{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-28T05:20:57.982Z","headline":"Getting the most out of GPT-5.6： Sol， Terra， and Luna","description":"Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.","url":"https://www.aioga.com/news/cms3qleu3018cro825lxolqvk/","mainEntityOfPage":"https://www.aioga.com/news/cms3qleu3018cro825lxolqvk/","datePublished":"2026-07-27T21:26:17.761Z","dateModified":"2026-07-27T21:26:17.761Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.cerebras.ai/blog/getting-the-most-out-of-gpt-5-6-sol-terra-and-luna","https://aihot.virxact.com/items/cms3qleu3018cro825lxolqvk"],"canonicalUrl":"https://www.aioga.com/news/cms3qleu3018cro825lxolqvk/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai. 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Learn more at cerebras.ai.","source":"Cerebras：Blog（网页）","sourceUrl":"https://www.cerebras.ai/blog/getting-the-most-out-of-gpt-5-6-sol-terra-and-luna","aiHotUrl":"https://aihot.virxact.com/items/cms3qleu3018cro825lxolqvk","publishedAt":"2026-07-27T21:26:17.761Z","category":"技巧观点","score":19,"selected":false,"articleBody":["Cerebras and AMD Partner on Disaggregated Inference. Learn more >>","Your Codex subscription now comes with 3 main models, Sol, Terra, and Luna, each independently trained and served：https://community.openai.com/t/introducing-gpt-5-6-series-sol-terra-and-luna-coming-july-9-10am-pt/1384931, with reasoning dials. Together, they let you choose a balance of speed, cost, and intelligence for each task.","On price, Terra costs 1/2 as much as Sol, while Luna costs one-fifth as much, across both short- and long-context usage.","This pricing tracks pretty closely with intelligence and speed. In practice, each model is best suited to a different kind of work:","How do you decide which model should handle a request and how much reasoning it should apply? That choice must be made before any tokens are generated.","A simple way to approach any task is to choose the model that offers the best balance of speed and intelligence at the price you are willing to pay. Within the GPT-5.6 family :","1. GPT-5.6-Sol : Highest intelligence floor and ceiling","2. GPT-5.6-Luna: Highest speed floor and ceiling","A good default is to start most tasks with Luna, then move up to Terra or Sol when progress stalls, such as when agents get stuck, fixes stop landing, or the model begins losing the thread. If you are willing to pay more for both speed and intelligence, you can run Sol on Cerebras at 750 tokens per second：https://openai.com/index/previewing-gpt-5-6-sol/, which is up to 10x faster speed advantage than regular mode of Sol：https://artificialanalysis.ai/models/gpt-5-6-sol.","Another way to tailor how the model handles a task is by adjusting its reasoning level：https://x.com/cerebras/status/2067357992929153268. In Codex, you can choose from five options: “Light,” “Medium,” “High,” “Extra High,” and “Ultra”.","By using more computer processing time to increase the accuracy of the output, the model will produce tokens arguing with and questioning itself before giving the user an answer.","We typically reserve Ultra mode for our biggest research questions and novel challenges. Ultra mode has a tendency to drain your usage limits very quickly, but you can rest assured the model has used the full reasoning budget available to explore, verify, and refine its answer.","In Artificial Analysis’ testing from July 17：https://artificialanalysis.ai/models/gpt-5-6-sol/, each step up in GPT-5.6 