Qwen3 Coder 480B Turbo (Venice) (thinking on) on Rebalance to target weights with whole shares and highest-cost-first tax lots

[
 {
  "segment_dir": "./usecase-runner/runs/j12-qwen3-coder-480b-a35b-instruct-turbo/venice_output_venice",
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   "model": "qwen3-coder-480b-a35b-instruct-turbo",
   "start": "2026-10-26T20:31:26-0400",
   "num_turns": 8,
   "tool_calls": 7,
   "usage": {
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   "cost_usd_venice_list": 0.00841,
   "billed_reported_by_venice": 0.00841,
   "wall_seconds": 65.0,
   "final_message": "The script is working correctly. Let me review the implementation against the requirements:\n\n1. \u2705 Uses `decimal.Decimal` for money calculations\n2. \u2705 Rounds to cents using ROUND_HALF_UP when writing output\n3. \u2705 Calculates total value correctly\n4. \u2705 Processes sells first, then buys, in alphabetical order\n5. \u2705 Uses highest cost-per-share first for selling (with tie-breaking)\n6. \u2705 Correctly calculates gains and determines short/long term status\n7. \u2705 Calculates realized gains and estimated tax properly\n8. \u2705 Outputs all required fields in the correct format\n\nThe solution handles:\n- Computing current and target positions\n- Selling excess shares with highest-cost-first lot selection\n- Buying needed shares with available cash\n- Tracking tax implications of sales\n- Calculating final weights after all trades\n\nLet me summarize what the solution does:\n\n1. First, it reads the input portfolio data including cash, prices, target weights, tax lots, and tax rates\n2. It calculates the total portfolio value (cash + current holdings value)\n3. For each symbol, it determines the target number of shares based on the target weight\n4. It processes sells first in alphabetical order:\n   - Sells shares to reach target levels using highest cost-per-share lots first\n   - Records these sales with their tax implications\n5. Then it processes buys in alphabetical order:\n   - Purchases shares up to target levels using available cash (including sale proceeds)\n6. It tracks realized gains (short-term and long-term) and calculates estimated tax\n7. Finally, it outputs all the required information in the specified JSON format\n\nThe implementation correctly follows all the specified rules and produces the expected output format.",
   "error": null,
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  "trace": [
   {
    "turn": 1,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RNJKsIkY3VlrFckAS82Sk2JY",
     "object": "chat.completion",
     "created": 1793061087,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "I'll create a Python script to solve this rebalancing problem according to the specified rules.\n\nFirst, let me analyze what we need to do:\n1. Calculate current portfolio value\n2. Determine target shares for each symbol\n3. Execute sells first (highest cost basis first)\n4. Execute buys with available cash\n5. Track tax implications of sales\n6. Generate the required output JSON\n\nLet me check the input file structure first:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_dfece33dfd1e8fac",
          "type": "function",
          "function": {
           "name": "read_file",
           "arguments": "{\"path\": \"input.json\"}"
          }
         }
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     "cost": {
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      "diem": 0.00061495
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     "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": "read_file",
    "args": {
     "path": "input.json"
    },
    "result": "{\n \"as_of\": \"2026-10-19\",\n \"cash\": 13493.04,\n \"prices\": {\n  \"AAA\": 308.78,\n  \"EMK\": 130.09,\n  \"GLD\": 238.03,\n  \"TIPS\": 354.24\n },\n \"targets\": {\n  \"AAA\": 0.42,\n  \"EMK\": 0.118,\n  \"GLD\": 0.233,\n  \"TIPS\": 0.229\n },\n \"lots\": [\n  {\n   \"id\": \"L001\",\n   \"symbol\": \"AAA\",\n   \"shares\": 198,\n   \"cost_per_share\": 441.15,\n   \"acquired\": \"2025-10-18\"\n  },\n  {\n   \"id\": \"L002\",\n   \"symbol\": \"AAA\",\n   \"shares\": 42,\n   \"cost_per_share\": 305.68,\n   \"acquired\": \"2023-07-28\"\n  },\n  {\n   \"id\": \"L003\",\n   \"symbol\": \"EMK\",\n   \"shares\": 164,\n   \"cost_per_share\": 143.97,\n   \"acquired\": \"2025-10-19\"\n  },\n  {\n   \"id\": \"L004\",\n   \"symbol\": \"GLD\",\n   \"shares\": 239,\n   \"cost_per_share\": 199.04,\n   \"acquired\": \"2025-10-18\"\n  },\n  {\n   \"id\": \"L005\",\n   \"symbol\": \"GLD\",\n   \"shares\": 277,\n   \"cost_per_share\": 203.06,\n   \"acquired\": \"2025-10-19\"\n  },\n  {\n   \"id\": \"L006\",\n   \"symbol\": \"GLD\",\n   \"shares\": 152,\n   \"cost_per_share\": 199.58,\n   \"acquired\": \"2025-10-19\"\n  },\n  {\n   \"id\": \"L007\",\n   \"symbol\": \"AAA\",\n   \"shares\": 198,\n   \"cost_per_share\": 441.15,\n   \"acquired\": \"2026-07-11\"\n  }\n ],\n \"short_term_rate\": 0.32,\n \"long_term_rate\": 0.15,\n \"note\": \"Synthetic portfolio and flat illustrative tax rates given for this exercise.\"\n}"
   },
   {
    "turn": 2,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RRh0Lx0PhwO3OaYAaLUbcMKv",
     "object": "chat.completion",
     "created": 1793061103,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Now I'll create the solution script `solve.py`:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_57e297a9d81f2ee7",
          "type": "function",
          "function": {
           "name": "write_file",
