GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Classify 12 faulty sensors (frozen/°F/spike/gap/drift) with fault start hours
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 46s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Task: find faulty sensors `readings.csv` has hourly temperature readings (sensor_id, hour 0–167, temp_c) from 40 indoor sensors over one week. Normal sensors follow a daily cycle around 18–24 °C with small noise. Some sensors developed exactly one fault each, starting at some hour. Fault types: `frozen` (value stops changing), `unit_fahrenheit` (starts reporting °F), `spike` (a single absurd reading), `offline_gap` (a run of missing hours), `drift` (steady upward drift). Write `faults.json`: `{"S07": {"type": "drift", "start_hour": 55}, ...}` listing ONLY faulty sensors. No network.
Needs per-sensor time-series reasoning; drift and °F shift are easy to confuse, and false positives on noisy healthy sensors are penalised (zero allowed). Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: 1. Fault type correct for at least 11 of the 12 faulty sensors. 2. Zero false positives: no healthy sensor may appear in faults.json. 3. start_hour (integer) within ±3 hours of the true start for at least 9 sensors (±8 hours allowed for drift faults).
Setup notes: Use Case Scout batch3 case c04-sensor-anomalies (offline fixture). Prepared with batch3/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR/faults.json KEYDIR/key.json`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: pass. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 5 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.05
- Wall time
- 46s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 102,716 | 1,499 | $0.05 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. Fault type correct for at least 11 of the 12 faulty sensors. 2. Zero false positives: no healthy sensor may appear in faults.json. 3. start_hour (integer) within ±3 hours of the true start for at least 9 sensors (±8 hours allowed for drift faults).
- 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
{"type": "thread.started", "thread_id": "01a16419-ca34-7ab3-8d99-c97b71ed89d6"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the readings for each fault pattern, check the onset hours, and write `faults.json`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; head -8 readings.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; head -8 readings.csv'", "aggregated_output": "total 108\ndrwxr-xr-x 6 bob bob 4096 Oct 22 10:52 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:52 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:52 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:52 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:52 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:52 .git\n-rw-r--r-- 1 bob bob 598 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 95465 Oct 22 10:08 readings.csv\nsensor_id,hour,temp_c\r\nS05,114,19.25\r\nS03,141,21.33\r\nS18,77,22.75\r\nS18,16,17.02\r\nS13,88,20.82\r\nS31,127,28.54\r\nS06,19,14.83\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,collections, numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')): D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,d in sorted(D.items()):\n a=np.array([d.get(i,np.nan) for i in range(168)])\n missing=[i for i in range(168) if i not in d or np.isnan(d[i])]\n runs=[]; st=0\n for i in range(1,169):\n if i==168 or a[i]\"'!=a[st]:\n if i-st>=3:runs.append((st,i-1,float(a[st])))\n st=i\n means=[round(float(np.nanmean(a[i:i+24])),2) for i in range(0,168,24)]\n print(s,'\"'range',round(np.nanmin(a),2),round(np.nanmax(a),2),'missing',missing,'flat',runs,'daily',means)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,collections, numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')): D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,d in sorted(D.items()):\n a=np.array([d.get(i,np.nan) for i in range(168)])\n missing=[i for i in range(168) if i not in d or np.isnan(d[i])]\n runs=[]; st=0\n for i in range(1,169):\n if i==168 or a[i]\"'!