Sol’s reasoning level increased the average cost per task by roughly 50%. The charts below show how additional reasoning affects both intelligence and cost.","Cached input is 90% cheaper than fresh input：https://developers.openai.com/api/docs/guides/prompt-caching. Codex tasks that repeatedly process the same codebase or context benefit enormously.","Each of the GPT-5.6 models has a cache TTL of ~30 minutes：https://developers.openai.com/api/docs/guides/prompt-caching. That means maintaining a single session you work in will be better than starting a new session for each task.","Codex’s compaction has also gotten good enough that you can run a single session to hundreds of millions of tokens without even experiencing any issues.","If you keep the session hot, you’ll have much cheaper prompt processing. You can set Codex automations scheduled every 20 minutes to keep your cache alive and use those automations to drive long running processes.","Different models are trained on different data and optimized for different goals, meaning each will be slightly better or worse at certain things.","The Advisor workflow, gives one agent a narrow job: read the full session, keep track of the goal and constraints, and step in whenever the worker starts drifting. This helps automatically steer the worker and improve reliability across longer tasks.","Local Codex also lets you bring other providers to the app. This means you can experiment with lower-cost, open-weight models like Kimi K2.7 Code , or models with different strengths, such as GLM-5.2 for long-horizon reasoning and coding , while staying inside the familiar Codex experience.","You can then use these external models for bounded subagent work, helping reduce costs or take advantage of the different strengths of each model.","One important nuance: this is done through Codex configuration：https://learn.chatgpt.com/docs/config-file/config-reference and custom agent files, rather than a simple in-app model picker. Codex supports local providers such as Ollama and LM Studio, along with custom Responses-compatible providers.","Subscription usage limits are pretty generous, but as AI agents become more useful to more people, many are pushing the limits way beyond what one subscription can offer.","And even if you have the money to burn , not every “frontier” model sits at the frontier of all use cases. Often a small model can completely wipe the floor with a consortium of the world’s best models on a few benchmarks.","Speed is an important toggle, during the day when you’re interacting with your agents, high tok/s can dramatically improve your experience. During off hours, a /goal with a slow, extra reasoning model can make a world’s difference.","And lastly, keep an eye on Artificial Analysis, it’s full of high quality model speed benchmarks and intelligence indexes.","Thank you to Sarah Chieng (@MilksandMatcha), Joyce Er, and Hai-Ching for your review and input, and Halley Change (@halleychangg) for the graphic design in this blog.","Performance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.","1237 E. Arques Ave Sunnyvale, CA 