rules 1. Speed and burn at an rpm: sort the table by rpm, interpolate each linearly, clamp to the first/last row outside the range. 2. Each leg in order: SOG = speed + current_kn. If SOG <= 0 the leg cannot be run: append `{"sog_kn", "hours": null, "fuel_l": null, "remaining_l"}` and stop processing legs (this leg is the dry leg if none yet, with `dry_after_nm_into_leg` 0 if none yet). Otherwise hours = distance / SOG, fuel = lph * hours, remaining -= fuel (remaining may go negative; keep going). 3. `reserve_l` = tank * pct / 100. `reserve_breach_leg` = index (0-based) of the first leg after which remaining < reserve, or null. `runs_dry_leg` = first leg whose fuel exceeds the fuel left at its start, and `dry_after_nm_into_leg` = fuel left at its start / lph * SOG; both null if never. 4. Economy: the table row with the greatest speed_kn / lph (compared at 6 decimals; ties -> lower rpm). `economy_range_nm` = max(0, fuel_l - reserve) * that ratio (still water). Round speeds and hours to 3, litres and nm to 2, `economy_nm_per_l` to 4. ## answer.json `{"reserve_l", "legs": [{"sog_kn", "hours", "fuel_l", "remaining_l"}, ...], "reserve_breach_leg", "runs_dry_leg", "dry_after_nm_into_leg", "economy_rpm", "economy_nm_per_l", "economy_range_nm"}` ## Scoring Hidden checks (partial credit; all must pass): `leg_speeds_and_burn` (first three legs), `remaining_fuel`, `reserve_and_dry`, `economy`, `fresh_run_1`, `fresh_run_2`. Tolerance 0.011 (0.0002 for nm per litre). 60-second limit per run.
Unsorted burn table with clamped interpolation, current added to speed (including a leg where the boat cannot make way), separate reserve-breach and run-dry legs, and the economy-rpm tie rule. 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. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 10 case k11-fuel-range-reserve (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. Harness: venice-mini-agent v1.3 (/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 $0.08, 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 via Venice OpenAI-compatible API (16000 max tokens/call, $0.08 run cap). Score: 6/6. 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.
Metrics
- Estimated cost
- $0.0096
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| DDeepSeek V4.1 Flash (Venice)venice/deepseek-v4-1-flashthinking on | 16,354 | 5,780 | $0.0096 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: DeepSeek V4.1 Flash (Venice) (venice/deepseek-v4-1-flash, thinking on) via
venice-mini-agent v1.3, cloud. - It counts as a success only if it meets every success criterion: 1. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 5. fresh_run_1 6. 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
[
{
"segment_dir": "./usecase-runner/runs/k11-deepseek-v4-1-flash/venice_output_venice",
"venice_output": {
"harness": "venice-mini-agent",
"harness_version": "1.3",
"max_tokens_per_call": 16000,
"disable_thinking": false,
"run_cost_cap": 0.08,
"no_tools": false,
"resumed_from": null,
"model": "deepseek-v4-1-flash",
"start": "2026-10-20T20:28:20-0400",
"num_turns": 6,
"tool_calls": 6,
"usage": {
"input_tokens": 8724,
"output_tokens": 5780,
"cache_read_input_tokens": 7630,
"cache_creation_input_tokens": 0
},
"reasoning_tokens": 3922,
"requests": [
{
"input_tokens": 1122,
"output_tokens": 75,
"cached_tokens": 374,
"cache_write_tokens": 0
},
{
"input_tokens": 1736,
"output_tokens": 2604,
"cached_tokens": 374,
"cache_write_tokens": 0
},
{
"input_tokens": 2797,
"output_tokens": 430,
"cached_tokens": 374,
"cache_write_tokens": 0
},
{
"input_tokens": 3280,
"output_tokens": 1632,
"cached_tokens": 2668,
"cache_write_tokens": 0
},
{
"input_tokens": 3595,
"output_tokens": 682,
"cached_tokens": 1920,
"cache_write_tokens": 0
},
{
"input_tokens": 3824,
"output_tokens": 357,
"cached_tokens": 1920,
"cache_write_tokens": 0
}
],
"cost_usd_venice_list": 0.00961,
"billed_reported_by_venice": 0.00961,
"wall_seconds": 132.4,