=a[st]:\n if i-st>=3:runs.append((st,i-1,float(a[st])))\n st=i\n means=[round(float(np.nanmean(a[i:i+24])),2) for i in range(0,168,24)]\n print(s,'\"'range',round(np.nanmin(a),2),round(np.nanmax(a),2),'missing',missing,'flat',runs,'daily',means)\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections, numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')): D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,d in sorted(D.items()):\n a=np.array([d.get(i,np.nan) for i in range(168)])\n missing=[i for i in range(168) if i not in d or np.isnan(d[i])]\n runs=[]; st=0\n for i in range(1,169):\n if i==168 or a[i]\"'!=a[st]:\n if i-st>=3:runs.append((st,i-1,float(a[st])))\n st=i\n means=[round(float(np.nanmean(a[i:i+24])),2) for i in range(0,168,24)]\n print(s,'\"'range',round(np.nanmin(a),2),round(np.nanmax(a),2),'missing',missing,'flat',runs,'daily',means)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections, numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')): D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,d in sorted(D.items()):\n a=np.array([d.get(i,np.nan) for i in range(168)])\n missing=[i for i in range(168) if i not in d or np.isnan(d[i])]\n runs=[]; st=0\n for i in range(1,169):\n if i==168 or a[i]\"'!=a[st]:\n if i-st>=3:runs.append((st,i-1,float(a[st])))\n st=i\n means=[round(float(np.nanmean(a[i:i+24])),2) for i in range(0,168,24)]\n print(s,'\"'range',round(np.nanmin(a),2),round(np.nanmax(a),2),'missing',missing,'flat',runs,'daily',means)\nPY\"", "aggregated_output": "S01 range -19.51 26.82 missing [] flat [] daily [23.37, 23.28, 23.35, 23.3, 21.64, 23.24, 23.21]\nS02 range 15.74 22.93 missing [] flat [] daily [19.48, 19.39, 19.55, 19.54, 19.45, 19.46, 19.4]\nS03 range 19.92 26.69 missing [] flat [] daily [23.26, 23.42, 23.34, 23.43, 23.29, 23.34, 23.31]\nS04 range 14.54 21.53 missing [] flat [(117, 167, 15.92)] daily [18.17, 18.21, 18.18, 18.26, 18.19, 15.92, 15.92]\nS05 range 19.02 25.76 missing [] flat [] daily [22.29, 22.26, 22.41, 22.33, 22.21, 22.27, 22.24]\nS06 range 14.82 21.51 missing [] flat [(67, 167, 15.05)] daily [18.2, 18.14, 17.93, 15.05, 15.05, 15.05, 15.05]\nS07 range 19.21 25.86 missing [] flat [(41, 167, 19.21)] daily [22.62, 22.25, 19.21, 19.21, 19.21, 19.21, 19.21]\nS08 range 16.51 23.34 missing [] flat [] daily [20.03, 19.92, 19.97, 19.87, 19.99, 19.93, 19.9]\nS09 range -22.99 23.3 missing [] flat [] daily [19.84, 19.68, 19.76, 19.81, 18.17, 19.7, 19.77]\nS10 range 16.5 24.08 missing [] flat [] daily [20.15, 20.16, 20.19, 20.31, 20.22, 20.27, 20.16]\nS11 range 17.3 24.38 missing [] flat [] daily [20.82, 20.8, 20.88, 20.92, 20.79, 20.91, 20.76]\nS12 range 15.15 22.34 missing [] flat [] daily [18.6, 18.73, 18.55, 18.65, 18.61, 18.69, 18.65]\nS13 range 19.57 26.98 missing [] flat [] daily [23.47, 23.47, 23.31, 23.44, 23.39, 23.4, 23.38]\nS14 range 16.3 22.8 missing [] flat [] daily [19.54, 19.47, 19.6, 19.56, 19.52, 19.57, 19.58]\nS15 range 14.68 21.72 missing [] flat [] daily [18.36, 18.44, 18.33, 18.33, 18.46, 18.41, 18.44]\nS16 range 16.9 23.84 missing [] flat [] daily [20.38, 20.41, 20.36, 20.25, 20.4, 20.32, 20.39]\nS17 range 16.74 23.68 missing [] flat [] daily [20.23, 20.34, 20.32, 20.3, 20.29, 20.3, 20.22]\nS18 range 15.98 23.11 missing [] flat [] daily [19.62, 19.65, 19.62, 19.63, 19.54, 19.53, 19.64]\nS19 range 17.77 24.92 missing [] flat [] daily [21.4, 21.5, 21.48, 21.4, 21.55, 21.43, 21.56]\nS20 range 18.49 25.38 missing [] flat [] daily [22.04, 22.18, 22.04, 22.02, 22.01, 22.05, 22.12]\nS21 range 18.44 25.14 missing [] flat [] daily [21.76, 21.79, 21.77, 21.7, 21.76, 21.88, 21.78]\nS22 range 16.67 73.93 missing [] flat [] daily [19.72, 21.61, 67.69, 67.4, 67.37, 67.42, 67.45]\nS23 range 19.15 26.68 missing [] flat [] daily [22.98, 22.91, 22.95, 22.99, 23.05, 22.92, 22.95]\nS24 range 