Gemma 4 31B Instruct (Venice) on Analyze five months of AI coding-agent session logs

{"turn": 1, "response": {"service_tier": "default", "id": "chatcmpl-RI7u7zELCEHDb48VFVw6O9cT", "object": "chat.completion", "created": 1792016341, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": "The objective is to process a file named `sessions.csv` and extract specific metrics:\n1.  `total_sessions`: Total number of sessions.\n2.  `total_prs`: Total number of PRs opened across all sessions.\n3.  `total_api_equiv_usd`: Total API equivalent cost in USD.\n4.  `busiest_month_by_sessions`: The month (YYYY-MM) with the most sessions.\n5.  `median_minutes`: Median duration of sessions in minutes.\n6.  `usd_per_pr_by_tool`: A mapping of tool name to (total api_equiv_usd / total prs_opened) for that tool.\n\nThe results should be written to `answers.json` and a summary report to `REPORT.md`.\n\nFirst, I need to explore the `sessions.csv` file to understand its structure.", "name": null, "tool_calls": [{"id": "chatcmpl-tool-9843f6258e64f9f6", "type": "function", "function": {"name": "list_files", "arguments": "{\"path\": \".\"}"}}]}, "finish_reason": "tool_calls", "logprobs": null}], "usage": {"prompt_tokens": 394, "completion_tokens": 213, "total_tokens": 607, "prompt_tokens_details": {"cached_tokens": 0}}, "cost": {"usd": 0, "diem": 0.00012396}, "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": "./sessions.csv"}
{"turn": 2, "response": {"service_tier": "default", "id": "chatcmpl-RYh8JJovnzfERb8gKl6USh5z", "object": "chat.completion", "created": 1792016343, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": "I need to analyze `sessions.csv` to extract the required metrics.\nFirst, I'll read the file to understand its structure.", "name": null, "tool_calls": [{"id": "chatcmpl-tool-94b7e4abfb007455", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"sessions.csv\"}"}}]}, "finish_reason": "tool_calls", "logprobs": null}], "usage": {"prompt_tokens": 425, "completion_tokens": 50, "total_tokens": 475, "prompt_tokens_details": {"cached_tokens": 384}, "cache_read_input_tokens": 384}, "cost": {"usd": 0, "diem": 5.748e-05}, "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": "read_file", "args": {"path": "sessions.csv"}, "result": "session_id,month,tool,minutes,prs_opened,api_equiv_usd\ns0000,2026-09,codex,93,2,23.56\ns0001,2026-09,claude-code,121,0,16.24\ns0002,2026-06,claude-code,97,1,21.72\ns0003,2026-08,claude-code,148,0,0.38\ns0004,2026-06,codex,73,0,22.91\ns0005,2026-08,claude-code,20,0,15.47\ns0006,2026-08,claude-code,35,0,24.31\ns0007,2026-05,claude-code,57,0,21.82\ns0008,2026-07,codex,52,1,21.91\ns0009,2026-06,claude-code,178,0,24.17\ns0010,2026-08,codex,7,0,10.4\ns0011,2026-06,codex,18,0,7.57\ns0012,2026-09,claude-code,154,2,17.71\ns0013,2026-05,codex,92,0,12.04\ns0014,2026-07,claude-code,125,1,17.63\ns0015,2026-05,codex,7,2,8.97\ns0016,2026-08,claude-code,142,1,9.19\ns0017,2026-09,claude-code,117,0,17.71\ns0018,2026-09,claude-code,32,0,23.24\ns0019,2026-08,codex,133,0,22.32\ns0020,2026-07,codex,29,1,18.72\ns0021,2026-07,codex,11,1,23.65\ns0022,2026-05,claude-code,89,1,15.29\ns0023,2026-06,codex,72,2,13.65\ns0024,2026-07,codex,80,0,19.98\ns0025,2026-06,codex,125,0,18.02\ns0026,2026-05,codex,10,0,18.53\ns0027,2026-07,codex,118,2,10.57\ns0028,2026-05,claude-code,128,0,20.97\ns0029,2026-06,claude-code,163,1,2.71\ns0030,2026-08,codex,40,0,21.13\ns0031,2026-07,claude-code,118,1,21.31\ns0032,2026-09,codex,126,2,18.25\ns0033,2026-08,codex,76,1,10.12\ns0034,2026-06,claude-code,98,1,24.0\ns0035,2026-08,codex,48,0,12.32\ns0036,2026-09,codex,18,0,17.38\ns0037,2026-05,codex,95,1,17.63\ns0038,2026-07,codex,69,2,17.91\ns0039,2026-07,claude-code,150,0,11.88\ns0040,2026-07,codex,173,0,11.6\ns0041,2026-09,codex,91,0,16.12\ns0042,2026-08,codex,46,2,11.27\ns0043,2026-07,codex,134,0,13.26\ns0044