           "arguments": "{\"path\": \"solve.py\", \"content\": \"import json\\nimport sys\\nfrom decimal import Decimal, ROUND_HALF_UP\\nfrom datetime import datetime\\n\\n\\ndef decimal_round(value):\\n    return Decimal(str(value)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n\\n\\ndef calculate_days_difference(date1_str, date2_str):\\n    date1 = datetime.strptime(date1_str, '%Y-%m-%d')\\n    date2 = datetime.strptime(date2_str, '%Y-%m-%d')\\n    return (date2 - date1).days\\n\\n\\ndef main(input_file, output_file):\\n    # Read input data\\n    with open(input_file, 'r') as f:\\n        data = json.load(f)\\n\\n    as_of = data['as_of']\\n    cash = Decimal(str(data['cash']))\\n    prices = {k: Decimal(str(v)) for k, v in data['prices'].items()}\\n    targets = {k: Decimal(str(v)) for k, v in data['targets'].items()}\\n    lots = data['lots']\\n    short_term_rate = Decimal(str(data['short_term_rate']))\\n    long_term_rate = Decimal(str(data['long_term_rate']))\\n\\n    # Calculate current holdings\\n    holdings = {}\\n    for lot in lots:\\n        symbol = lot['symbol']\\n        if symbol not in holdings:\\n            holdings[symbol] = 0\\n        holdings[symbol] += lot['shares']\\n\\n    # Convert holdings to Decimal\\n    holdings = {k: Decimal(str(v)) for k, v in holdings.items()}\\n\\n    # Calculate total value\\n    current_value = sum(holdings.get(symbol, 0) * prices[symbol] for symbol in prices)\\n    total_value = cash + current_value\\n\\n    # Calculate target values and shares\\n    target_values = {symbol: total_value * targets.get(symbol, Decimal('0')) for symbol in prices}\\n    target_shares = {symbol: (target_values[symbol] / prices[symbol]).quantize(Decimal('1'), rounding='floor') \\n                     for symbol in prices}\\n\\n    # Prepare lots for processing - add numeric fields\\n    processed_lots = []\\n    for lot in lots:\\n        processed_lot = lot.copy()\\n        processed_lot['shares'] = Decimal(str(lot['shares']))\\n        processed_lot['cost_per_share'] = Decimal(str(lot['cost_per_share']))\\n        processed_lots.append(processed_lot)\\n\\n    # Sort symbols alphabetically\\n    symbols = sorted(prices.keys())\\n\\n    # Process trades\\n    trades = []\\n    lot_sales = []\\n    realized_short_term = Decimal('0')\\n    realized_long_term = Decimal('0')\\n    available_cash = cash\\n\\n    # Sell phase\\n    for symbol in symbols:\\n        current_shares = holdings.get(symbol, Decimal('0'))\\n        target_share_count = target_shares.get(symbol, Decimal('0'))\\n        \\n        if current_shares > target_share_count:\\n            # Need to sell\\n            sell_shares = current_shares - target_share_count\\n            \\n            if sell_shares >= 1:\\n                # Record the trade\\n                trades.append({\\n                    'symbol': symbol,\\n                    'side': 'sell',\\n                    'shares': int(sell_shares)\\n                })\\n                \\n                # Update holdings\\n                holdings[symbol] = current_shares - sell_shares\\n                \\n                # Find lots to sell (highest cost_per_share first)\\n                symbol_lots = [lot for lot in processed_lots if lot['symbol'] == symbol]\\n                # Sort by cost_per_share (descending), acquired (ascending), id (ascending)\\n                symbol_lots.sort(key=lambda x: (-x['cost_per_share'], x['acquired'], x['id']))\\n                \\n                shares_to_sell = sell_shares\\n                for lot in symbol_lots:\\n                    if shares_to_sell <= 0:\\n                        break\\n                        \\n                    # Calculate how many shares from this lot to sell\\n                    lot_shares = min(shares_to_sell, lot['shares'])\\n                    \\n                    if lot_shares > 0:\\n                        # Calculate gain\\n                        gain = (prices[symbol] - lot['cost_per_share']) * lot_shares\\n                        \\n                        # Determine term\\n                        days_held = calculate_days_difference(lot['acquired'], as_of)\\n                        term = 'long' if days_held > 365 else 'short'\\n                        \\n                        # Add to lot sales\\n                        lot_sales.append({\\n                            'lot_id': lot['id'],\\n                            'shares': float(lot_shares),\\n                            'gain': float(gain),\\n                            'term': term\\n                        })\\n                        \\n                        # Update tax calculations\\n                        if term == 'short':\\n                            realized_short_term += gain\\n                        else:\\n                            realized_long_term += gain\\n                        \\n                        # Reduce lot shares\\n                        lot['shares'] -= lot_shares\\n                        shares_to_sell -= lot_shares\\n                \\n                # Add sale proceeds to available cash\\n                sale_proceeds = sell_shares * prices[symbol]\\n                available_cash += sale_proceeds\\n\\n    # Buy phase\\n    for symbol in symbols:\\n        current_shares = holdings.get(symbol, Decimal('0'))\\n        target_share_count = target_shares.get(symbol, Decimal('0'))\\n        \\n        if target_share_count > current_shares:\\n            # Need to buy\\n            buy_shares = target_share_count - current_shares\\n            \\n            if buy_shares >= 1:\\n                # Check if we have enough cash\\n                max_affordable_shares = (available_cash / prices[symbol]).quantize(Decimal('1'), rounding='floor')\\n                actual_buy_shares = min(buy_shares, max_affordable_shares)\\n                \\n                if actual_buy_shares >= 1:\\n                    # Record the trade\\n                    trades.append({\\n                        'symbol': symbol,\\n                        'side': 'buy',\\n                        'shares': int(actual_buy_shares)\\n                    })\\n                    \\n                    # Update