16.74 23.87 missing [] flat [] daily [20.32, 20.32, 20.41, 20.29, 20.27, 20.36, 20.37]\nS25 range 16.78 23.79 missing [] flat [] daily [20.27, 20.35, 20.33, 20.4, 20.47, 20.37, 20.33]\nS26 range 20.56 81.11 missing [] flat [] daily [23.95, 24.01, 30.22, 75.12, 75.09, 75.32, 75.13]\nS27 range 17.53 24.76 missing [] flat [] daily [21.21, 21.15, 21.14, 21.28, 21.07, 21.26, 21.29]\nS28 range 18.21 25.13 missing [] flat [] daily [21.62, 21.67, 21.73, 21.63, 21.57, 21.66, 21.67]\nS29 range 19.84 26.67 missing [] flat [] daily [23.25, 23.27, 23.32, 23.22, 23.33, 23.32, 23.24]\nS30 range 18.87 25.69 missing [] flat [] daily [22.36, 22.27, 22.32, 22.25, 22.34, 22.38, 22.37]\nS31 range 16.73 30.36 missing [] flat [] daily [20.4, 20.44, 20.64, 22.03, 23.89, 25.76, 27.6]\nS32 range 19.47 26.27 missing [] flat [] daily [22.84, 22.76, 22.83, 22.7, 22.73, 22.81, 22.82]\nS33 range 14.75 21.83 missing [] flat [] daily [18.23, 18.38, 18.35, 18.26, 18.33, 18.4, 18.37]\nS34 range 17.28 24.13 missing [36, 37, 38, 39, 40, 41, 42, 43, 44, 45] flat [] daily [20.6, 22.13, 20.56, 20.68, 20.7, 20.69, 20.47]\nS35 range 16.34 23.14 missing [] flat [] daily [19.64, 19.82, 19.77, 19.69, 19.62, 19.76, 19.76]\nS36 range 18.78 25.71 missing [] flat [] daily [22.28, 22.36, 22.22, 22.41, 22.42, 22.31, 22.37]\nS37 range 15.51 22.48 missing [] flat [] daily [19.07, 19.19, 19.09, 19.16, 19.08, 19.09, 19.09]\nS38 range 19.9 80.26 missing [] flat [] daily [23.65, 23.66, 23.51, 23.52, 31.85, 74.43, 74.28]\nS39 range 14.98 21.88 missing [66, 67, 68, 69, 70, 71, 72, 73, 74, 75] flat [] daily [18.43, 18.49, 19.06, 18.14, 18.45, 18.29, 18.43]\nS40 range 18.34 31.8 missing [] flat [] daily [21.33, 21.32, 21.58, 23.41, 25.28, 27.13, 29.24]\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections,numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')):D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,st in [('S04',117),('S06',67),('S07',41)]:print(s,[(h,D[s][h]) for h in range(st-3,st+4)])\nfor s in ['S01','S09','S22','S26','S38']: print(s,[(h,v) for h,v in sorted(D[s].items()) if v<0 or v>40][:4])\nt=np.arange(168); base=np.column_stack([np.ones(168),np.sin(t*2*np.pi/24),np.cos(t*2*np.pi/24)])\nfor s in ['S31','S40']:\n y=np.array([D[s][i] for i in t]); fits=[]\n for st in range(10,150):\n X=np.column_stack([base,np.maximum(0,t-st)])\n c=np.linalg.lstsq(X,y,rcond=None)[0];err=np.sum((y-X@c)**2);fits.append((err,st,c[-1]))\n print(s,'best hinge fits',sorted(fits)[:8])\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections,numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')):D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nfor s,st in [('S04',117),('S06',67),('S07',41)]:print(s,[(h,D[s][h]) for h in range(st-3,st+4)])\nfor s in ['S01','S09','S22','S26','S38']: print(s,[(h,v) for h,v in sorted(D[s].items()) if v<0 or v>40][:4])\nt=np.arange(168); base=np.column_stack([np.ones(168),np.sin(t*2*np.pi/24),np.cos(t*2*np.pi/24)])\nfor s in ['S31','S40']:\n y=np.array([D[s][i] for i in t]); fits=[]\n for st in range(10,150):\n X=np.column_stack([base,np.maximum(0,t-st)])\n c=np.linalg.lstsq(X,y,rcond=None)[0];err=np.sum((y-X@c)**2);fits.append((err,st,c[-1]))\n print(s,'best hinge fits',sorted(fits)[:8])\nPY\"", "aggregated_output": "S04 [(114, 15.1), (115, 15.42), (116, 16.04), (117, 15.92), (118, 15.92), (119, 15.92), (120, 15.92)]\nS06 [(64, 15.26), (65, 15.52), (66, 15.07), (67, 15.05), (68, 15.05), (69, 15.05), (70, 15.05)]\nS07 [(38, 20.66), (39, 19.83), (40, 20.03), (41, 19.21), (42, 19.21), (43, 19.21), (44, 19.21)]\nS01 [(115, -19.51)]\nS09 [(113, -22.99)]\nS22 [(47, 66.08), (48, 67.36), (49, 69.35), (50, 70.94)]\nS26 [(69, 70.36), (70, 73.47), (71, 72.97), (72, 75.71)]\nS38 [(116, 69.24), (117, 71.89), (118, 71.92), (119, 73.58)]\nS31 best hinge fits [(np.float64(13.465182624856213), 62, np.float64(0.07669823047803126)), (np.float64(13.497532813125073), 63, np.float64(0.07746967476066446)), (np.float64(13.647971788339035), 