ALL TRANSACTIONS

guuv2O8BrNgSOTBt

CONSUMP_PREPOST_GRANT

completed · 9/9/2026, 3:23:09 PM

Statuscompleted
Date9/9/2026, 3:23:09 PM
Usersamuel_wang@yeah.net
User AmountCNY 0.0171882
User BalancegudA6ExG30hklCQ2
Vendorvolcengine-bizplan-dtx
Vendor CostCNY -0.0152784
Vendor BalancegtMyXDjqOp6l8OSh
Aliasdeepseek-v4-pro-g3 / gucs9pv3TRoBtepN
Mode / Protoreq-v1-chatcmpl / openai-chatcmpl
Log MIDguuv2O2XD0w2k2gm
SETTLEMENT TELEMETRY

Inference Log

Log MIDguuv2O2XD0w2k2gm
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
152 × 4.05 = 0.000616
0 × 0.135 = 0.000000
1364 × 12.15 = 0.0166
CNY 0.0172
Vendor Cost
152 × 3.6 = 0.000547
0 × 0.12 = 0.000000
1364 × 10.8 = 0.0147
CNY 0.0153
Latency22.85083965s
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 100 tokens 的输入):性能优化、内存管理、结合实际案例、under high concurrency、内存管理、并给出可落地的建议、streaming response、机器学习、load balancing、背后的设计取舍、如何度量与验证、load",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "1. **性能优化与内存管理**:在 under high concurrency 下,结合实际案例(如机器学习推理服务),采用 **streaming response** 降低内存峰值,配合对象池/零拷贝减少 GC 压力。  \n2. **负载均衡与设计取舍**:通过 **load balanci",
    "tool_calls": [],
    "stop_reason": "length"
  }
}
Stop Reasonlength
Token Usage
{
  "completion_tokens": 1364,
  "completion_tokens_details": {
    "reasoning_tokens": 1235
  },
  "prompt_tokens": 152,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 1516
}
Fixes
Created At9/9/2026, 3:23:09 PM