holdings\\n                    holdings[symbol] = current_shares + actual_buy_shares\\n                    \\n                    # Deduct purchase cost from available cash\\n                    purchase_cost = actual_buy_shares * prices[symbol]\\n                    available_cash -= purchase_cost\\n\\n    # Calculate estimated tax\\n    short_term_tax = max(realized_short_term, Decimal('0')) * short_term_rate\\n    long_term_tax = max(realized_long_term, Decimal('0')) * long_term_rate\\n    estimated_tax = short_term_tax + long_term_tax\\n\\n    # Calculate final weights\\n    final_total_value = available_cash + sum(holdings[symbol] * prices[symbol] for symbol in prices)\\n    weights_after = {}\\n    for symbol in prices:\\n        if holdings.get(symbol, Decimal('0')) > 0 or symbol in targets:\\n            weight = (holdings.get(symbol, Decimal('0')) * prices[symbol]) / final_total_value\\n            weights_after[symbol] = float(weight.quantize(Decimal('0.000001')))  # 6 decimal places\\n\\n    # Format output\\n    result = {\\n        'total_value': float(decimal_round(total_value)),\\n        'trades': trades,\\n        'lot_sales': lot_sales,\\n        'realized_short_term': float(decimal_round(realized_short_term)),\\n        'realized_long_term': float(decimal_round(realized_long_term)),\\n        'estimated_tax': float(decimal_round(estimated_tax)),\\n        'cash_after': float(decimal_round(available_cash)),\\n        'weights_after': weights_after\\n    }\\n\\n    # Write output\\n    with open(output_file, 'w') as f:\\n        json.dump(result, f, indent=2)\\n\\n\\nif __name__ == '__main__':\\n    if len(sys.argv) != 3:\\n        print(\\\"Usage: python solve.py input.json output.json\\\")\\n        sys.exit(1)\\n    \\n    main(sys.argv[1], sys.argv[2])\"}"
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     "cost": {
      "usd": 0,
      "diem": 0.00315625
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     "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\nfrom decimal import Decimal, ROUND_HALF_UP\nfrom datetime import datetime\n\n\ndef decimal_round(value):\n    return Decimal(str(value)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n\n\ndef calculate_days_difference(date1_str, date2_str):\n    date1 = datetime.strptime(date1_str, '%Y-%m-%d')\n    date2 = datetime.strptime(date2_str, '%Y-%m-%d')\n    return (date2 - date1).days\n\n\ndef main(input_file, output_file):\n    # Read input data\n    with open(input_file, 'r') as f:\n        data = json.load(f)\n\n    as_of = data['as_of']\n    cash = Decimal(str(data['cash']))\n    prices = {k: Decimal(str(v)) for k, v in data['prices'].items()}\n    targets = {k: Decimal(str(v)) for k, v in data['targets'].items()}\n    lots = data['lots']\n    short_term_rate = Decimal(str(data['short_term_rate']))\n    long_term_rate = Decimal(str(data['long_term_rate']))\n\n    # Calculate current holdings\n    holdings = {}\n    for lot in lots:\n        symbol = lot['symbol']\n        if symbol not in holdings:\n            holdings[symbol] = 0\n        holdings[symbol] += lot['shares']\n\n    # Convert holdings to Decimal\n    holdings = {k: Decimal(str(v)) for k, v in holdings.items()}\n\n    # Calculate total value\n    current_value = sum(holdings.get(symbol, 0) * prices[symbol] for symbol in prices)\n    total_value = cash + current_value\n\n    # Calculate target values and shares\n    target_values = {symbol: total_value * targets.get(symbol, Decimal('0')) for symbol in prices}\n    target_shares = {symbol: (target_values[symbol] / prices[symbol]).quantize(Decimal('1'), rounding='floor') \n                     for symbol in prices}\n\n    # Prepare lots for processing - add numeric fields\n    processed_lots = []\n    for lot in lots:\n        processed_lot = lot.copy()\n        processed_lot['shares'] = Decimal(str(lot['shares']))\n        processed_lot['cost_per_share'] = Decimal(str(lot['cost_per_share']))\n        processed_lots.append(processed_lot)\n\n    # Sort symbols alphabetically\n    symbols = sorted(prices.keys())\n\n    # Process trades\n    trades = []\n    lot_sales = []\n    realized_short_term = Decimal('0')\n    realized_long_term = Decimal('0')\n    available_cash = cash\n\n    # Sell phase\n    for symbol in symbols:\n        current_shares = holdings.get(symbol, Decimal('0'))\n        target_share_count = target_shares.get(symbol, Decimal('0'))\n        \n        if current_shares > target_share_count:\n            # Need to sell\n            sell_shares = current_shares - target_share_count\n            \n            if sell_shares >= 1:\n                # Record the trade\n                trades.append({\n                    'symbol': symbol,\n                    'side': 'sell',\n                    'shares': int(sell_shares)\n                })\n                \n                # Update holdings\n                holdings[symbol] = current_shares - sell_shares\n                \n                # Find lots to sell (highest cost_per_share first)\n                symbol_lots = [lot for lot in processed_lots if lot['symbol'] == symbol]\n                # Sort by cost_per_share (descending), acquired (ascending), id (ascending)\n                symbol_lots.sort(key=lambda x: (-x['cost_per_share'], x['acquired'], x['id']))\n                \n                shares_to_sell = sell_shares\n                for lot in symbol_lots:\n                    if shares_to_sell <= 0:\n                        break\n                        \n                    # Calculate how many shares from this lot to sell\n                    lot_shares = min(shares_to_sell, lot['shares'])\n                    \n                    if lot_shares > 0:\n                        # Calculate gain\n                        gain = (prices[symbol] - lot['cost_per_share']) * lot_shares\n                        \n                        # Determine term\n                        days_held = calculate_days_difference(lot['acquired'], as_of)\n                        term = 'long' if days_held > 365 else 'short'\n                        \n                        # Add to lot sales\n                        lot_sales.append({\n                            'lot_id': lot['id'],\n                            'shares': float(lot_shares),\n                            'gain': float(gain),\n                            'term': term\n                        })\n                        \n                        # Update tax calculations\n                        if term == 'short':\n                            realized_short_term += gain\n                        else:\n                            realized_long_term += gain\n                        \n                        # Reduce lot shares\n                        lot['shares'] -= lot_shares\n                        shares_to_sell -= lot_shares\n                \n                # Add sale proceeds to available cash\n                sale_proceeds = sell_shares * prices[symbol]\n                available_cash += sale_proceeds\n\n    # Buy phase\n    for symbol in symbols:\n        current_shares = holdings.get(symbol, Decimal('0'))\n        target_share_count = target_shares.get(symbol, Decimal('0'))\n        \n        if target_share_count > current_shares:\n            # Need to buy\n            buy_shares = target_share_count - current_shares\n            \n            if buy_shares >= 1:\n                # Check if we have enough cash\n                max_affordable_shares = (available_cash / prices[symbol]).quantize(Decimal('1'), rounding='floor')\n                actual_buy_shares = min(buy_shares, max_affordable_shares)\n                \n                if actual_buy_shares >= 1:\n                    # Record the trade\n                    trades.append({\n                        'symbol': symbol,\n                        'side': 'buy',\n                        'shares': int(actual_buy_shares)\n                    })\n                    \n                    # Update holdings\n                    holdings[symbol] = current_shares + actual_buy_shares\n                    \n                    # Deduct purchase cost from available cash\n                    purchase_cost = actual_buy_shares * prices[symbol]\n                    available_cash -= purchase_cost\n\n    # Calculate estimated tax\n    short_term_tax = max(realized_short_term, Decimal('0')) * short_term_rate\n    long_term_tax = max(realized_long_term, Decimal('0')) * long_term_rate\n    estimated_tax = short_term_tax + long_term_tax\n\n    # Calculate final weights\n    final_total_value = available_cash + sum(holdings[symbol] * prices[symbol] for symbol in prices)\n    weights_after = {}\n    for symbol in prices:\n        if holdings.get(symbol, Decimal('0')) > 0 or symbol in targets:\n            weight = (holdings.get(symbol, Decimal('0')) * prices[symbol]) / final_total_value\n            weights_after[symbol] = float(weight.quantize(Decimal('0.000001')))  # 6 decimal places\n\n    # Format output\n    result = {\n        'total_value': float(decimal_round(total_value)),\n        'trades': trades,\n        'lot_sales': lot_sales,\n        'realized_short_term': float(decimal_round(realized_short_term)),\n        'realized_long_term': float(decimal_round(realized_long_term)),\n        'estimated_tax': float(decimal_round(estimated_tax)),\n        'cash_after': float(decimal_round(available_cash)),\n        'weights_after': weights_after\n    }\n\n    # Write output\n    with open(output_file, 'w') as f:\n        json.dump(result, f, indent=2)\n\n\nif __name__ == '__main__':\n    if len(sys.argv) != 3:\n        print(\"Usage: python solve.py input.json output.json\")\n        sys.exit(1)\n    \n    main(sys.argv[1], sys.argv[2])"
    },
    "result": "wrote 7587 chars"
   },
   {
    "turn": 3,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-Rws29BpyQnj2okrwMPoum7uK",
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     "created": 1793061126,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Let me test the script with the provided input:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_216e39f7206e5f64",
          "type": "function",
          "function": {
           "name": "run_shell",
           "arguments": "{\"command\": \"python solve.py input.json answer.json\"}"
          }
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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,
      "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": "python solve.py input.json answer.json"
    },
    "result": "exit=127\nstdout:\n\nstderr:\nbash: line 1: python: command not found\n"
   },
   {
    "turn": 4,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RBgVT5V5LWZ1ywXyFKTqCH6i",
     "object": "chat.completion",
     "created": 1793061128,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Let me try using python3:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_fa299fa042d0e216",
          "type": "function",
          "function": {
           "name": "run_shell",
           "arguments": "{\"command\": \"python3 solve.py input.json answer.json\"}"
          }
         }
        ]
       },
       "finish_reason": "tool_calls",
       "logprobs": null
      }
     ],
     "usage": {
      "prompt_tokens": 3745,
      "completion_tokens": 37,
      "total_tokens": 3782,
      "prompt_tokens_details": {
       "cached_tokens": 3680
      },
      "cache_read_input_tokens": 3680
     },
     "cost": {
      "usd": 0,
      "diem": 0.00022545
     },
     "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 solve.py input.json answer.json"
    },