"final_message": "Done. `solve.py` implements the full spec:\n\n- **Interpolation**: sorts `burn_table` by rpm, linearly interpolates `speed_kn` and `lph` at each leg's rpm, clamping outside the range.\n- **Legs**: computes `SOG = speed + current_kn`; if `SOG <= 0` appends a null-hours/fuel leg and stops (marking it as the dry leg with `dry_after_nm_into_leg = 0` if none yet). Otherwise `hours = distance/SOG`, `fuel = lph*hours`, decrements remaining (allowing negatives).\n- **Reserve/dry**: `reserve_l = tank*pct/100`; `reserve_breach_leg` = first leg ending below reserve; `runs_dry_leg` = first leg whose fuel exceeds fuel at its start, with `dry_after_nm_into_leg = start_remaining/lph*SOG`.\n- **Economy**: picks the row with max `speed_kn/lph` (rounded to 6 dp, ties \u2192 lower rpm), computes `economy_nm_per_l` and `economy_range_nm = max(0, fuel_l - reserve)*ratio`.\n- **Rounding**: speeds/hours to 3, litres/nm to 2, nm per litre to 4.\n\nVerified on `input.json` (reserve 60.0, breach leg 4, dry leg 5 at 9.31 nm, economy 1400 rpm / 0.6429 / 124.71 nm) and on a synthetic edge case with a non-runnable leg.",
"error": null,
"fatal": false
},
"trace": [
{
"turn": 1,
"response": {
"id": "chatcmpl-9b8a5d1f",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_854fb73112f64c42a75fa335",
"type": "function",
"function": {
"name": "list_files",
"arguments": "{\"path\":\".\"}"
}
},
{
"index": 1,
"id": "call_80a24779cc3e4f9cbc007a70",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"input.json\"}"
}
}
],
"role": "assistant",
"reasoning_content": "Let me start by exploring the directory."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542502,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 1122,
"completion_tokens": 75,
"total_tokens": 1197,
"prompt_tokens_details": {
"cached_tokens": 374
},
"cache_read_input_tokens": 374,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 8,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.000317205
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 1,
"tool": "list_files",
"args": {
"path": "."
},
"result": "./TASK.md\n./input.json"
},
{
"turn": 1,
"tool": "read_file",
"args": {
"path": "input.json"
},
"result": "{\n \"tank_l\": 300,\n \"fuel_l\": 254,\n \"reserve_pct\": 20,\n \"burn_table\": [\n {\n \"rpm\": 1000,\n \"speed_kn\": 2.83,\n \"lph\": 4.9\n },\n {\n \"rpm\": 1400,\n \"speed_kn\": 3.96,\n \"lph\": 6.16\n },\n {\n \"rpm\": 3000,\n \"speed_kn\": 8.15,\n \"lph\": 19.66\n },\n {\n \"rpm\": 2200,\n \"speed_kn\": 6.22,\n \"lph\": 10.99\n },\n {\n \"rpm\": 2600,\n \"speed_kn\": 7.36,\n \"lph\": 14.79\n },\n {\n \"rpm\": 1800,\n \"speed_kn\": 5.09,\n \"lph\": 8.15\n },\n {\n \"rpm\": 3400,\n \"speed_kn\": 10.15,\n \"lph\": 25.68\n }\n ],\n \"legs\": [\n {\n \"distance_nm\": 17.7,\n \"rpm\": 2100,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 19.0,\n \"rpm\": 3300,\n \"current_kn\": 0.8\n },\n {\n \"distance_nm\": 20.1,\n \"rpm\": 2100,\n \"current_kn\": -1.0\n },\n {\n \"distance_nm\": 20.9,\n \"rpm\": 1800,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 27.4,\n \"rpm\": 2500,\n \"current_kn\": -1.1\n },\n {\n \"distance_nm\": 14.1,\n \"rpm\": 3000,\n \"current_kn\": -1.6\n },\n {\n \"distance_nm\": 23.6,\n \"rpm\": 2500,\n \"current_kn\": 1.1\n }\n ]\n}"
},
{
"turn": 2,
"response": {
"id": "chatcmpl-a68aeebd",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_404d8a89f9b948e38e77a669",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\":\"solve.py\",\"content\":\"import json\\nimport sys\\n\\n\\ndef interp(table, rpm):\\n # table sorted by rpm\\n if rpm <= table[0][\\\"rpm\\\"]:\\n row = table[0]\\n return row[\\\"speed_kn\\\"], row[\\\"lph\\\"]\\n if rpm >= table[-1][\\\"rpm\\\"]:\\n row = table[-1]\\n return row[\\\"speed_kn\\\"], row[\\\"lph\\\"]\\n for i in range(len(table) - 1):\\n a = table[i]\\n b = table[i + 1]\\n if a[\\\"rpm\\\"] <= rpm <= b[\\\"rpm\\\"]:\\n span = b[\\\"rpm\\\"] - a[\\\"rpm\\\"]\\n if span == 0:\\n return a[\\\"speed_kn\\\"], a[\\\"lph\\\"]\\n f = (rpm - a[\\\"rpm\\\"]) / span\\n speed = a[\\\"speed_kn\\\"] + f * (b[\\\"speed_kn\\\"] - a[\\\"speed_kn\\\"])\\n lph = a[\\\"lph\\\"] + f * (b[\\\"lph\\\"] - a[\\\"lph\\\"])\\n return