延迟与稳定性实测（示例数据）Latency & Stability (Sample Data)
模型实测 · 2026-08 · 示例框架，正式发布前替换为实测数据Benchmarks · 2026-08 · Sample frame; replace with real measurements before publication
测试方法Methodology
固定提示词（约 800 字中文客服回复任务），温度 0.7，单次调用 24 小时周期内重复 50 次，取中位数。首字延迟指从发出请求到收到第一个输出 Token 的时间。Fixed 800-char Chinese support-reply task, temperature 0.7, 50 runs over a 24h window, median reported. First-token latency = time from request to first output token.
| 模型Model | 首字延迟中位数Median first-token | 总响应中位数Median total | 成功率Success rate | 高峰抖动Peak variance |
|---|
| 旗舰模型 A | 1.8s | 9.4s | 99.2% | 中等 |
| 均衡模型 B | 0.9s | 6.1s | 99.6% | 低 |
| 轻量模型 C | 0.4s | 2.8s | 99.8% | 低 |
| 国产模型 D | 1.1s | 7.0s | 98.5% | 高 |
对体验的影响Impact on UX
- 首字延迟决定"有没有反应"的感受：超过 3 秒用户会明显不耐烦，建议首字延迟目标 <2s。First-token latency drives perceived responsiveness: >3s reads as broken. Aim for <2s.
- 成功率决定业务可用性：99% 成功率意味着每天 1 万次调用有 100 次失败，必须配重试与降级。Success rate defines availability: 99% = 100 failures per 10k calls — you need retry and fallback.
- 高峰抖动影响晚高峰体验，建议在真实用户时段做多次采样，而不是只看白天测试。Peak variance hits evening hours hardest — sample during real peak times, not just daytime.
落地建议Takeaway
对交互型业务，用"均衡模型 + 轻量模型降级 + 自动重试"通常比单用旗舰模型体验更好、成本更低。
For interactive products, "balanced model + lightweight fallback + auto retry" usually beats flagship-only on both UX and cost.
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