SETTLEMENT TELEMETRY

Inference Log

MID: guoL0skcHy7USat2

Log MIDguoL0skcHy7USat2
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
288 × 4.05 = 0.001166
0 × 0.135 = 0.000000
2391 × 12.15 = 0.0291
CNY 0.0302
Vendor Cost
288 × 3.6 = 0.001037
0 × 0.12 = 0.000000
2391 × 10.8 = 0.0258
CNY 0.0269
Latency39.81099969s
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 300 tokens 的输入):碳中和、under high concurrency、服务治理、并给出可落地的建议、内存管理、latency distribution、cache strategy、在生产环境中、load balancing、潜在的风险点、st",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "在生产环境 under high concurrency 的分布式系统中,人工智能 token generation 的 streaming response 要求在服务治理、性能优化与碳中和之间做设计取舍。可落地建议如下:\n\n1. **并发控制与内存管理**:用对象池、KV cache 前缀共享、",
    "tool_calls": [],
    "stop_reason": "stop"
  }
}
Stop Reasonstop
Token Usage
{
  "completion_tokens": 2391,
  "completion_tokens_details": {
    "reasoning_tokens": 2107
  },
  "prompt_tokens": 288,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 2679
}
Fixes
Created At9/8/2026, 7:33:47 PM