DeepSeek V4.1 Flash (Venice) (thinking on) on Monthly cohort retention + revenue from a messy orders CSV

[
 {
  "segment_dir": "./usecase-runner/runs/c03-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": "2026-10-19T20:57:42-0400",
   "num_turns": 6,
   "tool_calls": 6,
   "usage": {
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   ],
   "cost_usd_venice_list": 0.003863,
   "billed_reported_by_venice": 0.003863,
   "wall_seconds": 17.2,
   "final_message": "Done. `answer.json` is written.\n\n**Cleaning steps applied:**\n- Parsed both `YYYY-MM-DD` (2565 rows) and `DD/MM/YYYY` (133 rows) date formats.\n- Normalized `user_id` with `strip().upper()` (72 IDs had stray whitespace/lowercase).\n- Removed 42 exact duplicate rows (all duplicate `order_id`s were identical rows).\n- Kept only `status == \"paid\"` (dropped 126 refunded, 82 test).\n\n**Results:**\n- Cohorts by first paid month: Jan 148, Feb 216, Mar 228, Apr 217, May 246, Jun 116 users, with retention arrays from k=0 through 2026-06 (rounded to 4 dp).\n- Paid revenue by month: Jan 16862.20, Feb 29862.41, Mar 43820.16, Apr 47451.77, May 51556.03, Jun 36742.33.",
   "error": null,
   "fatal": false
  },
  "trace": [
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       "message": {
        "role": "assistant",
        "content": "I'll start by inspecting the data file.",
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          "type": "function",
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           "arguments": "{\"command\": \"ls -la; echo \\\"---\\\"; head -20 orders.csv; echo \\\"---\\\"; wc -l orders.csv\"}"
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     "venice_parameters": {
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      "disable_thinking": false,
      "enable_e2ee": true,
      "enable_web_search": "off",
      "enable_web_scraping": false,
      "enable_web_citations": false,
      "include_search_results_in_stream": false,
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      "enable_x_search": false,
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      "return_search_results_as_documents": false
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   {
    "turn": 1,
    "tool": "run_shell",
    "args": {
     "command": "ls -la; echo \"---\"; head -20 orders.csv; echo \"---\"; wc -l orders.csv"
    },
    "result": "exit=0\nstdout:\ntotal 116\ndrwxr-xr-x 2 box box   4096 Oct 19 20:57 .\ndrwxr-xr-x 5 box box   4096 Oct 19 20:57 ..\n-rw-r--r-- 1 box box    886 Oct 19 20:57 TASK.md\n-rw-r--r-- 1 box box 104118 Oct 19 20:57 orders.csv\n---\norder_id,user_id,order_date,amount_usd,status\nO001806,U00805,10/04/2026,36.53,paid\nO002655,U01199,2026-03-13,111.58,paid\nO002399,U01075,2026-02-12,171.97,paid\nO001414,U00638,2026-05-01,133.68,paid\nO002623,U01182,2026-04-14,59.11,paid\nO002652,U01198,2026-04-20,172.72,paid\nO002179,U00972,2026-02-26,74.73,paid\nO000245,U00109,2026-04-25,171.59,paid\nO001522,U00682,2026-06-07,76.41,paid\nO000487, u00220 ,2026-05-21,142.2,paid\nO002065,U00916,2026-03-22,112.27,paid\nO000999,U00449,2026-01-06,88.61,paid\nO001968,U00880,2026-04-21,68.0,test\nO001174,U00527,2026-04-04,27.36,paid\nO000186,U00087,2026-03-23,85.17,paid\nO001978,U00884,2026-04-27,154.0,test\nO001407,U00634,2026-04-25,147.45,paid\nO001620,U00723,2026-04-05,37.27,paid\nO002509,U01129,2026-02-19,65.62,test\n---\n2699 orders.csv\n\nstderr:\n"
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    "turn": 1,
    "tool": "list_files",
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     "path": "."