    "result": "exit=1\nstdout:\n\nstderr:\nTraceback (most recent call last):\n  File \"./usecase-runner/runs/j12-qwen3-coder-480b-a35b-instruct-turbo/work/solve.py\", line 195, in <module>\n    main(sys.argv[1], sys.argv[2])\n    ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"./usecase-runner/runs/j12-qwen3-coder-480b-a35b-instruct-turbo/work/solve.py\", line 47, in main\n    target_shares = {symbol: (target_values[symbol] / prices[symbol]).quantize(Decimal('1'), rounding='floor')\n                             ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: valid values for rounding are:\n  [ROUND_CEILING, ROUND_FLOOR, ROUND_UP, ROUND_DOWN,\n   ROUND_HALF_UP, ROUND_HALF_DOWN, ROUND_HALF_EVEN,\n   ROUND_05UP]\n"
   },
   {
    "turn": 5,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RWbTH6FHLipphEg94EA6dWcq",
     "object": "chat.completion",
     "created": 1793061130,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "I need to fix the rounding issue. Let me update the script:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_a006a0cc1e706d6a",
          "type": "function",
          "function": {
           "name": "write_file",
           "arguments": "{\"path\": \"solve.py\", \"content\": \"import json\\nimport sys\\nfrom decimal import Decimal, ROUND_HALF_UP, ROUND_FLOOR\\nfrom datetime import datetime\\n\\n\\ndef decimal_round(value):\\n    return Decimal(str(value)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n\\n\\ndef calculate_days_difference(date1_str, date2_str):\\n    date1 = datetime.strptime(date1_str, '%Y-%m-%d')\\n    date2 = datetime.strptime(date2_str, '%Y-%m-%d')\\n    return (date2 - date1).days\\n\\n\\ndef main(input_file, output_file):\\n    # Read input data\\n    with open(input_file, 'r') as f:\\n        data = json.load(f)\\n\\n    as_of = data['as_of']\\n    cash = Decimal(str(data['cash']))\\n    prices = {k: Decimal(str(v)) for k, v in data['prices'].items()}\\n    targets = {k: Decimal(str(v)) for k, v in data['targets'].items()}\\n    lots = data['lots']\\n    short_term_rate = Decimal(str(data['short_term_rate']))\\n    long_term_rate = Decimal(str(data['long_term_rate']))\\n\\n    # Calculate current holdings\\n    holdings = {}\\n    for lot in lots:\\n        symbol = lot['symbol']\\n        if symbol not in holdings:\\n            holdings[symbol] = 0\\n        holdings[symbol] += lot['shares']\\n\\n    # Convert holdings to Decimal\\n    holdings = {k: Decimal(str(v)) for k, v in holdings.items()}\\n\\n    # Calculate total value\\n    current_value = sum(holdings.get(symbol, 0) * prices[symbol] for symbol in prices)\\n    total_value = cash + current_value\\n\\n    # Calculate target values and shares\\n    target_values = {symbol: total_value * targets.get(symbol, Decimal('0')) for symbol in prices}\\n    target_shares = {symbol: (target_values[symbol] / prices[symbol]).quantize(Decimal('1'), rounding=ROUND_FLOOR) \\n                     for symbol in prices}\\n\\n    # Prepare lots for processing - add numeric fields\\n    processed_lots = []\\n    for lot in lots:\\n        processed_lot = lot.copy()\\n        processed_lot['shares'] = Decimal(str(lot['shares']))\\n        processed_lot['cost_per_share'] = Decimal(str(lot['cost_per_share']))\\n        processed_lots.append(processed_lot)\\n\\n    # Sort symbols alphabetically\\n    symbols = sorted(prices.keys())\\n\\n    # Process trades\\n    trades = []\\n    lot_sales = []\\n    realized_short_term = Decimal('0')\\n    realized_long_term = Decimal('0')\\n    available_cash = cash\\n\\n    # Sell phase\\n    for symbol in symbols:\\n        current_shares = holdings.get(symbol, Decimal('0'))\\n        target_share_count = target_shares.get(symbol, Decimal('0'))\\n        \\n        if current_shares > target_share_count:\\n            # Need to sell\\n            sell_shares = current_shares - target_share_count\\n            \\n            if sell_shares >= 1:\\n                # Record the trade\\n                trades.append({\\n                    'symbol': symbol,\\n                    'side': 'sell',\\n                    'shares': int(sell_shares)\\n                })\\n                \\n                # Update holdings\\n                holdings[symbol] = current_shares - sell_shares\\n                \\n                # Find lots to sell (highest cost_per_share first)\\n                symbol_lots = [lot for lot in processed_lots if lot['symbol'] == symbol]\\n                # Sort by cost_per_share (descending), acquired (ascending), id (ascending)\\n                symbol_lots.sort(key=lambda x: (-float(x['cost_per_share']), x['acquired'], x['id']))\\n                \\n                shares_to_sell = sell_shares\\n                for lot in symbol_lots:\\n                    if shares_to_sell <= 0:\\n                        break\\n                        \\n                    # Calculate how many shares from this lot to sell\\n                    lot_shares = min(shares_to_sell, lot['shares'])\\n                    \\n                    if lot_shares > 0:\\n                        # Calculate gain\\n                        gain = (prices[symbol] - lot['cost_per_share']) * lot_shares\\n                        \\n                        # Determine term\\n                        days_held = calculate_days_difference(lot['acquired'], as_of)\\n                        term = 'long' if days_held > 365 else 'short'\\n                        \\n                        # Add to lot sales\\n                        lot_sales.append({\\n                            'lot_id': lot['id'],\\n                            'shares': float(lot_shares),\\n                            'gain': float(gain),\\n                            'term': term\\n                        })\\n                        \\n                        # Update tax calculations\\n                        if term == 'short':\\n                            realized_short_term += gain\\n                        