,2026-06,claude-code,94,1,7.11\ns0045,2026-05,codex,45,1,19.52\ns0046,2026-09,codex,79,1,13.85\ns0047,2026-07,claude-code,52,0,14.68\ns0048,2026-09,claude-code,58,2,4.55\ns0049,2026-05,codex,59,0,1.4\ns0050,2026-06,claude-code,83,0,12.09\ns0051,2026-09,claude-code,108,1,8.81\ns0052,2026-09,claude-code,153,0,5.98\ns0053,2026-07,claude-code,91,1,23.81\ns0054,2026-08,claude-code,178,1,9.38\ns0055,2026-05,codex,26,0,13.65\ns0056,2026-07,codex,92,0,10.51\ns0057,2026-09,codex,121,0,3.96\ns0058,2026-08,codex,52,0,15.23\ns0059,2026-07,claude-code,99,0,8.18\ns0060,2026-09,claude-code,85,2,21.92\ns0061,2026-09,codex,111,1,24.42\ns0062,2026-08,codex,124,2,9.53\ns0063,2026-06,codex,79,1,6.31\ns0064,2026-05,codex,80,1,23.24\ns0065,2026-06,codex,8,0,16.52\ns0066,2026-08,claude-code,77,0,19.55\ns0067,2026-08,claude-code,124,1,14.03\ns0068,2026-06,codex,11,0,12.28\ns0069,2026-06,codex,77,1,15.22\ns0070,2026-08,claude-code,6,0,7.55\ns0071,2026-09,codex,159,0,18.3\ns0072,2026-05,codex,91,0,17.06\ns0073,2026-09,claude-code,11,0,6.35\ns0074,2026-08,claude-code,177,1,4.82\ns0075,2026-08,codex,154,1,17.24\ns0076,2026-08,codex,123,1,17.07\ns0077,2026-06,codex,121,0,7.63\ns0078,2026-05,codex,151,0,16.23\ns0079,2026-08,codex,38,0,11.99\ns0080,2026-09,codex,86,1,3.88\ns0081,2026-09,claude-code,19,2,5.79\ns0082,2026-05,claude-code,171,0,8.36\ns0083,2026-08,claude-code,105,2,12.19\ns0084,2026-05,claude-code,58,1,16.16\ns0085,2026-05,claude-code,76,2,22.95\ns0086,2026-06,claude-code,158,0,10.38\ns0087,2026-0"}
{"turn": 3, "response": {"service_tier": "default", "id": "chatcmpl-R5skuVlOOGwUWbN6IQ3Bt33F", "object": "chat.completion", "created": 1792016365, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": "I need to process the `sessions.csv` file to calculate several metrics:\n1. `total_sessions`: Total number of sessions.\n2. `total_prs`: Total number of PRs opened across all sessions.\n3. `total_api_equiv_usd`: Sum of `api_equiv_usd` for all sessions.\n4. `busiest_month_by_sessions`: The month with the highest number of sessions.\n5. `median_minutes`: The median of the `minutes` column.\n6. `usd_per_pr_by_tool`: For each tool, calculate (total `api_equiv_usd` / total `prs_opened`).\n\nI will write a Python script to perform these calculations and output the results to `answers.json` and `REPORT.md`.\n\nPlan:\n1. Read `sessions.csv`.\n2. Calculate the required metrics.\n3. Write `answers.json`.\n4. Write `REPORT.md` with a 5-bullet summary.", "name": null, "tool_calls": [{"id": "chatcmpl-tool-8ca0d5ebbaf3141d", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"import csv\\nimport json\\nimport statistics\\nfrom collections import defaultdict\\n\\ndef analyze_sessions():\\n    sessions = []\\n    with open('sessions.csv', 'r') as f:\\n        reader = csv.DictReader(f)\\n        for row in reader:\\n            sessions.append({\\n                'month': row['month'],\\n                'tool': row['tool'],\\n                'minutes': int(row['minutes']),\\n                'prs_opened': int(row['prs_opened']),\\n                'api_equiv_usd': float(row['api_equiv_usd'])\\n            })\\n\\n    total_sessions = len(sessions)\\n    total_prs = sum(s['prs_opened'] for s in sessions)\\n    total_api_equiv_usd = sum(s['api_equiv_usd'] for s in sessions)\\n    \\n    month_counts = defaultdict(int)\\n    for s in sessions:\\n        month_counts[s['month']] += 1\\n    busiest_month = max(month_counts, key=month_counts.get)\\n    \\n    minutes = [s['minutes'] for s in sessions]\\n    median_minutes = statistics.median(minutes)\\n    \\n    tool_stats = defaultdict(lambda: {'usd': 0.0, 'prs': 0})\\n    for s in sessions:\\n        tool_stats[s['tool']]['usd'] += s['api_equiv_usd']\\n        tool_stats[s['tool']]['prs'] += s['prs_opened']\\n    \\n    usd_per_pr_by_tool = {}\\n    for tool, stats in