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    "result": "./TASK.md\n./orders.csv"
   },
   {
    "turn": 2,
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        "reasoning_content": "Let me inspect the data more: date formats, duplicates, statuses, user_id formats.",
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           "arguments": "{\"command\": \"cd .; python3 - <<'EOF'\\nimport csv, collections, re\\nrows=list(csv.DictReader(open('orders.csv')))\\nprint(len(rows))\\nprint(rows[0].keys())\\n# date formats\\nfmt=collections.Counter()\\nfor r in rows:\\n    d=r['order_date']\\n    if re.match(r'^\\\\d{4}-\\\\d{2}-\\\\d{2}$',d): fmt['iso']+=1\\n    elif re.match(r'^\\\\d{2}/\\\\d{2}/\\\\d{4}$',d): fmt['dmy']+=1\\n    else: fmt['other:'+d]+=1\\nprint(fmt)\\nprint(collections.Counter(r['status'] for r in rows))\\n# duplicates exact\\nc=collections.Counter(tuple(r.values()) for r in rows)\\nprint('exact dup rows extra:', sum(v-1 for v in c.values() if v>1))\\n# user id raw forms\\nraw=collections.Counter(r['user_id'] for r in rows)\\nweird=[k for k in raw if k!=k.strip() or k!=k.upper()]\\nprint('weird ids count', len(weird), weird[:10])\\n# amount formats\\nbad=[r['amount_usd'] for r in rows if not re.match(r'^\\\\d+(\\\\.\\\\d+)?$', r['amount_usd'])]\\nprint('bad amounts', bad[:10], len(bad))\\nEOF\"}"
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      "strip_thinking_response": false,
      "disable_thinking": false,
      "enable_e2ee": true,
      "enable_web_search": "off",
      "enable_web_scraping": false,
      "enable_web_citations": false,
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    "turn": 2,
    "tool": "run_shell",
    "args": {
     "command": "cd .; python3 - <<'EOF'\nimport csv, collections, re\nrows=list(csv.DictReader(open('orders.csv')))\nprint(len(rows))\nprint(rows[0].keys())\n# date formats\nfmt=collections.Counter()\nfor r in rows:\n    d=r['order_date']\n    if re.match(r'^\\d{4}-\\d{2}-\\d{2}$',d): fmt['iso']+=1\n    elif re.match(r'^\\d{2}/\\d{2}/\\d{4}$',d): fmt['dmy']+=1\n    else: fmt['other:'+d]+=1\nprint(fmt)\nprint(collections.Counter(r['status'] for r in rows))\n# duplicates exact\nc=collections.Counter(tuple(r.values()) for r in rows)\nprint('exact dup rows extra:', sum(v-1 for v in c.values() if v>1))\n# user id raw forms\nraw=collections.Counter(r['user_id'] for r in rows)\nweird=[k for k in raw if k!=k.strip() or k!=k.upper()]\nprint('weird ids count', len(weird), weird[:10])\n# amount formats\nbad=[r['amount_usd'] for r in rows if not re.match(r'^\\d+(\\.\\d+)?$', r['amount_usd'])]\nprint('bad amounts', bad[:10], len(bad))\nEOF"
    },
    "result": "exit=0\nstdout:\n2698\ndict_keys(['order_id', 'user_id', 'order_date', 'amount_usd', 'status'])\nCounter({'iso': 2565, 'dmy': 133})\nCounter({'paid': 2490, 'refunded': 126, 'test': 82})\nexact dup rows extra: 42\nweird ids count 72 [' u00220 ', ' u00543 ', ' u00406 ', ' u00099 ', ' u00685 ', ' u00302 ', ' u00703 ', ' u00045 ', ' u00564 ', ' u00504 ']\nbad amounts [] 0\n\nstderr:\n"
   },
   {
    "turn": 3,
    "response": {
     "id": "chatcmpl-789ee8da609e4f788522c60534207dd4",
     "object": "chat.completion",
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     "model": "deepseek-v4-1-flash",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Let me check for duplicate order_ids, and whether duplicates are exact. Also check date ambiguity: DD/MM vs MM/DD. Let's check the dmy rows \u2014 day >12 to confirm DD/MM.\n\nAlso need to consider: dedupe exact duplicates. Then normalize user_id (strip, upper). Then filter paid. Then cohort.\n\nLet me check order_id duplicates and whether same order_id appears with different data.",
        "tool_calls": [
         {
          "index": 0,
          "id": "chatcmpl-tool-b6fda5bb958cf2c4",
          "type": "function",
          "function": {
           "name": "run_shell",
