{"schema": 2, "epoch": 87602, "nonce": "13b91e4b7fef33c7", "hotkey": "5CaXH581GtSjxFaFaJzSN35CyKzvFMQhRAxrNy6qmoztNiPz", "source_hash": "24837b9ae6895829747c5eb448673693fedbeb2e3e62a5ca170051a512053fc0", "weights_hash": "19aafcfeb694bb06e7fbbedbfd7fce6c50bd3389a57eeefd1b5d2e5475afdd62", "model_id": "router", "total_cost_usd": 0.00271064, "n_calls": 6, "call_log_hash": "f0e3e9cc4b108c8589e41777f5bd570123357bef74880f0100b2ea16fcc1cc4e", "measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "confined": true, "latency_s": 137.174, "tokens_in": 1151, "tokens_out": 17772, "results": [{"benchmark": "mmlu", "task_id": "mmlu-5254", "answer": "B", "cost_usd": 7.5e-06, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.016668, 0.000159, 0.000137, 0.000197, 0.98231, 0.000403, 0.000126]}, {"benchmark": "mmlu", "task_id": "mmlu-12856", "answer": "C", "cost_usd": 0.0002063, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.00022, 1.6e-05, 1.5e-05, 2.2e-05, 0.999671, 4.3e-05, 1.3e-05]}, {"benchmark": "math", "task_id": "gsm8k-810", "answer": "Starting with 10 students:\n\n- End of January: 20\n- End of February: 40\n- End of March: 80\n- End of April: 160\n- End of May: 320\n\nAdditional students: \\(320 - 10 = 310\\)\n\n310", "cost_usd": 0.00011039999999999997, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.076397, 0.000188, 0.000162, 0.000219, 0.922464, 0.000432, 0.000138]}, {"benchmark": "math", "task_id": "gsm8k-277", "answer": "30 small holes: \\(30 \\times 3 = 90\\) minutes \n15 large holes: \\(15 \\times 10 = 150\\) minutes \nTotal: \\(90 + 150 = 240\\) minutes \nConvert to hours: \\(240 \\div 60 = 4\\)\n\n4", "cost_usd": 0.0001002, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.16695, 0.000306, 0.000241, 0.000377, 0.831133, 0.000781, 0.000212]}, {"benchmark": "code", "task_id": "lcb-abc389_b", "answer": "import sys\n\nx = int(sys.stdin.readline())\nfactorial = 1\nn = 1\n\nwhile factorial < x:\n n += 1\n factorial *= n\n\nprint(n)", "cost_usd": 4.8099999999999977e-05, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.035223, 7.3e-05, 5.7e-05, 9.3e-05, 0.96432, 0.000186, 4.8e-05]}, {"benchmark": "code", "task_id": "lcb-abc388_c", "answer": "```python\nimport sys\n\ndef solve():\n # Read all input from standard input efficiently\n # split() splits by any whitespace including newlines\n input_data = sys.stdin.read().split()\n \n if not input_data:\n return\n\n iterator = iter(input_data)\n \n try:\n n = int(next(iterator))\n except StopIteration:\n return\n\n # Convert the rest of the tokens to integers\n # This creates a list of integers for the mochi sizes\n a = list(map(int, iterator))\n \n # Defensive check: if input has fewer elements than N, adjust N\n if len(a) < n:\n n = len(a)\n \n count = 0\n ptr = 0\n \n # Iterate through each mochi considering it as the bottom mochi.\n # The array 'a' is sorted in non-decreasing order.\n # For a bottom mochi with size 'bottom_val', we want to count how many\n # mochi with size 'top_val' satisfy 2 * top_val <= bottom_val.\n # Since 'a' is sorted, the valid top mochi will always be a prefix of the array.\n # We maintain a pointer 'ptr' which indicates the number of valid top mochi found so far.\n # As we iterate through larger bottom mochi (increasing 'bottom_val'), the threshold\n # increases, so the set of valid top mochi can only grow. Thus, 'ptr' only moves forward.\n \n for j in range(n):\n bottom_val = a[j]\n \n # Advance ptr while the condition 2 * a[ptr] <= bottom_val holds.\n # ptr represents the count of elements a[0]...a[ptr-1] that satisfy the condition.\n # We check a[ptr] (the next candidate) against the current bottom_val.\n while ptr < n and 2 * a[ptr] <= bottom_val:\n ptr += 1\n \n # Add the number of valid tops for the current bottom mochi to the total count.\n count += ptr\n \n print(count)\n\nif __name__ == '__main__':\n solve()\n```", "cost_usd": 0.00223814, "chosen_rung": 0, "rungs_used": [0], "distribution": [0.996716, 2.2e-05, 1.6e-05, 2.1e-05, 0.003166, 4.6e-05, 1.3e-05]}], "quote": {"measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "report_data": "b3e900f882f93cdea1254cc1b47dae33a0c848b55cfbfddd1c7f19ed2c5441ea", "platform_sig": 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