speed, lph\\n row = table[-1]\\n return row[\\\"speed_kn\\\"], row[\\\"lph\\\"]\\n\\n\\ndef main():\\n inp = json.load(open(sys.argv[1]))\\n tank = inp[\\\"tank_l\\\"]\\n fuel = inp[\\\"fuel_l\\\"]\\n reserve_pct = inp[\\\"reserve_pct\\\"]\\n table = sorted(inp[\\\"burn_table\\\"], key=lambda r: r[\\\"rpm\\\"])\\n legs = inp[\\\"legs\\\"]\\n\\n reserve_l = tank * reserve_pct / 100.0\\n\\n remaining = fuel\\n out_legs = []\\n reserve_breach_leg = None\\n runs_dry_leg = None\\n dry_after = None\\n\\n for idx, leg in enumerate(legs):\\n speed, lph = interp(table, leg[\\\"rpm\\\"])\\n sog = speed + leg[\\\"current_kn\\\"]\\n if sog <= 0:\\n out_legs.append({\\n \\\"sog_kn\\\": round(sog, 3),\\n \\\"hours\\\": None,\\n \\\"fuel_l\\\": None,\\n \\\"remaining_l\\\": round(remaining, 2),\\n })\\n if runs_dry_leg is None:\\n runs_dry_leg = idx\\n dry_after = 0.0\\n if reserve_breach_leg is None and remaining < reserve_l:\\n reserve_breach_leg = idx\\n break\\n hours = leg[\\\"distance_nm\\\"] / sog\\n leg_fuel = lph * hours\\n start_remaining = remaining\\n if runs_dry_leg is None and leg_fuel > start_remaining:\\n runs_dry_leg = idx\\n dry_after = start_remaining / lph * sog\\n remaining -= leg_fuel\\n out_legs.append({\\n \\\"sog_kn\\\": round(sog, 3),\\n \\\"hours\\\": round(hours, 3),\\n \\\"fuel_l\\\": round(leg_fuel, 2),\\n \\\"remaining_l\\\": round(remaining, 2),\\n })\\n if reserve_breach_leg is None and remaining < reserve_l:\\n reserve_breach_leg = idx\\n\\n # economy\\n best = None\\n best_ratio = None\\n for row in table:\\n ratio = row[\\\"speed_kn\\\"] / row[\\\"lph\\\"]\\n r = round(ratio, 6)\\n if best_ratio is None or r > best_ratio:\\n best_ratio = r\\n best = row\\n economy_rpm = best[\\\"rpm\\\"]\\n economy_nm_per_l = best[\\\"speed_kn\\\"] / best[\\\"lph\\\"]\\n economy_range_nm = max(0.0, fuel - reserve_l) * economy_nm_per_l\\n\\n answer = {\\n \\\"reserve_l\\\": round(reserve_l, 2),\\n \\\"legs\\\": out_legs,\\n \\\"reserve_breach_leg\\\": reserve_breach_leg,\\n \\\"runs_dry_leg\\\": runs_dry_leg,\\n \\\"dry_after_nm_into_leg\\\": None if dry_after is None else round(dry_after, 2),\\n \\\"economy_rpm\\\": economy_rpm,\\n \\\"economy_nm_per_l\\\": round(economy_nm_per_l, 4),\\n \\\"economy_range_nm\\\": round(economy_range_nm, 2),\\n }\\n\\n json.dump(answer, open(sys.argv[2], \\\"w\\\"), indent=1)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"}"
}
}
],
"role": "assistant",
"reasoning_content": "Let me carefully parse the rules.\n\nRule 1: sort table by rpm, interpolate speed and lph linearly at given rpm, clamp outside range.\n\nRule 2: Each leg in order: SOG = speed + current_kn. If SOG <= 0, leg cannot be run: append {\"sog_kn\", \"hours\": null, \"fuel_l\": null, \"remaining_l\"} and stop processing legs. This leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet.\n\nHmm, \"this leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet\". So if SOG<=0 and no dry leg yet, then runs_dry_leg = this leg index, dry_after_nm_into_leg = 0.\n\nOtherwise hours = distance/SOG, fuel = lph*hours, remaining -= fuel.\n\nRule 3: reserve_l = tank*pct/100. reserve_breach_leg = index of first leg after which remaining < reserve, or null. runs_dry_leg = first leg whose fuel exceeds the fuel left at its start, and dry_after_nm_into_leg = fuel left at its start / lph * SOG; both null if never.\n\nSo runs_dry_leg: first leg where fuel > remaining at start of leg. dry_after_nm_into_leg = (fuel left at start)/lph * SOG. That's the distance into the leg where fuel runs out.\n\nNote: if SOG<=0 leg, it's the dry leg with dry_after 0 (if none yet). But also rule 3 says runs_dry_leg is first leg whose fuel exceeds fuel left at start. For SOG<=0 leg, fuel is null... but rule 2 says it's the dry leg if none yet. So we need to handle: when SOG<=0, if runs_dry_leg is None, set runs_dry_leg = index, dry_after = 0.\n\nLet me think about ordering: process legs in order. For each leg, compute speed/lph. SOG. If SOG<=0: append entry, if runs_dry_leg is None: runs_dry_leg=idx, dry_after=0. Stop.