SETTLEMENT TELEMETRY

Inference Log

MID: guvs7SElkPb5oywY

Log MIDguvs7SElkPb5oywY
Typechat_completion
Aliasdeepseek-v4-flash-g3 / gucs9pn4RAHUO73l
Hostinforaxi-server:3300:v1.1.4013-14-gbfebabe6d
Mode / Protoreq-v1-chatcmplopenai-chatcmpl
Vendor/LLMvolcengine-bizplan-dtx/deepseek-v4-flash-ga-260731
Statussuccess
User Pricing
137 × 1.35 = 0.000185
0 × 0.045 = 0.000000
160 × 4.05 = 0.000648
CNY 0.000833
Vendor Cost
137 × 1.2 = 0.000164
0 × 0.04 = 0.000000
160 × 3.6 = 0.000576
CNY 0.000740
Latency2.73421693s
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 80 tokens 的输入):latency distribution、结合实际案例、背后的设计取舍、背后的设计取舍、并给出可落地的建议、分布式系统、latency distribution、under high concurrency、token gener",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "在高并发下分布式系统的 token generation 延迟分布常呈长尾:如某 LLM 服务 99% 请求在 200ms 内,但峰值尾延迟超过 2s,源于排队与资源争抢。设计取舍在于牺牲平均吞吐换取尾部稳定性,或反之。可落地建议:为生成任务拆分细粒度队列,限流并隔离高优先级请求,同时用容量预估和缓",
    "tool_calls": [],
    "stop_reason": "stop"
  }
}
Stop Reasonstop
Token Usage
{
  "completion_tokens": 160,
  "completion_tokens_details": {
    "reasoning_tokens": 63
  },
  "prompt_tokens": 137,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 297
}
Fixes
Created At9/9/2026, 6:15:22 PM