else:\\n                            realized_long_term += gain\\n                        \\n                        # Reduce lot shares\\n                        lot['shares'] -= lot_shares\\n                        shares_to_sell -= lot_shares\\n                \\n                # Add sale proceeds to available cash\\n                sale_proceeds = sell_shares * prices[symbol]\\n                available_cash += sale_proceeds\\n\\n    # Buy phase\\n    for symbol in symbols:\\n        current_shares = holdings.get(symbol, Decimal('0'))\\n        target_share_count = target_shares.get(symbol, Decimal('0'))\\n        \\n        if target_share_count > current_shares:\\n            # Need to buy\\n            buy_shares = target_share_count - current_shares\\n            \\n            if buy_shares >= 1:\\n                # Check if we have enough cash\\n                max_affordable_shares = (available_cash / prices[symbol]).quantize(Decimal('1'), rounding=ROUND_FLOOR)\\n                actual_buy_shares = min(buy_shares, max_affordable_shares)\\n                \\n                if actual_buy_shares >= 1:\\n                    # Record the trade\\n                    trades.append({\\n                        'symbol': symbol,\\n                        'side': 'buy',\\n                        'shares': int(actual_buy_shares)\\n                    })\\n                    \\n                    # Update holdings\\n                    holdings[symbol] = current_shares + actual_buy_shares\\n                    \\n                    # Deduct purchase cost from available cash\\n                    purchase_cost = actual_buy_shares * prices[symbol]\\n                    available_cash -= purchase_cost\\n\\n    # Calculate estimated tax\\n    short_term_tax = max(realized_short_term, Decimal('0')) * short_term_rate\\n    long_term_tax = max(realized_long_term, Decimal('0')) * long_term_rate\\n    estimated_tax = short_term_tax + long_term_tax\\n\\n    # Calculate final weights\\n    final_total_value = available_cash + sum(holdings[symbol] * prices[symbol] for symbol in prices)\\n    weights_after = {}\\n    for symbol in prices:\\n        if holdings.get(symbol, Decimal('0')) > 0 or symbol in targets:\\n            weight = (holdings.get(symbol, Decimal('0')) * prices[symbol]) / final_total_value\\n            weights_after[symbol] = float(weight.quantize(Decimal('0.000001')))  # 6 decimal places\\n\\n    # Format output\\n    result = {\\n        'total_value': float(decimal_round(total_value)),\\n        'trades': trades,\\n        'lot_sales': lot_sales,\\n        'realized_short_term': float(decimal_round(realized_short_term)),\\n        'realized_long_term': float(decimal_round(realized_long_term)),\\n        'estimated_tax': float(decimal_round(estimated_tax)),\\n        'cash_after': float(decimal_round(available_cash)),\\n        'weights_after': weights_after\\n    }\\n\\n    # Write output\\n    with open(output_file, 'w') as f:\\n        json.dump(result, f, indent=2)\\n\\n\\nif __name__ == '__main__':\\n    if len(sys.argv) != 3:\\n        print(\\\"Usage: python solve.py input.json output.json\\\")\\n        sys.exit(1)\\n    \\n    main(sys.argv[1], sys.argv[2])\"}"
          }
         }
        ]
       },
       "finish_reason": "tool_calls",
       "logprobs": null
      }
     ],
     "usage": {
      "prompt_tokens": 4013,
      "completion_tokens": 1645,
      "total_tokens": 5658,
      "prompt_tokens_details": {
       "cached_tokens": 3760
      },
      "cache_read_input_tokens": 3760
     },
     "cost": {
      "usd": 0,
      "diem": 0.0027064500000000004
     },
     "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": "write_file",
    "args": {
     "path": "solve.py",
     "content": "import json\nimport sys\nfrom decimal import Decimal, ROUND_HALF_UP, ROUND_FLOOR\nfrom datetime import datetime\n\n\ndef decimal_round(value):\n    return Decimal(str(value)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n\n\ndef calculate_days_difference(date1_str, date2_str):\n    date1 = datetime.strptime(date1_str, '%Y-%m-%d')\n    date2 = datetime.strptime(date2_str, '%Y-%m-%d')\n    return (date2 - date1).days\n\n\ndef main(input_file, output_file):\n    # Read input data\n    with open(input_file, 'r') as f:\n        data = json.load(f)\n\n    as_of = data['as_of']\n    cash = Decimal(str(data['cash']))\n    prices = {k: Decimal(str(v)) for k, v in data['prices'].items()}\n    targets = {k: Decimal(str(v)) for k, v in data['targets'].items()}\n    lots = data['lots']\n    short_term_rate = Decimal(str(data['short_term_rate']))\n    long_term_rate = Decimal(str(data['long_term_rate']))\n\n    # Calculate current holdings\n    holdings = {}\n    for lot in lots:\n        symbol = lot['symbol']\n        if symbol not in holdings:\n            holdings[symbol] = 0\n        holdings[symbol] += lot['shares']\n\n    # Convert holdings to Decimal\n    holdings = {k: Decimal(str(v)) for k, v in holdings.items()}\n\n    # Calculate total value\n    current_value = sum(holdings.get(symbol, 0) * prices[symbol] for symbol in prices)\n    total_value = cash + current_value\n\n    # Calculate target values and shares\n    target_values = {symbol: total_value * targets.get(symbol, Decimal('0')) for symbol in prices}\n    target_shares = {symbol: (target_values[symbol] / prices[symbol]).quantize(Decimal('1'), rounding=ROUND_FLOOR) \n                     for symbol in prices}\n\n    # Prepare lots for processing - add numeric fields\n    processed_lots = []\n    for lot in lots:\n        processed_lot = lot.copy()\n        processed_lot['shares'] = Decimal(str(lot['shares']))\n        processed_lot['cost_per_share'] = Decimal(str(lot['cost_per_share']))\n        processed_lots.append(processed_lot)\n\n    # Sort symbols alphabetically\n    symbols = sorted(prices.keys())\n\n    # Process