SETTLEMENT TELEMETRY

Inference Log

MID: guw0nDCRoS8uQzfs

Log MIDguw0nDCRoS8uQzfs
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
219 × 1.35 = 0.000296
0 × 0.045 = 0.000000
313 × 4.05 = 0.001268
CNY 0.001563
Vendor Cost
219 × 1.2 = 0.000263
0 × 0.04 = 0.000000
313 × 3.6 = 0.001127
CNY 0.001390
Latency4.80601606s
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):内存管理、云计算架构、请详细说明、人工智能、潜在的风险点、服务治理、并发控制、背后的设计取舍、分点阐述其中的、内存管理、分布式系统、背后的设计取舍、机器学习、背后的设计取舍、token generation、请举例说明、潜在的风",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "在现代云计算架构中,内存管理直接制约着系统吞吐量,尤其在高并发与流式响应(streaming response)场景下,其背后的设计取舍尤为关键。以机器学习模型服务为例,Token generation 通常采用自回归解码,每次迭代需缓存中间状态(KV cache),这本质是内存与算力的权衡:缓存越",
    "tool_calls": [],
    "stop_reason": "length"
  }
}
Stop Reasonlength
Token Usage
{
  "completion_tokens": 313,
  "completion_tokens_details": {
    "reasoning_tokens": 56
  },
  "prompt_tokens": 219,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 532
}
Fixes
Created At9/9/2026, 6:40:39 PM