SETTLEMENT TELEMETRY

Inference Log

MID: guw1WZvTZlWXlwBK

Log MIDguw1WZvTZlWXlwBK
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
219 × 4.05 = 0.000887
0 × 0.135 = 0.000000
1996 × 12.15 = 0.0243
CNY 0.0251
Vendor Cost
219 × 3.6 = 0.000788
0 × 0.12 = 0.000000
1996 × 10.8 = 0.0216
CNY 0.0223
Latency38.64900465s
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):需要考虑的权衡、人工智能、token generation、token generation、under high concurrency、人工智能、请举例说明、在生产环境中、算法复杂度、以及未来发展趋势、并给出可落地的建议、结",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "在高并发(under high concurrency)生产环境中,人工智能/机器学习的 token generation 系统需重点权衡 latency distribution、系统吞吐与内存管理之间的关系。从技术角度分析,自回归 token generation 算法复杂度较高,注意力机制通常",
    "tool_calls": [],
    "stop_reason": "stop"
  }
}
Stop Reasonstop
Token Usage
{
  "completion_tokens": 1996,
  "completion_tokens_details": {
    "reasoning_tokens": 1796
  },
  "prompt_tokens": 219,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 2215
}
Fixes
Created At9/9/2026, 6:42:47 PM