主流模型成本对比（示例数据）Model Cost Comparison (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
为什么成本要先算清楚Why cost math comes first
API 成本 = 输入 Token 价格 × 输入量 + 输出 Token 价格 × 输出量。同样一个客服场景，模型选错可能让月度成本相差 5–10 倍。下表给出主流模型的公开价格区间（按测试时点，单位：人民币/百万 Token）。API cost = input price × input tokens + output price × output tokens. For the same support-bot workload, choosing the wrong model can multiply monthly cost by 5–10×. The table shows public price ranges at the time of testing (CNY per million tokens).
| 模型Model | 输入价Input | 输出价Output | 典型任务月成本*Typical monthly cost* | 适合场景Best for |
|---|
| 旗舰模型 A | ¥75 | ¥300 | ¥8,400 | 复杂推理、长文写作Complex reasoning, long-form writing |
| 均衡模型 B | ¥18 | ¥72 | ¥1,900 | 客服、总结、翻译Support, summarization, translation |
| 轻量模型 C | ¥3 | ¥12 | ¥320 | 分类、抽取、路由Classification, extraction, routing |
| 国产模型 D | ¥6 | ¥24 | ¥580 | 中文场景、成本敏感Chinese workloads, cost-sensitive |
*假设：日均 2 万次调用，平均每次 1,500 输入 Token + 400 输出 Token。Assumes 20k calls/day, ~1,500 input + 400 output tokens each.
省钱的三条铁律Three rules to cut cost
- 按任务分级用模型：简单任务走轻量模型，复杂任务才用旗舰模型，可节省 60–80% 成本。Match model tier to task: lightweight models for simple tasks, flagship only for hard ones — save 60–80%.
- 控制输出 Token：输出比输入贵 4 倍，设置 max_tokens 上限与精简提示词，收益立竿见影。Trim output tokens: output is ~4× input price. Cap max_tokens and tighten prompts.
- 加缓存与降级：重复请求命中缓存，失败自动降级到低成本模型，可用性与成本兼得。Cache and degrade: cache repeated requests, auto-degrade to cheaper models on failure.
免责声明Disclaimer
以上为示例数据，仅用于展示测试框架。正式数据发布前需按官方最新价格与真实用量重新测算。模型价格随时可能调整。
Sample data only, shown to illustrate the framework. Recalculate with official pricing and real usage before publication. Model prices change frequently.
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