           "arguments": "{\"command\": \"cd .; python3 - <<'EOF'\\nimport csv, collections, re\\nrows=list(csv.DictReader(open('orders.csv')))\\n# order_id duplicates\\nc=collections.Counter(r['order_id'] for r in rows)\\ndups={k:v for k,v in c.items() if v>1}\\nprint('dup order_ids', len(dups), list(dups.items())[:5])\\n# check if dup order_ids have differing rows\\nbyid=collections.defaultdict(list)\\nfor r in rows: byid[r['order_id']].append(tuple(r.values()))\\ndiff=[k for k,v in byid.items() if len(set(v))>1]\\nprint('order_ids with differing rows', len(diff), diff[:5])\\n# dmy day values\\ndays=[int(r['order_date'][:2]) for r in rows if re.match(r'^\\\\d{2}/\\\\d{2}/\\\\d{4}$',r['order_date'])]\\nprint('dmy day max', max(days), 'count>12', sum(1 for d in days if d>12))\\n# months in dmy\\nmons=collections.Counter(r['order_date'][3:5] for r in rows if re.match(r'^\\\\d{2}/\\\\d{2}/\\\\d{4}$',r['order_date']))\\nprint(mons)\\n# iso months\\nprint(collections.Counter(r['order_date'][:7] for r in rows if re.match(r'^\\\\d{4}-\\\\d{2}-\\\\d{2}$',r['order_date'])))\\nEOF\"}"
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      "disable_thinking": false,
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   {
    "turn": 3,
    "tool": "run_shell",
    "args": {
     "command": "cd .; python3 - <<'EOF'\nimport csv, collections, re\nrows=list(csv.DictReader(open('orders.csv')))\n# order_id duplicates\nc=collections.Counter(r['order_id'] for r in rows)\ndups={k:v for k,v in c.items() if v>1}\nprint('dup order_ids', len(dups), list(dups.items())[:5])\n# check if dup order_ids have differing rows\nbyid=collections.defaultdict(list)\nfor r in rows: byid[r['order_id']].append(tuple(r.values()))\ndiff=[k for k,v in byid.items() if len(set(v))>1]\nprint('order_ids with differing rows', len(diff), diff[:5])\n# dmy day values\ndays=[int(r['order_date'][:2]) for r in rows if re.match(r'^\\d{2}/\\d{2}/\\d{4}$',r['order_date'])]\nprint('dmy day max', max(days), 'count>12', sum(1 for d in days if d>12))\n# months in dmy\nmons=collections.Counter(r['order_date'][3:5] for r in rows if re.match(r'^\\d{2}/\\d{2}/\\d{4}$',r['order_date']))\nprint(mons)\n# iso months\nprint(collections.Counter(r['order_date'][:7] for r in rows if re.match(r'^\\d{4}-\\d{2}-\\d{2}$',r['order_date'])))\nEOF"
    },
    "result": "exit=0\nstdout:\ndup order_ids 42 [('O000718', 2), ('O001363', 2), ('O002398', 2), ('O002218', 2), ('O000538', 2)]\norder_ids with differing rows 0 []\ndmy day max 29 count>12 71\nCounter({'05': 36, '04': 30, '03': 20, '06': 19, '02': 17, '01': 11})\nCounter({'2026-05': 578, '2026-04': 534, '2026-03': 504, '2026-06': 410, '2026-02': 355, '2026-01': 184})\n\nstderr:\n"
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   {
    "turn": 4,
    "response": {
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       "index": 0,
       "message": {
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        "reasoning_content": "Duplicates are exact duplicate rows (same order_id, same everything). Dedup by full row.\n\nNow build the analysis. Note: dedup should be applied before everything. Then filter paid. Normalize user_id (strip, upper). Parse dates (DD/MM/YYYY -> YYYY-MM-DD).\n\nCohort = month of first paid order. retention[k] = share of cohort users with >=1 paid order in cohort month + k, k=0..through 2026-06.\n\nRevenue per calendar month from paid dedup orders.\n\nLet me write the script.",
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          "index": 0,
          "id": "chatcmpl-tool-b046933f2df30f61",
          "type": "function",
