GPT-5.6 Sol (batch)
OpenAIGPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...
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GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...
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GPT-5.6 Sol Pro is the same underlying model as GPT-5.6 Sol, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
GPT-5.6 Sol Pro is the same underlying model as GPT-5.6 Sol, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
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对于复杂的推理和编码,可以选择 GPT-5.6 Sol;对于智能和成本之间的平衡,可以选择 GPT-5.6 Terra;对于对成本敏感、高容量的工作负载,可以使用 GPT-5.6 Luna。 所有最新的 OpenAI 模型均支持文本和图像输入、文本输出、多语言功能以及视觉识别。这些模型可通过 Responses API 使用。
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GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is suited for everyday coding, reasoning, and agentic...
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GPT-5.6 Terra Pro is the same underlying model as GPT-5.6 Terra, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
GPT-5.6 Terra Pro is the same underlying model as GPT-5.6 Terra, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
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GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy...
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GPT-5-chat指的是当前在 ChatGPT 中使用的 GPT-5 最新版本。GPT-5 是我们下一代高智能旗舰模型。它接受文本和图像输入,并生成文本输出
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GPT-5-Codex 是 GPT-5 的一个版本,针对 Codex 中的代理编码进行了优化。与其前身 codex-1 一样,该模型在各种环境中使用强化学习对真实世界的编码任务进行训练,以生成与人类风格和公关偏好密切相关的代码,精确遵守指令,并迭代运行测试,直到获得通过的结果。
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GPT-5-Codex 是 GPT-5 的一个版本,针对 Codex 中的代理编码进行了优化。与其前身 codex-1 一样,该模型在各种环境中使用强化学习对真实世界的编码任务进行训练,以生成与人类风格和公关偏好密切相关的代码,精确遵守指令,并迭代运行测试,直到获得通过的结果。
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