61, np.float64(0.07594160840818676)), (np.float64(13.711751228751439), 64, np.float64(0.07825785605843416)), (np.float64(14.032934434674708), 60, np.float64(0.07519935583579018)), (np.float64(14.11969801846908), 65, np.float64(0.0790632073459947)), (np.float64(14.640592500766296), 59, np.float64(0.07446997990682291)), (np.float64(14.800366705423555), 66, np.float64(0.07988384948647953))]\nS40 best hinge fits [(np.float64(15.355854649401921), 57, np.float64(0.07971711236010792)), (np.float64(15.426541897184507), 58, np.float64(0.08045039354175909)), (np.float64(15.52021389897595), 56, np.float64(0.07899566202933483)), (np.float64(15.763472935558527), 59, np.float64(0.08119532437297927)), (np.float64(15.956546722380898), 55, np.float64(0.07828444288665937)), (np.float64(16.364593732814242), 60, np.float64(0.08195273275235371)), (np.float64(16.659428421764957), 54, np.float64(0.07758315855972331)), (np.float64(17.14982767319264), 61, np.float64(0.08272576892957195))]\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "I found 12 faulty sensors: three frozen, three reporting Fahrenheit, two spikes, two missing-hour gaps, and two drifts. I\u2019m estimating the drift onset hours by fitting the daily cycle plus an upward trend.\n"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json,csv,collections,numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')):D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nt=np.arange(168)\nfor s in ['S31','S40']:\n y=np.array([D[s][i] for i in t]); results=[]\n # An independent hourly baseline avoids assuming a perfectly sinusoidal daily cycle.\n base=np.eye(24)[t%24]\n for st in range(20,120):\n X=np.column_stack([base,np.maximum(0,t-st)]); c=np.linalg.lstsq(X,y,rcond=None)[0]\n results.append((round(float(np.sum((y-X@c)**2)),4),st))\n print(s,sorted(results)[:4])\nfaults={ 'S01':('spike',115),'S04':('frozen',117),'S06':('frozen',67),'S07':('frozen',41),'S09':('spike',113),'S22':('unit_fahrenheit',47),'S26':('unit_fahrenheit',69),'S31':('drift',62),'S34':('offline_gap',36),'S38':('unit_fahrenheit',116),'S39':('offline_gap',66),'S40':('drift',57)}\nwith open('faults.json','w') as f:json.dump({s:{'type':v[0],'start_hour':v[1]} for s,v in sorted(faults.items())},f,indent=2);f.write('\\\\n')\nassert len(json.load(open('faults.json')))==12\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json,csv,collections,numpy as np\nD=collections.defaultdict(dict)\nfor r in csv.DictReader(open('readings.csv')):D[r['sensor_id']][int(r['hour'])]=float(r['temp_c'])\nt=np.arange(168)\nfor s in ['S31','S40']:\n y=np.array([D[s][i] for i in t]); results=[]\n # An independent hourly baseline avoids assuming a perfectly sinusoidal daily cycle.\n base=np.eye(24)[t%24]\n for st in range(20,120):\n X=np.column_stack([base,np.maximum(0,t-st)]); c=np.linalg.lstsq(X,y,rcond=None)[0]\n results.append((round(float(np.sum((y-X@c)**2)),4),st))\n print(s,sorted(results)[:4])\nfaults={ 'S01':('spike',115),'S04':('frozen',117),'S06':('frozen',67),'S07':('frozen',41),'S09':('spike',113),'S22':('unit_fahrenheit',47),'S26':('unit_fahrenheit',69),'S31':('drift',62),'S34':('offline_gap',36),'S38':('unit_fahrenheit',116),'S39':('offline_gap',66),'S40':('drift',57)}\nwith open('faults.json','w') as f:json.dump({s:{'type':v[0],'start_hour':v[1]} for s,v in sorted(faults.items())},f,indent=2);f.write('\\\\n')\nassert len(json.load(open('faults.json')))==12\nPY\"", "aggregated_output": "S31 [(10.9478, 62), (11.0061, 63), (11.0918, 61), (11.2223, 64)]\nS40 [(13.7882, 57), (13.8443, 58), (13.9523, 56), (14.1611, 59)]\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Created [faults.json](faults.json) listing 12 faulty sensors with fault types and onset hours. Drift onset hours are estimated from the readings."}}
{"type": "turn.completed", "usage": {"input_tokens": 102716, "cached_input_tokens": 88320, "cache_write_input_tokens": 0, "output_tokens": 1499, "reasoning_output_tokens": 88}}