\n\nElse compute hours, fuel. Check if fuel > remaining_at_start (remaining before subtracting). If runs_dry_leg is None and fuel > remaining_at_start: runs_dry_leg=idx, dry_after = remaining_at_start/lph*SOG. Then remaining -= fuel. Continue.\n\nreserve_breach_leg: first leg after which remaining < reserve. So after subtracting, check remaining < reserve; if reserve_breach_leg None, set to idx.\n\nNote reserve breach check happens for legs that are run. For SOG<=0 leg, remaining unchanged, but we stop. Should we check reserve breach for that leg? The leg is appended with remaining_l. Hmm. \"first leg after which remaining < reserve\". For the dry leg, remaining is unchanged. Probably we should still check? Let's think: the leg is processed (appended). After it, remaining is whatever. If remaining < reserve, then reserve_breach_leg could be that leg. But typically reserve breach would occur earlier. Let's just check after each appended leg including the SOG<=0 one. Actually careful: rule 2 says stop processing legs. The reserve breach is computed over legs processed. I'll check after each leg appended.\n\nHmm, but there's ambiguity. Let's just implement: after computing remaining for a leg (including SOG<=0 where remaining unchanged), check breach.\n\nActually for SOG<=0, remaining_l = remaining (unchanged). Let's include the check.\n\nLet me compute the example.\n\nTable sorted by rpm:\n1000: 2.83, 4.9\n1400: 3.96, 6.16\n1800: 5.09, 8.15\n2200: 6.22, 10.99\n2600: 7.36, 14.79\n3000: 8.15, 19.66\n3400: 10.15, 25.68\n\nLeg1: rpm 2100, between 1800 and 2200. frac = (2100-1800)/(2200-1800)=300/400=0.75. speed = 5.09 + 0.75*(6.22-5.09)=5.09+0.75*1.13=5.09+0.8475=5.9375. lph = 8.15+0.75*(10.99-8.15)=8.15+0.75*2.84=8.15+2.13=10.28. SOG = 5.9375-0.9=5.0375. hours=17.7/5.0375=3.5136... let's compute: 17.7/5.0375 = 3.51365... fuel = 10.28*3.51365 = 36.12... remaining = 254 - 36.12 = 217.88.\n\nLet me just write the code and run.\n\nEconomy: row with greatest speed_kn/lph. Compute ratios:\n1000: 2.83/4.9=0.577551\n1400: 3.96/6.16=0.642857\n1800: 5.09/8.15=0.624540\n2200: 6.22/10.99=0.565969\n2600: 7.36/14.79=0.497634\n3000: 8.15/19.66=0.414547\n3400: 10.15/25.68=0.395249\nMax is 1400: 0.642857. economy_rpm=1400, economy_nm_per_l=0.6429 (4 decimals). economy_range_nm = max(0, 254 - 60)*0.642857 = 194*0.642857=124.714... = 124.71.\n\nreserve_l = 300*20/100=60.\n\nNow let me write solve.py.\n\nRounding: round speeds and hours to 3, litres and nm to 2, economy_nm_per_l to 4.\n\nsog_kn round 3, hours round 3, fuel_l round 2, remaining_l round 2, dry_after_nm_into_leg round 2 (nm), economy_range_nm round 2.\n\nLet me write it.\n\nInterpolation clamp: if rpm <= first rpm, use first row. If rpm >= last, use last.\n\nLet me code.\n\nFor runs_dry_leg detection: fuel > remaining_at_start. Note remaining_at_start could be negative already (remaining may go negative, keep going). Then fuel > negative always true, so runs_dry_leg would be set at first leg where remaining negative. That's fine per rules.\n\nLet me write.\n\nAlso need to handle: what if SOG<=0 leg occurs and runs_dry_leg already set? Then just append and stop.\n\nLet me write the code."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542503,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 1736,
"completion_tokens": 2604,
"total_tokens": 4340,
"prompt_tokens_details": {
"cached_tokens": 374
},
"cache_read_input_tokens": 374,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 1560,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.0035362049999999997
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 2,
"tool": "write_file",
"args": {
"path": "solve.py",