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           "arguments": "{\"command\": \"cd .; python3 - <<'EOF'\\nimport csv, collections, re, json\\nrows=list(csv.DictReader(open('orders.csv')))\\n# dedup exact rows\\nseen=set(); ded=[]\\nfor r in rows:\\n    t=tuple(r.values())\\n    if t in seen: continue\\n    seen.add(t); ded.append(r)\\nprint('after dedup', len(ded))\\n\\ndef parse_date(d):\\n    if re.match(r'^\\\\d{4}-\\\\d{2}-\\\\d{2}$',d): return d\\n    m=re.match(r'^(\\\\d{2})/(\\\\d{2})/(\\\\d{4})$',d)\\n    return f\\\"{m.group(3)}-{m.group(2)}-{m.group(1)}\\\"\\n\\npaid=[]\\nfor r in ded:\\n    if r['status']!='paid': continue\\n    uid=r['user_id'].strip().upper()\\n    date=parse_date(r['order_date'])\\n    paid.append((uid,date,float(r['amount_usd'])))\\nprint('paid rows', len(paid))\\n\\nmonths=[f\\\"2026-{m:02d}\\\" for m in range(1,7)]\\nmidx={m:i for i,m in enumerate(months)}\\n\\n# revenue by month\\nrev=collections.defaultdict(float)\\nfor uid,date,amt in paid:\\n    rev[date[:7]]+=amt\\npaid_revenue_by_month={m: round(rev.get(m,0.0),2) for m in months}\\n\\n# first paid month per user\\nfirst={}\\nfor uid,date,amt in paid:\\n    m=date[:7]\\n    if uid not in first or m<first[uid]: first[uid]=m\\n\\ncohort_users=collections.defaultdict(set)\\nfor uid,m in first.items(): cohort_users[m].add(uid)\\n\\n# user active months\\nactive=collections.defaultdict(set)\\nfor uid,date,amt in paid: active[uid].add(date[:7])\\n\\ncohorts={}\\nfor m in months:\\n    users=cohort_users.get(m,set())\\n    size=len(users)\\n    ret=[]\\n    for k in range(0, len(months)-midx[m]):\\n        cm=months[midx[m]+k]\\n        cnt=sum(1 for u in users if cm in active[u])\\n        ret.append(round(cnt/size,4) if size else 0.0)\\n    cohorts[m]={\\\"size\\\":size,\\\"retention\\\":ret}\\n\\nout={\\\"cohorts\\\":cohorts,\\\"paid_revenue_by_month\\\":paid_revenue_by_month}\\njson.dump(out, open('answer.json','w'), indent=2)\\nprint(json.dumps(out, indent=2))\\nEOF\"}"
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   {
    "turn": 4,
    "tool": "run_shell",
    "args": {
     "command": "cd .; python3 - <<'EOF'\nimport csv, collections, re, json\nrows=list(csv.DictReader(open('orders.csv')))\n# dedup exact rows\nseen=set(); ded=[]\nfor r in rows:\n    t=tuple(r.values())\n    if t in seen: continue\n    seen.add(t); ded.append(r)\nprint('after dedup', len(ded))\n\ndef parse_date(d):\n    if re.match(r'^\\d{4}-\\d{2}-\\d{2}$',d): return d\n    m=re.match(r'^(\\d{2})/(\\d{2})/(\\d{4})$',d)\n    return f\"{m.group(3)}-{m.group(2)}-{m.group(1)}\"\n\npaid=[]\nfor r in ded:\n    if r['status']!='paid': continue\n    uid=r['user_id'].strip().upper()\n    date=parse_date(r['order_date'])\n    paid.append((uid,date,float(r['amount_usd'])))\nprint('paid rows', len(paid))\n\nmonths=[f\"2026-{m:02d}\" for m in range(1,7)]\nmidx={m:i for i,m in enumerate(months)}\n\n# revenue by month\nrev=collections.defaultdict(float)\nfor uid,date,amt in paid:\n    rev[date[:7]]+=amt\npaid_revenue_by_month={m: round(rev.get(m,0.0),2) for m in months}\n\n# first paid month per user\nfirst={}\nfor uid,date,amt in paid:\n    m=date[:7]\n    if uid not in first or m<first[uid]: first[uid]=m\n\ncohort_users=collections.defaultdict(set)\nfor uid,m in first.items(): cohort_users[m].add(uid)\n\n# user active months\nactive=collections.defaultdict(set)\nfor uid,date,amt in paid: active[uid].add(date[:7])\n\ncohorts={}\nfor m in months:\n    users=cohort_users.get(m,set())\n    size=len(users)\n    ret=[]\n    for k in range(0, len(months)-midx[m]):\n        cm=months[midx[m]+k]\n        cnt=sum(1 for u in users if cm in active[u])\n        ret.append(round(cnt/size,4) if size else 0.0)\n    cohorts[m]={\"size\":size,\"retention\":ret}\n\nout={\"cohorts\":cohorts,\"paid_revenue_by_month\":paid_revenue_by_month}\njson.dump(out, open('answer.json','w'), indent=2)\nprint(json.dumps(out, indent=2))\nEOF"
    },
    "result": "exit=0\nstdout:\nafter dedup 2656\npaid rows 2450\n{\n  \"cohorts\": {\n    \"2026-01\": {\n      \"size\": 148,\n      \"retention\": [\n        1.0,\n        0.3311,\n        0.2703,\n        0.1486,\n        0.1284,\n        0.0405\n      ]\n    },\n    \"2026-02\": {\n      \"size\": 216,\n      \"retention\": [\n        1.0,\n        0.3889,\n        0.2731,\n        0.1157,\n        0.0648\n      ]\n    },\n    \"2026-03\": {\n      \"size\": 228,\n      \"retention\": [\n        1.0,\n        0.4123,\n        0.2544,\n        0.1228\n      ]\n    },\n    \"2026-04\": {\n      \"size\": 217,\n      \"retention\": [\n        1.0,\n        0.3733,\n        0.2212\n      ]\n    },\n    \"2026-05\": {\n      \"size\": 246,\n      \"retention\": [\n        1.0,\n        0.3415\n      ]\n    },\n    \"2026-06\": {\n      \"size\": 116,\n      \"retention\": [\n        1.0\n      ]\n    }\n  },\n  \"paid_revenue_by_month\": {\n    \"2026-01\": 16862.2,\n    \"2026-02\": 29862.41,\n    \"2026-03\": 43820.16,\n    \"2026-04\": 47451.77,\n    \"2026-05\": 51556.03,\n    \"2026-06\": 36742.33\n  }\n}\n\nstderr:\n"