trades\n    trades = []\n    lot_sales = []\n    realized_short_term = Decimal('0')\n    realized_long_term = Decimal('0')\n    available_cash = cash\n\n    # Sell phase\n    for symbol in symbols:\n        current_shares = holdings.get(symbol, Decimal('0'))\n        target_share_count = target_shares.get(symbol, Decimal('0'))\n        \n        if current_shares > target_share_count:\n            # Need to sell\n            sell_shares = current_shares - target_share_count\n            \n            if sell_shares >= 1:\n                # Record the trade\n                trades.append({\n                    'symbol': symbol,\n                    'side': 'sell',\n                    'shares': int(sell_shares)\n                })\n                \n                # Update holdings\n                holdings[symbol] = current_shares - sell_shares\n                \n                # Find lots to sell (highest cost_per_share first)\n                symbol_lots = [lot for lot in processed_lots if lot['symbol'] == symbol]\n                # Sort by cost_per_share (descending), acquired (ascending), id (ascending)\n                symbol_lots.sort(key=lambda x: (-float(x['cost_per_share']), x['acquired'], x['id']))\n                \n                shares_to_sell = sell_shares\n                for lot in symbol_lots:\n                    if shares_to_sell <= 0:\n                        break\n                        \n                    # Calculate how many shares from this lot to sell\n                    lot_shares = min(shares_to_sell, lot['shares'])\n                    \n                    if lot_shares > 0:\n                        # Calculate gain\n                        gain = (prices[symbol] - lot['cost_per_share']) * lot_shares\n                        \n                        # Determine term\n                        days_held = calculate_days_difference(lot['acquired'], as_of)\n                        term = 'long' if days_held > 365 else 'short'\n                        \n                        # Add to lot sales\n                        lot_sales.append({\n                            'lot_id': lot['id'],\n                            'shares': float(lot_shares),\n                            'gain': float(gain),\n                            'term': term\n                        })\n                        \n                        # Update tax calculations\n                        if term == 'short':\n                            realized_short_term += gain\n                        else:\n                            realized_long_term += gain\n                        \n                        # Reduce lot shares\n                        lot['shares'] -= lot_shares\n                        shares_to_sell -= lot_shares\n                \n                # Add sale proceeds to available cash\n                sale_proceeds = sell_shares * prices[symbol]\n                available_cash += sale_proceeds\n\n    # Buy phase\n    for symbol in symbols:\n        current_shares = holdings.get(symbol, Decimal('0'))\n        target_share_count = target_shares.get(symbol, Decimal('0'))\n        \n        if target_share_count > current_shares:\n            # Need to buy\n            buy_shares = target_share_count - current_shares\n            \n            if buy_shares >= 1:\n                # Check if we have enough cash\n                max_affordable_shares = (available_cash / prices[symbol]).quantize(Decimal('1'), rounding=ROUND_FLOOR)\n                actual_buy_shares = min(buy_shares, max_affordable_shares)\n                \n                if actual_buy_shares >= 1:\n                    # Record the trade\n                    trades.append({\n                        'symbol': symbol,\n                        'side': 'buy',\n                        'shares': int(actual_buy_shares)\n                    })\n                    \n                    # Update holdings\n                    holdings[symbol] = current_shares + actual_buy_shares\n                    \n                    # Deduct purchase cost from available cash\n                    purchase_cost = actual_buy_shares * prices[symbol]\n                    available_cash -= purchase_cost\n\n    # Calculate estimated tax\n    short_term_tax = max(realized_short_term, Decimal('0')) * short_term_rate\n    long_term_tax = max(realized_long_term, Decimal('0')) * long_term_rate\n    estimated_tax = short_term_tax + long_term_tax\n\n    # Calculate final weights\n    final_total_value = available_cash + sum(holdings[symbol] * prices[symbol] for symbol in prices)\n    weights_after = {}\n    for symbol in prices:\n        if holdings.get(symbol, Decimal('0')) > 0 or symbol in targets:\n            weight = (holdings.get(symbol, Decimal('0')) * prices[symbol]) / final_total_value\n            weights_after[symbol] = float(weight.quantize(Decimal('0.000001')))  # 6 decimal places\n\n    # Format output\n    result = {\n        'total_value': float(decimal_round(total_value)),\n        'trades': trades,\n        'lot_sales': lot_sales,\n        'realized_short_term': float(decimal_round(realized_short_term)),\n        'realized_long_term': float(decimal_round(realized_long_term)),\n        'estimated_tax': float(decimal_round(estimated_tax)),\n        'cash_after': float(decimal_round(available_cash)),\n        'weights_after': weights_after\n    }\n\n    # Write output\n    with open(output_file, 'w') as f:\n        json.dump(result, f, indent=2)\n\n\nif __name__ == '__main__':\n    if len(sys.argv) != 3:\n        print(\"Usage: python solve.py input.json output.json\")\n        sys.exit(1)\n    \n    main(sys.argv[1], sys.argv[2])"
    },
    "result": "wrote 7615 chars"
   },
   {
    "turn": 6,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RlitxkTOlW7n89aGnEKpbxxY",
     "object": "chat.completion",