"content": "import json\nimport sys\n\n\ndef interp(table, rpm):\n # table sorted by rpm\n if rpm <= table[0][\"rpm\"]:\n row = table[0]\n return row[\"speed_kn\"], row[\"lph\"]\n if rpm >= table[-1][\"rpm\"]:\n row = table[-1]\n return row[\"speed_kn\"], row[\"lph\"]\n for i in range(len(table) - 1):\n a = table[i]\n b = table[i + 1]\n if a[\"rpm\"] <= rpm <= b[\"rpm\"]:\n span = b[\"rpm\"] - a[\"rpm\"]\n if span == 0:\n return a[\"speed_kn\"], a[\"lph\"]\n f = (rpm - a[\"rpm\"]) / span\n speed = a[\"speed_kn\"] + f * (b[\"speed_kn\"] - a[\"speed_kn\"])\n lph = a[\"lph\"] + f * (b[\"lph\"] - a[\"lph\"])\n return speed, lph\n row = table[-1]\n return row[\"speed_kn\"], row[\"lph\"]\n\n\ndef main():\n inp = json.load(open(sys.argv[1]))\n tank = inp[\"tank_l\"]\n fuel = inp[\"fuel_l\"]\n reserve_pct = inp[\"reserve_pct\"]\n table = sorted(inp[\"burn_table\"], key=lambda r: r[\"rpm\"])\n legs = inp[\"legs\"]\n\n reserve_l = tank * reserve_pct / 100.0\n\n remaining = fuel\n out_legs = []\n reserve_breach_leg = None\n runs_dry_leg = None\n dry_after = None\n\n for idx, leg in enumerate(legs):\n speed, lph = interp(table, leg[\"rpm\"])\n sog = speed + leg[\"current_kn\"]\n if sog <= 0:\n out_legs.append({\n \"sog_kn\": round(sog, 3),\n \"hours\": None,\n \"fuel_l\": None,\n \"remaining_l\": round(remaining, 2),\n })\n if runs_dry_leg is None:\n runs_dry_leg = idx\n dry_after = 0.0\n if reserve_breach_leg is None and remaining < reserve_l:\n reserve_breach_leg = idx\n break\n hours = leg[\"distance_nm\"] / sog\n leg_fuel = lph * hours\n start_remaining = remaining\n if runs_dry_leg is None and leg_fuel > start_remaining:\n runs_dry_leg = idx\n dry_after = start_remaining / lph * sog\n remaining -= leg_fuel\n out_legs.append({\n \"sog_kn\": round(sog, 3),\n \"hours\": round(hours, 3),\n \"fuel_l\": round(leg_fuel, 2),\n \"remaining_l\": round(remaining, 2),\n })\n if reserve_breach_leg is None and remaining < reserve_l:\n reserve_breach_leg = idx\n\n # economy\n best = None\n best_ratio = None\n for row in table:\n ratio = row[\"speed_kn\"] / row[\"lph\"]\n r = round(ratio, 6)\n if best_ratio is None or r > best_ratio:\n best_ratio = r\n best = row\n economy_rpm = best[\"rpm\"]\n economy_nm_per_l = best[\"speed_kn\"] / best[\"lph\"]\n economy_range_nm = max(0.0, fuel - reserve_l) * economy_nm_per_l\n\n answer = {\n \"reserve_l\": round(reserve_l, 2),\n \"legs\": out_legs,\n \"reserve_breach_leg\": reserve_breach_leg,\n \"runs_dry_leg\": runs_dry_leg,\n \"dry_after_nm_into_leg\": None if dry_after is None else round(dry_after, 2),\n \"economy_rpm\": economy_rpm,\n \"economy_nm_per_l\": round(economy_nm_per_l, 4),\n \"economy_range_nm\": round(economy_range_nm, 2),\n }\n\n json.dump(answer, open(sys.argv[2], \"w\"), indent=1)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
"result": "wrote 3221 chars"
},
{
"turn": 3,
"response": {
"id": "chatcmpl-ce857eea",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_0eca03dfed194d1693fc6c9f",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && python3 solve.py input.json answer.json && cat answer.json\"}"
}
}
],
"role": "assistant",
"reasoning_content": "Let me run it and check.\n\nWait, there's a subtlety about the dry leg. Rule 2 says if SOG <= 0, append and stop; \"this leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet\". So a dry leg can be a SOG<=0 leg. But also rule 3 says runs_dry_leg = first leg whose fuel exceeds the fuel left at its start. So the dry leg determination happens in both cases. Let me re-read.\n\nRule 2: \"If SOG <= 0 the leg cannot be run: append {...} and stop processing legs (this leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet).\"\n\nSo if a leg has SOG<=0 and no dry leg yet, it becomes the dry leg with dry_after=0.\n\nRule 3: runs_dry_leg = first leg whose fuel exceeds the fuel left at its start.\n\nSo for SOG<=0 legs, we set dry leg with dry_after 0. For normal legs, if fuel > start remaining, dry leg with computed dry_after.\n\nMy code handles both. Good.\n\nBut wait, in the SOG<=0 case, should reserve_breach be checked? The leg can't be run, remaining unchanged. Rule 3 says reserve_breach_leg = first leg after which remaining < reserve. Since remaining unchanged, if it was already < reserve it would have been caught earlier. Actually if remaining < reserve before this leg, it would've been caught at previous leg. So no need. But harmless. Actually careful: if the first leg is SOG<=0, remaining = fuel which is >= reserve presumably. Fine.\n\nLet me run."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542517,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 2797,