   },
   {
    "turn": 5,
    "response": {
     "id": "chatcmpl-46b2cde3508c439680df5f27964586a7",
     "object": "chat.completion",
     "created": 1792457876,
     "model": "deepseek-v4-1-flash",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Let me verify a few things: the DD/MM/YYYY parsing \u2014 day max 29, so no ambiguity issue (all days \u2264 29, but could be >12 which confirms DD/MM). Good.\n\nLet me double-check the dedup: exact duplicates removed. Also check whether there are duplicate order_ids that are exact duplicates \u2014 yes 42 dup order_ids all identical rows, consistent.\n\nLet me sanity check retention k=0 always 1.0 \u2014 yes since first paid order is in cohort month.\n\nVerify answer.json written.",
        "tool_calls": [
         {
          "index": 0,
          "id": "chatcmpl-tool-bbdff046498e2e6b",
          "type": "function",
          "function": {
           "name": "run_shell",
           "arguments": "{\"command\": \"cd .; cat answer.json | python3 -c \\\"import json,sys; d=json.load(sys.stdin); print('ok', list(d['cohorts']), list(d['paid_revenue_by_month']))\\\"\"}"
          },
          "name": null
         }
        ],
        "tools": null
       },
       "finish_reason": "tool_calls"
      }
     ],
     "usage": {
      "prompt_tokens": 3478,
      "completion_tokens": 195,
      "total_tokens": 3673,
      "prompt_tokens_details": {
       "cached_tokens": 2326
      },
      "cache_read_input_tokens": 2326,
      "completion_tokens_details": {
       "reasoning_tokens": 110
      }
     },
     "cost": {
      "usd": 0,
      "diem": 0.000597045
     },
     "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 .; cat answer.json | python3 -c \"import json,sys; d=json.load(sys.stdin); print('ok', list(d['cohorts']), list(d['paid_revenue_by_month']))\""
    },
    "result": "exit=0\nstdout:\nok ['2026-01', '2026-02', '2026-03', '2026-04', '2026-05', '2026-06'] ['2026-01', '2026-02', '2026-03', '2026-04', '2026-05', '2026-06']\n\nstderr:\n"
   },
   {
    "turn": 6,
    "response": {
     "id": "chatcmpl-11a861028f8b4a5cb56655754c71f0fa",
     "object": "chat.completion",
     "created": 1792457878,
     "model": "deepseek-v4-1-flash",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Done. `answer.json` is written.\n\n**Cleaning steps applied:**\n- Parsed both `YYYY-MM-DD` (2565 rows) and `DD/MM/YYYY` (133 rows) date formats.\n- Normalized `user_id` with `strip().upper()` (72 IDs had stray whitespace/lowercase).\n- Removed 42 exact duplicate rows (all duplicate `order_id`s were identical rows).\n- Kept only `status == \"paid\"` (dropped 126 refunded, 82 test).\n\n**Results:**\n- Cohorts by first paid month: Jan 148, Feb 216, Mar 228, Apr 217, May 246, Jun 116 users, with retention arrays from k=0 through 2026-06 (rounded to 4 dp).\n- Paid revenue by month: Jan 16862.20, Feb 29862.41, Mar 43820.16, Apr 47451.77, May 51556.03, Jun 36742.33.",
        "tools": null
       },
       "finish_reason": "stop"
      }
     ],
     "usage": {
      "prompt_tokens": 3657,
      "completion_tokens": 219,
      "total_tokens": 3876,
      "prompt_tokens_details": {
       "cached_tokens": 3348
      },
      "cache_read_input_tokens": 3348,
      "completion_tokens_details": {
       "reasoning_tokens": 0
      }
     },
     "cost": {
      "usd": 0,
      "diem": 0.00038061000000000003
     },
     "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
     }
    }
   }
  ]
 }
]