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codex-usage v2.7.1

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@github-actions github-actions released this 23 Sep 03:42
· 2 commits to main since this release
6647f2f

codex-usage v2.7.1

新增 GPT-6 Sol 和 GPT-6 Luna 的内置定价与 Codex Fast 费用折算。升级后,这两个模型已记录的用量可以直接参与 API 等价成本估算,无需手动配置模型映射。

模型 Input Cached Input Cache Write Output
GPT-6 Sol 2.00 0.20 2.50 10.00
GPT-6 Luna 0.10 0.01 0.125 0.50

以上为 2026-09-23 核对的 Standard 短上下文文本价格,单位 USD / 1M Token。

  • 支持正式模型标识 gpt-6-solgpt-6-luna,沿用有效日期后缀的快照匹配规则,并可作为本机内部模型映射的目标。
  • 两个模型明确标记的 Fast 用量按 ChatGPT Codex 2.5× 额度倍率折算;原始 Token 数量保持不变。该估算与 API Fast 计费口径不同,仍不推断长上下文附加费。
  • Dashboard 内置价格表、模型映射选项、synthetic Demo 和中英文文档同步更新。
  • 沿用 schema v11,从 v2.7.0 升级无需为此次模型目录更新重建历史。原有模型单价与用户已有的内部模型映射保持不变;未知模型继续标为未定价。

验证覆盖四类 Token 单价、缓存写入精度、快照匹配、内部模型映射、Fast 加权和真实服务的 JSONL → API → Dashboard 流程,并运行 Go 测试、静态检查及 Dashboard / synthetic Demo 浏览器回归。


Add built-in pricing and Codex Fast cost estimates for GPT-6 Sol and GPT-6 Luna. Existing recorded usage for these models becomes priceable after upgrading without a manual model mapping.

Standard short-context text rates verified on September 23, 2026, in USD / 1M tokens:

Model Input Cached Input Cache Write Output
GPT-6 Sol 2.00 0.20 2.50 10.00
GPT-6 Luna 0.10 0.01 0.125 0.50
  • Resolve the exact model IDs and valid dated snapshots; allow explicit internal-model aliases to either model.
  • Apply the documented ChatGPT Codex 2.5× credit multiplier to confirmed Fast usage without changing raw token counts. This remains distinct from API Fast billing and does not infer long-context surcharges.
  • Update the Dashboard catalog, alias choices, synthetic Demo, and Chinese/English documentation.
  • Retain schema v11. No history rebuild is needed for this catalog update when upgrading from v2.7.0. Existing model rates and internal-model mappings are retained; unknown models remain unpriced.

Validation covers token-category rates, cache-write precision, snapshot matching, aliases, Fast weighting, and the real-service JSONL → API → Dashboard path, plus Go tests, vet, and Dashboard / synthetic Demo browser regressions.