SETTLEMENT TELEMETRY

Inference Log

MID: guw12QlchGYpMHu3

Log MIDguw12QlchGYpMHu3
Typechat_completion
Aliasdeepseek-v4-pro-g3 / gucs9pv3TRoBtepN
Hostinforaxi-server:3300:v1.1.4013-14-gbfebabe6d
Mode / Protoreq-v1-chatcmplopenai-chatcmpl
Vendor/LLMvolcengine-bizplan-dtx/deepseek-v4-pro-ga-260813
Statussuccess
User Pricing
218 × 4.05 = 0.000883
0 × 0.135 = 0.000000
1704 × 12.15 = 0.0207
CNY 0.0216
Vendor Cost
218 × 3.6 = 0.000785
0 × 0.12 = 0.000000
1704 × 10.8 = 0.0184
CNY 0.0192
Latency39.52590750s
Summary
{
  "system_prefix": null,
  "history_count": 1,
  "user_summary": {
    "text": "请围绕下面的关键词写一段连贯的分析(约 200 tokens 的输入):分布式系统、token generation、load balancing、streaming response、cache strategy、人工智能、以及未来发展趋势、请举例说明、token generation、laten",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "在分布式人工智能推理系统中,**token generation** 是流式响应(**streaming response**)的核心路径。以 LLM 服务为例,**load balancing** 若只看请求数而忽略并发 token 数与显存水位,容易导致 **under high concurr",
    "tool_calls": [],
    "stop_reason": "length"
  }
}
Stop Reasonlength
Token Usage
{
  "completion_tokens": 1704,
  "completion_tokens_details": {
    "reasoning_tokens": 1447
  },
  "prompt_tokens": 218,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 1922
}
Fixes
Created At9/9/2026, 6:41:22 PM