"completion_tokens": 430,
"total_tokens": 3227,
"prompt_tokens_details": {
"cached_tokens": 374
},
"cache_read_input_tokens": 374,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 376,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.001245705
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 3,
"tool": "run_shell",
"args": {
"command": "cd . && python3 solve.py input.json answer.json && cat answer.json"
},
"result": "exit=0\nstdout:\n{\n \"reserve_l\": 60.0,\n \"legs\": [\n {\n \"sog_kn\": 5.037,\n \"hours\": 3.514,\n \"fuel_l\": 36.12,\n \"remaining_l\": 217.88\n },\n {\n \"sog_kn\": 10.45,\n \"hours\": 1.818,\n \"fuel_l\": 43.95,\n \"remaining_l\": 173.93\n },\n {\n \"sog_kn\": 4.938,\n \"hours\": 4.071,\n \"fuel_l\": 41.85,\n \"remaining_l\": 132.08\n },\n {\n \"sog_kn\": 4.19,\n \"hours\": 4.988,\n \"fuel_l\": 40.65,\n \"remaining_l\": 91.42\n },\n {\n \"sog_kn\": 5.975,\n \"hours\": 4.586,\n \"fuel_l\": 63.47,\n \"remaining_l\": 27.96\n },\n {\n \"sog_kn\": 6.55,\n \"hours\": 2.153,\n \"fuel_l\": 42.32,\n \"remaining_l\": -14.36\n },\n {\n \"sog_kn\": 8.175,\n \"hours\": 2.887,\n \"fuel_l\": 39.95,\n \"remaining_l\": -54.32\n }\n ],\n \"reserve_breach_leg\": 4,\n \"runs_dry_leg\": 5,\n \"dry_after_nm_into_leg\": 9.31,\n \"economy_rpm\": 1400,\n \"economy_nm_per_l\": 0.6429,\n \"economy_range_nm\": 124.71\n}\nstderr:\n"
},
{
"turn": 4,
"response": {
"id": "chatcmpl-c4a9ae5a",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_8dab5f16a87d49cf84814674",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && cat > t.json <<'EOF'\\n{\\\"tank_l\\\":100,\\\"fuel_l\\\":50,\\\"reserve_pct\\\":10,\\n\\\"burn_table\\\":[{\\\"rpm\\\":1000,\\\"speed_kn\\\":5,\\\"lph\\\":5},{\\\"rpm\\\":2000,\\\"speed_kn\\\":10,\\\"lph\\\":20}],\\n\\\"legs\\\":[{\\\"distance_nm\\\":10,\\\"rpm\\\":1500,\\\"current_kn\\\":-8},{\\\"distance_nm\\\":10,\\\"rpm\\\":1000,\\\"current_kn\\\":0}]}\\nEOF\\npython3 solve.py t.json o.json && cat o.json\"}"
}
}
],
"role": "assistant",
"reasoning_content": "Let me verify the logic. The dry leg: leg 5 (index 5), start remaining = 27.96, lph at 2500 rpm. Let me check: rpm 2500 between 2200 (6.22, 10.99) and 2600 (7.36, 14.79). f = 300/400 = 0.75. speed = 6.22 + 0.75*1.14 = 6.22+0.855=7.075. lph = 10.99 + 0.75*3.8 = 10.99+2.85=13.84. sog = 7.075 - 1.1 = 5.975. hours = 27.4/5.975 = 4.586. fuel = 13.84*4.586 = 63.47. Yes.\n\ndry_after = 27.96/13.84*5.975 = 2.0202*5.975 = 12.07? Wait let me compute: 27.96/13.84 = 2.0202. *5.975 = 12.07. But output says 9.31. Hmm.\n\nWait, start remaining for leg 5 is 27.96? Actually remaining after leg 4 is 27.96. So start remaining = 27.96. dry_after = 27.96/13.84*5.975.\n\n27.96/13.84 = 2.02023. *5.975 = 12.071. But output 9.31. Let me recheck. Hmm, maybe I mislabeled. Let me recompute leg 4 (index 4): rpm 2500, current -1.1, distance 27.4. Wait leg index 4 is distance 27.4 rpm 2500 current -1.1. That's the one with remaining 27.96. Leg index 5 is distance 14.1 rpm 3000 current -1.6.\n\nSo runs_dry_leg should be index 5? Let's check leg 4: start remaining = 91.42, fuel = 63.47, so 63.47 < 91.42, not dry. Leg 5: start remaining 27.96, fuel = lph(3000)*hours. rpm 3000: speed 8.15, lph 19.66. sog = 8.15-1.6=6.55. hours=14.1/6.55=2.1527. fuel=19.66*2.1527=42.32. 42.32 > 27.96, so dry. dry_after = 27.96/19.66*6.55 = 1.4222*6.55 = 9.315. Yes 9.31. Good, I confused myself.\n\nNow reserve_breach_leg: first leg after which remaining < reserve (60). After leg 4 remaining 27.96 < 60. After leg 3 remaining 132.08 > 60. So index 4. Correct.\n\nEconomy: ratios: 1000: 2.83/4.9=0.5776; 1400: 3.96/6.16=0.6429; 3000: 8.15/19.66=0.4145; 2200: 6.22/10.99=0.5660; 2600: 7.36/14.79=0.4976; 1800: 5.09/8.15=0.6245; 3400: 10.15/25.68=0.3953. Max 0.6429 at 1400. Correct.\n\neconomy_range = (254-60)*0.6429 = 194*0.6429 = 124.72. Output 124.71 (rounding). Fine.\n\nNow consider the dry leg handling when SOG<=0. The spec: \"If SOG <= 0 the leg cannot be run: append {...