tool_stats.items():\\n        if stats['prs'] > 0:\\n            usd_per_pr_by_tool[tool] = round(stats['usd'] / stats['prs'], 2)\\n        else:\\n            usd_per_pr_by_tool[tool] = 0.0 # Or handle as needed, but usually 0 if no PRs\\n\\n    answers = {\\n        \\\"total_sessions\\\": total_sessions,\\n        \\\"total_prs\\\": total_prs,\\n        \\\"total_api_equiv_usd\\\": round(total_api_equiv_usd, 2),\\n        \\\"busiest_month_by_sessions\\\": busiest_month,\\n        \\\"median_minutes\\\": median_minutes,\\n        \\\"usd_per_pr_by_tool\\\": usd_per_pr_by_tool\\n    }\\n    \\n    with open('answers.json', 'w') as f:\\n        json.dump(answers, f, indent=2)\\n    \\n    # Generate REPORT.md\\n    report_content = f\\\"\\\"\\\"# AI Coding Agent Session Report\\n\\n- Total sessions analyzed: {total_sessions}\\n- Total PRs opened: {total_prs}\\n- Total API equivalent cost: ${total_api_equiv_usd:.2f}\\n- Busiest month: {busiest_month}\\n- Median session duration: {median_minutes} minutes\\n\\\"\\\"\\\"\\n    with open('REPORT.md', 'w') as f:\\n        f.write(report_content)\\n\\nif __name__ == \\\"__main__\\\":\\n    analyze_sessions()\\n\", \"path\": \"analyze.py\"}"}}]}, "finish_reason": "tool_calls", "logprobs": null}], "usage": {"prompt_tokens": 12027, "completion_tokens": 960, "total_tokens": 12987, "prompt_tokens_details": {"cached_tokens": 416}, "cache_read_input_tokens": 416}, "cost": {"usd": 0, "diem": 0.0017763599999999998}, "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": "write_file", "args": {"content": "import csv\nimport json\nimport statistics\nfrom collections import defaultdict\n\ndef analyze_sessions():\n    sessions = []\n    with open('sessions.csv', 'r') as f:\n        reader = csv.DictReader(f)\n        for row in reader:\n            sessions.append({\n                'month': row['month'],\n                'tool': row['tool'],\n                'minutes': int(row['minutes']),\n                'prs_opened': int(row['prs_opened']),\n                'api_equiv_usd': float(row['api_equiv_usd'])\n            })\n\n    total_sessions = len(sessions)\n    total_prs = sum(s['prs_opened'] for s in sessions)\n    total_api_equiv_usd = sum(s['api_equiv_usd'] for s in sessions)\n    \n    month_counts = defaultdict(int)\n    for s in sessions:\n        month_counts[s['month']] += 1\n    busiest_month = max(month_counts, key=month_counts.get)\n    \n    minutes = [s['minutes'] for s in sessions]\n    median_minutes = statistics.median(minutes)\n    \n    tool_stats = defaultdict(lambda: {'usd': 0.0, 'prs': 0})\n    for s in sessions:\n        tool_stats[s['tool']]['usd'] += s['api_equiv_usd']\n        tool_stats[s['tool']]['prs'] += s['prs_opened']\n    \n    usd_per_pr_by_tool = {}\n    for tool, stats in tool_stats.items():\n        if stats['prs'] > 0:\n            usd_per_pr_by_tool[tool] = round(stats['usd'] / stats['prs'], 2)\n        else:\n            usd_per_pr_by_tool[tool] = 0.0 # Or handle as needed, but usually 0 if no PRs\n\n    answers = {\n        \"total_sessions\": total_sessions,\n        \"total_prs\": total_prs,\n        \"total_api_equiv_usd\": round(total_api_equiv_usd, 2),\n        \"busiest_month_by_sessions\": busiest_month,\n        \"median_minutes\": median_minutes,\n        \"usd_per_pr_by_tool\": usd_per_pr_by_tool\n    }\n    \n    with open('answers.json', 'w') as f:\n        json.dump(answers, f, indent=2)\n    \n    # Generate REPORT.md\n    report_content = f\"\"\"# AI Coding Agent Session Report\n\n- Total sessions analyzed: {total_sessions}\n- Total PRs opened: {total_prs}\n- Total API equivalent cost: ${total_api_equiv_usd:.2f}\n- Busiest month: {busiest_month}\n- Median session duration: {median_minutes} minutes\n\"\"\"\n    with open('REPORT.md', 'w') as f:\n        f.write(report_content)\n\nif __name__ == \"__main__\":\n    analyze_sessions()\n", "path": "analyze.py"}, "result": "wrote 2237 chars"}