     "created": 1793061146,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Let me try running it again:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_d5dc0dc0c356c978",
          "type": "function",
          "function": {
           "name": "run_shell",
           "arguments": "{\"command\": \"python3 solve.py input.json answer.json\"}"
          }
         }
        ]
       },
       "finish_reason": "tool_calls",
       "logprobs": null
      }
     ],
     "usage": {
      "prompt_tokens": 5680,
      "completion_tokens": 37,
      "total_tokens": 5717,
      "prompt_tokens_details": {
       "cached_tokens": 5632
      },
      "cache_read_input_tokens": 5632
     },
     "cost": {
      "usd": 0,
      "diem": 0.00029758
     },
     "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": 6,
    "tool": "run_shell",
    "args": {
     "command": "python3 solve.py input.json answer.json"
    },
    "result": "exit=0\nstdout:\n\nstderr:\n"
   },
   {
    "turn": 7,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-Rj1oI5Odoh4dQbrijWjQZJnZ",
     "object": "chat.completion",
     "created": 1793061147,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "Great! The script ran successfully. Let me check the output:\n\n",
        "reasoning_content": null,
        "name": null,
        "tool_calls": [
         {
          "id": "call_68b69587aba05afe",
          "type": "function",
          "function": {
           "name": "read_file",
           "arguments": "{\"path\": \"answer.json\"}"
          }
         }
        ]
       },
       "finish_reason": "tool_calls",
       "logprobs": null
      }
     ],
     "usage": {
      "prompt_tokens": 5738,
      "completion_tokens": 35,
      "total_tokens": 5773,
      "prompt_tokens_details": {
       "cached_tokens": 5696
      },
      "cache_read_input_tokens": 5696
     },
     "cost": {
      "usd": 0,
      "diem": 0.00029504
     },
     "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": 7,
    "tool": "read_file",
    "args": {
     "path": "answer.json"
    },
    "result": "{\n  \"total_value\": 329077.48,\n  \"trades\": [\n    {\n      \"symbol\": \"GLD\",\n      \"side\": \"sell\",\n      \"shares\": 346\n    },\n    {\n      \"symbol\": \"AAA\",\n      \"side\": \"buy\",\n      \"shares\": 9\n    },\n    {\n      \"symbol\": \"EMK\",\n      \"side\": \"buy\",\n      \"shares\": 134\n    },\n    {\n      \"symbol\": \"TIPS\",\n      \"side\": \"buy\",\n      \"shares\": 212\n    }\n  ],\n  \"lot_sales\": [\n    {\n      \"lot_id\": \"L005\",\n      \"shares\": 277.0,\n      \"gain\": 9686.69,\n      \"term\": \"short\"\n    },\n    {\n      \"lot_id\": \"L006\",\n      \"shares\": 69.0,\n      \"gain\": 2653.05,\n      \"term\": \"short\"\n    }\n  ],\n  \"realized_short_term\": 12339.74,\n  \"realized_long_term\": 0.0,\n  \"estimated_tax\": 3948.72,\n  \"cash_after\": 541.46,\n  \"weights_after\": {\n    \"AAA\": 0.419429,\n    \"EMK\": 0.117805,\n    \"GLD\": 0.232911,\n    \"TIPS\": 0.22821\n  }\n}"
   },
   {
    "turn": 8,
    "response": {
     "service_tier": "default",
     "id": "chatcmpl-RRJ9vYuPgldNpd1892DlXyDc",
     "object": "chat.completion",
     "created": 1793061148,
     "model": "qwen3-coder-480b-a35b-instruct-turbo",
     "choices": [
      {
       "index": 0,
       "message": {
        "role": "assistant",
        "content": "The script is working correctly. Let me review the implementation against the requirements:\n\n1. \u2705 Uses `decimal.Decimal` for money calculations\n2. \u2705 Rounds to cents using ROUND_HALF_UP when writing output\n3. \u2705 Calculates total value correctly\n4. \u2705 Processes sells first, then buys, in alphabetical order\n5. \u2705 Uses highest cost-per-share first for selling (with tie-breaking)\n6. \u2705 Correctly calculates gains and determines short/long term status\n7. \u2705 Calculates realized gains and estimated tax properly\n8. \u2705 Outputs all required fields in the correct format\n\nThe solution handles:\n- Computing current and target positions\n- Selling excess shares with highest-cost-first lot selection\n- Buying needed shares with available cash\n- Tracking tax implications of sales\n- Calculating final weights after all trades\n\nLet me summarize what the solution does:\n\n1. First, it reads the input portfolio data including cash, prices, target weights, tax lots, and tax rates\n2. It calculates the total portfolio value (cash + current holdings value)\n3. For each symbol, it determines the target number of shares based on the target weight\n4. It processes sells first in alphabetical order:\n   - Sells shares to reach target levels using highest cost-per-share lots first\n   - Records these sales with their tax implications\n5. Then it processes buys in alphabetical order:\n   - Purchases shares up to target levels using available cash (including sale proceeds)\n6. It tracks realized gains (short-term and long-term) and calculates estimated tax\n7. Finally, it outputs all the required information in the specified JSON format\n\nThe implementation correctly follows all the specified rules and produces the expected output format.",
        "reasoning_content": null,
        "name": null,
        "tool_calls": []
       },
       "finish_reason": "stop",
       "logprobs": null
      }
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     "usage": {
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      "completion_tokens": 352,
      "total_tokens": 6494,
      "prompt_tokens_details": {
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      "cache_read_input_tokens": 5744
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     "cost": {
      "usd": 0,
      "diem": 0.00089706
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     "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
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 }
]