} and stop processing legs (this leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet).\" So if runs_dry_leg is None, set it to this leg with dry_after 0. My code does that. Good.\n\nBut also reserve_breach_leg for the SOG<=0 leg? The spec says reserve_breach_leg = index of first leg after which remaining < reserve. For a leg that can't be run, remaining doesn't change. Hmm. Should we consider it? The spec says \"first leg after which remaining < reserve\". For a non-run leg, remaining stays same. If remaining < reserve already, then... Actually the breach would have been detected earlier. But if remaining >= reserve at that point, no breach. My code checks remaining < reserve_l for the SOG<=0 leg too, which is harmless since it would already have been caught. Actually if remaining < reserve at start of a SOG<=0 leg, it would have been caught at the previous leg already. So fine.\n\nWait, but there's subtlety: what if the very first leg has SOG<=0 and remaining < reserve? Then reserve_breach_leg would be 0 in my code. Is that right? \"first leg after which remaining < reserve\" - after leg 0 remaining is unchanged = fuel. If fuel < reserve, then breach at leg 0. Reasonable.\n\nNow the dry_after rounding: spec says round nm to 2. Good.\n\nOne concern: \"runs_dry_leg = first leg whose fuel exceeds the fuel left at its start\". For SOG<=0 leg, fuel is null, so it can't exceed. But spec explicitly says the SOG<=0 leg is the dry leg if none yet. So handled.\n\nAnother concern: economy tie-breaking at 6 decimals, ties -> lower rpm. My loop iterates sorted by rpm ascending, and uses strict >, so first (lowest rpm) wins ties. Good.\n\nLet me double check interpolation clamping and the \"sort by rpm\" - done.\n\nRounding: speeds and hours to 3, litres and nm to 2. sog_kn rounded to 3. remaining_l to 2. fuel_l to 2. hours to 3. dry_after to 2. reserve_l to 2. economy_nm_per_l to 4. economy_range_nm to 2. Good.\n\nPotential issue: negative zero or rounding of sog. Fine.\n\nLet me also consider: should remaining_l in the SOG<=0 leg be rounded? Yes.\n\nI think it's good. Let me test a fresh scenario mentally with SOG<=0.\n\nLet me create a quick test."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542521,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 3280,
"completion_tokens": 1632,
"total_tokens": 4912,
"prompt_tokens_details": {
"cached_tokens": 2668
},
"cache_read_input_tokens": 2668,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 1466,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.00216201
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 4,
"tool": "run_shell",
"args": {
"command": "cd . && cat > t.json <<'EOF'\n{\"tank_l\":100,\"fuel_l\":50,\"reserve_pct\":10,\n\"burn_table\":[{\"rpm\":1000,\"speed_kn\":5,\"lph\":5},{\"rpm\":2000,\"speed_kn\":10,\"lph\":20}],\n\"legs\":[{\"distance_nm\":10,\"rpm\":1500,\"current_kn\":-8},{\"distance_nm\":10,\"rpm\":1000,\"current_kn\":0}]}\nEOF\npython3 solve.py t.json o.json && cat o.json"
},
"result": "exit=0\nstdout:\n{\n \"reserve_l\": 10.0,\n \"legs\": [\n {\n \"sog_kn\": -0.5,\n \"hours\": null,\n \"fuel_l\": null,\n \"remaining_l\": 50\n }\n ],\n \"reserve_breach_leg\": null,\n \"runs_dry_leg\": 0,\n \"dry_after_nm_into_leg\": 0.0,\n \"economy_rpm\": 1000,\n \"economy_nm_per_l\": 1.0,\n \"economy_range_nm\": 40.0\n}\nstderr:\n"
},
{
"turn": 5,
"response": {
"id": "chatcmpl-74b5d905",
"choices": [
{
"index": 0,
"message": {
"content": "The solution works correctly. Let me verify the interpolation logic once more against the spec with a quick sanity check on the first leg:",
"tool_calls": [
{
"index": 0,
"id": "call_00308cf674594794b3804c41",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && python3 -c \\\"\\n# leg0 rpm 2100 between 1800(5.09,8.15) and 2200(6.22,10.99)\\nf=(2100-1800)/400\\nsp=5.09+f*(6.22-5.09); lp=8.15+f*(10.99-8.15)\\nsog=sp-0.9; h=17.7/sog\\nprint(sp,lp,sog,h,lp*h)\\n\\\"\"}"
}
}
],
"role": "assistant",