{"turn": 4, "response": {"service_tier": "default", "id": "chatcmpl-RanPJ8sHIVjYilu27LICvHDp", "object": "chat.completion", "created": 1792016366, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": null, "name": null, "tool_calls": [{"id": "chatcmpl-tool-9a2d59864945f44d", "type": "function", "function": {"name": "run_shell", "arguments": "{\"command\": \"python3 analyze.py\"}"}}]}, "finish_reason": "tool_calls", "logprobs": null}], "usage": {"prompt_tokens": 12782, "completion_tokens": 19, "total_tokens": 12801, "prompt_tokens_details": {"cached_tokens": 12000}, "cache_read_input_tokens": 12000}, "cost": {"usd": 0, "diem": 0.00118068}, "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": "python3 analyze.py"}, "result": "exit=0\nstdout:\n\nstderr:\n"}
{"turn": 5, "response": {"service_tier": "default", "id": "chatcmpl-RJWEmL3KvO66JirKDvXhm3Ho", "object": "chat.completion", "created": 1792016367, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": null, "name": null, "tool_calls": [{"id": "chatcmpl-tool-a5a755b88f46a98b", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"answers.json\"}"}}]}, "finish_reason": "tool_calls", "logprobs": null}], "usage": {"prompt_tokens": 12823, "completion_tokens": 17, "total_tokens": 12840, "prompt_tokens_details": {"cached_tokens": 12800}, "cache_read_input_tokens": 12800}, "cost": {"usd": 0, "diem": 0.00116088}, "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": "read_file", "args": {"path": "answers.json"}, "result": "{\n  \"total_sessions\": 400,\n  \"total_prs\": 256,\n  \"total_api_equiv_usd\": 4843.16,\n  \"busiest_month_by_sessions\": \"2026-09\",\n  \"median_minutes\": 88.5,\n  \"usd_per_pr_by_tool\": {\n    \"codex\": 18.39,\n    \"claude-code\": 19.5\n  }\n}"}
{"turn": 6, "response": {"service_tier": "default", "id": "chatcmpl-RGlbIgcP29jLTawPTSyybV35", "object": "chat.completion", "created": 1792016378, "model": "google-gemma-4-31b-it", "choices": [{"index": 0, "message": {"role": "assistant", "content": "The analysis of `sessions.csv` has been completed. The results are stored in `answers.json` and a summary report is provided in `REPORT.md`.\n\n**Summary of results:**\n- Total Sessions: 400\n- Total PRs: 256\n- Total API Equivalent Cost: $4,843.16\n- Busiest Month: 2026-09\n- Median Session Duration: 88.5 minutes\n- Cost per PR: Codex ($18.39), Claude-Code ($19.50)", "reasoning_content": "The user wants a 5-bullet summary in REPORT.md. My current `analyze.py` generates a 5-bullet summary, but I should make sure it's a good summary.\n\nWait, the `REPORT.md` I generated is:\n- Total sessions analyzed: 400\n- Total PRs opened: 256\n- Total API equivalent cost: $4843.16\n- Busiest month: 2026-09\n- Median session duration: 88.5 minutes\n\nThis is exactly 5 bullets. I'll double check if there's anything else.\nThe prompt says \"exactly these keys\" for `answers.json` and \"a 5-bullet summary\" for `REPORT.md`.\n\nLet's review the `answers.json` content:\n- `total_sessions`: 400 (int) - OK\n- `total_prs`: 256 (int) - OK\n- `total_api_equiv_usd`: 4843.16 (number rounded to 2 decimals) - OK\n- `busiest_month_by_sessions`: \"2026-09\" (string) - OK\n- `median_minutes`: 88.5 (number) - OK\n- `usd_per_pr_by_tool`: {\"codex\": 18.39, \"claude-code\": 19.5} (object tool -> total api_equiv_usd / total prs_opened, rounded to 2 decimals) - OK\n\nThe `REPORT.md` has 5 bullets.\n\nI'm confident.", "name": null, "tool_calls": null}, "finish_reason": "stop", "logprobs": null}], "usage": {"prompt_tokens": 12977, "completion_tokens": 495, "total_tokens": 13472, "prompt_tokens_details": {"cached_tokens": 12800}, "cache_read_input_tokens": 12800}, "cost": {"usd": 0, "diem": 0.00135144}, "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}}}