"reasoning_content": "The dry leg logic: when SOG<=0, the leg cannot be run. The spec says \"this leg is the dry leg if none yet, with dry_after_nm_into_leg 0 if none yet\". So runs_dry_leg = idx, dry_after = 0. That's what I did. Good.\n\nBut wait \u2014 should the dry leg be set even if a previous leg already ran dry? \"this leg is the dry leg if none yet\" \u2014 yes only if none yet. Good.\n\nAlso reserve_breach for the dry leg: spec says reserve_breach_leg = index of first leg after which remaining < reserve. For a dry leg, remaining unchanged. Hmm, should we check? The spec rule 2 says append and stop. Rule 3 defines reserve_breach independently. I check remaining < reserve after the dry leg too. That seems reasonable but ambiguous. Actually for a dry leg, remaining doesn't change, so if it was already below reserve it would have been caught earlier. So it's fine.\n\nLet me double check economy tie-breaking: \"compared at 6 decimals; ties -> lower rpm\". My loop iterates sorted table ascending rpm, and uses strict > so ties keep earlier (lower rpm). Good.\n\nEverything looks correct. Let me verify the first three legs manually.\n\nLeg 0: rpm 2100. Between 1800 (5.09, 8.15) and 2200 (6.22, 10.99). f=(2100-1800)/400=0.75. speed=5.09+0.75*1.13=5.09+0.8475=5.9375. lph=8.15+0.75*2.84=8.15+2.13=10.28. sog=5.9375-0.9=5.0375 -> 5.037 (round half? 5.0375 rounds to 5.038 with banker's? Python round(5.0375,3)... floating). Hmm output 5.037. Let me check: 5.0375 in float is slightly less maybe. Tolerance 0.011 so fine.\n\nhours=17.7/5.0375=3.5136 -> 3.514. fuel=10.28*3.5136=36.12. Good.\n\nLooks fine. Done."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542530,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 3595,
"completion_tokens": 682,
"total_tokens": 4277,
"prompt_tokens_details": {
"cached_tokens": 1920
},
"cache_read_input_tokens": 1920,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 508,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.0013353
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 5,
"tool": "run_shell",
"args": {
"command": "cd . && python3 -c \"\n# leg0 rpm 2100 between 1800(5.09,8.15) and 2200(6.22,10.99)\nf=(2100-1800)/400\nsp=5.09+f*(6.22-5.09); lp=8.15+f*(10.99-8.15)\nsog=sp-0.9; h=17.7/sog\nprint(sp,lp,sog,h,lp*h)\n\""
},
"result": "exit=0\nstdout:\n5.9375 10.280000000000001 5.0375 3.513647642679901 36.12029776674939\n\nstderr:\n"
},
{
"turn": 6,
"response": {
"id": "chatcmpl-afb9e504",
"choices": [
{
"index": 0,
"message": {
"content": "Done. `solve.py` implements the full spec:\n\n- **Interpolation**: sorts `burn_table` by rpm, linearly interpolates `speed_kn` and `lph` at each leg's rpm, clamping outside the range.\n- **Legs**: computes `SOG = speed + current_kn`; if `SOG <= 0` appends a null-hours/fuel leg and stops (marking it as the dry leg with `dry_after_nm_into_leg = 0` if none yet). Otherwise `hours = distance/SOG`, `fuel = lph*hours`, decrements remaining (allowing negatives).\n- **Reserve/dry**: `reserve_l = tank*pct/100`; `reserve_breach_leg` = first leg ending below reserve; `runs_dry_leg` = first leg whose fuel exceeds fuel at its start, with `dry_after_nm_into_leg = start_remaining/lph*SOG`.\n- **Economy**: picks the row with max `speed_kn/lph` (rounded to 6 dp, ties \u2192 lower rpm), computes `economy_nm_per_l` and `economy_range_nm = max(0, fuel_l - reserve)*ratio`.\n- **Rounding**: speeds/hours to 3, litres/nm to 2, nm per litre to 4.\n\nVerified on `input.json` (reserve 60.0, breach leg 4, dry leg 5 at 9.31 nm, economy 1400 rpm / 0.6429 / 124.71 nm) and on a synthetic edge case with a non-runnable leg.",
"role": "assistant",
"reasoning_content": "Matches. Done."
},
"finish_reason": "stop",
"logprobs": null,
"raw_output": null
}
],
"created": 1792542534,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 3824,
"completion_tokens": 357,
"total_tokens": 4181,
"prompt_tokens_details": {
"cached_tokens": 1920
},
"cache_read_input_tokens": 1920,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 4,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.001014
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
}
]
}
]