94085"],"articleImages":[{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/0340a00948049fa6c5a80e12d08b75be2b06539e-1525x1452.png?auto=format&dpr=2&fit=max&q=75&w=1525","alt":"","afterParagraph":2,"url":"/media/articles/cms3qleu3018cro825lxolqvk/5dc0aadce5bb30f1.avif"},{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/b9e3f94ce58f4eb8dbbe25a626ccd049ced1b976-1540x867.png?auto=format&dpr=2&fit=max&q=75&w=1540","alt":"","afterParagraph":3,"url":"/media/articles/cms3qleu3018cro825lxolqvk/b35ce9655c954bdd.avif"},{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/f3b5f90e8f50d09f13d6fe8c3a582efc6a47cc2f-1589x314.png?auto=format&dpr=2&fit=max&q=75&w=1589","alt":"","afterParagraph":8,"url":"/media/articles/cms3qleu3018cro825lxolqvk/accaa20f60303d76.avif"},{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/ad06f2b993eaa0f9858b1e5975ad5b9c97f2e9df-1302x728.png?auto=format&dpr=2&fit=max&q=75&w=1302","alt":"","afterParagraph":12,"url":"/media/articles/cms3qleu3018cro825lxolqvk/bb4f0956b2cb22a8.avif"},{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/805ad8722ee4021ccaccc835516795f8e25cfd84-1227x251.png?auto=format&dpr=2&fit=max&q=75&w=1227","alt":"","afterParagraph":16,"url":"/media/articles/cms3qleu3018cro825lxolqvk/b5d77ef5411f8f6f.avif"},{"sourceUrl":"https://cdn.sanity.io/images/e4qjo92p/production/fa4a07fed147ba64316af57b9934d2bfc92b3b29-1627x985.png?auto=format&dpr=2&fit=max&q=75&w=1627","alt":"","afterParagraph":21,"url":"/media/articles/cms3qleu3018cro825lxolqvk/dda992ec11de5ba3.avif"}],"mediaStatus":"ok","articleBodyZh":["Cerebras 和 AMD 在分离式推理方面进行合作。了解更多 >>","您的 Codex 订阅现在包含三个主要模型：Sol、Terra 和 Luna，每个模型都是独立训练和服务的：https://community.openai.com/t/introducing-gpt-5-6-series-sol-terra-and-luna-coming-july-9-10am-pt/1384931，并带有推理调节选项。它们共同让您可以为每个任务选择速度、成本和智能之间的平衡。","在价格方面，Terra 的费用是 Sol 的一半，而 Luna 的费用是五分之一，无论是短上下文还是长上下文使用。","这种定价与智能和速度大致相符。实际上，每个模型最适合不同类型的工作：","如何决定哪个模型处理请求以及应应用多少推理？这个选择必须在生成任何 token 之前做出。","处理任何任务的一个简单方法是选择在您愿意支付的价格下速度和智能平衡最合适的模型。在 GPT-5.6 系列中：","1. GPT-5.6-Sol：最高的智能底线和上限","2. GPT-5.6-Luna：最高的速度底线和上限","一个好的默认方法是大多数任务先使用 Luna，然后在进度停滞时转向 Terra 或 Sol，例如当代理卡住、修复无法生效或模型开始丢失上下文时。如果您愿意为速度和智能支付更多费用，可以在 Cerebras 上以每秒 750 个 token 运行 Sol：https://openai.com/index/previewing-gpt-5-6-sol/，这比 Sol 的常规模式：https://artificialanalysis.ai/models/gpt-5-6-sol 快最多 10 倍。","调整模型处理任务的另一种方法是设置其推理级别：https://x.com/cerebras/status/2067357992929153268。在 Codex 中，您可以从五个选项中选择：“轻量”、“中等”、“高”、“超高”和“极致”。","通过使用更多的计算时间来提高输出的准确性，模型将在给用户答案之前生成与自身争论和质疑的 tokens。","我们通常将超级模式保留用于我们最大的问题和新颖的挑战。超级模式往往会非常快地消耗你的使用额度，但你可以放心，模型已经使用了可用的全部推理预算来探索、验证和优化它的答案。","在 Artificial Analysis 2023 年 7 月 17 日的测试中：https://artificialanalysis.ai/models/gpt-5-6-sol/，GPT-5.6 Sol 每提升一步推理水平，平均每个任务的成本约增加 50%。下图展示了额外的推理如何同时影响智能和成本。","缓存输入比全新输入便宜 90%：https://developers.openai.com/api/docs/guides/prompt-caching。反复处理相同代码库或上下文的 Codex 任务将大大受益。","每个 GPT-5.6 模型的缓存 TTL 约为 30 分钟：https://developers.openai.com/api/docs/guides/prompt-caching。这意味着保持一个你工作的会话，比每个任务启动一个新会话要好。","Codex 的压缩技术也已足够出色，你可以在单个会话中处理数亿个 token，而不会遇到任何问题。","如果你保持会话活跃，你将获得更便宜的提示处理费用。你可以设置 Codex 自动化每 20 分钟运行一次，以保持缓存活跃，并使用这些自动化来驱动长时间运行的流程。","不同模型训练的数据不同、优化目标不同，这意味着每个模型在某些方面会稍好或稍差。","Advisor 工作流给一个代理一个狭窄的任务：阅读完整会话，跟踪目标和约束，并在工作者偏离时介入。这有助于自动引导工作者，并提高较长任务的可靠性。","本地 Codex 还允许你将其他提供者引入应用程序。这意味着你可以在熟悉的 Codex 体验中，试验低成本的开源权重模型，如 Kimi K2.7 Code，或具有不同优势的模型，例如适合长远推理和编码的 GLM-5.2。","然后你可以将这些外部模型用于有界子代理工作，有助于降低成本或利用每个模型的不同优势。","一个重要的细节：这是通过 Codex 配置完成的：https://learn.chatgpt.com/docs/config-file/config-reference 以及自定义代理文件，而不是简单的应用内模型选择器。Codex 支持本地提供商，如 Ollama 和 LM Studio，以及兼容 Responses 的自定义提供商。","订阅使用限制相当宽松，但随着 AI 代理对更多人变得更有用，许多人正在将使用量推到单个订阅所能提供的极限之外。","即使你有足够的资金，并非每个“前沿”模型都在所有使用场景中处于前沿。通常，一个小模型在一些基准测试中就能完全碾压由世界上最优秀模型组成的联合体。","速度是一个重要的开关，在你白天与代理互动时，高 tok/s 可以显著提升你的体验。在非工作时间，用慢速、额外推理能力的模型执行 /goal 也能产生巨大的差异。","最后，关注 Artificial Analysis，它充满了高质量的模型速度基准测试和智能指数。","感谢 Sarah Chieng (@MilksandMatcha)、Joyce Er 和 Hai-Ching 的审阅与意见，以及 Halley Change (@halleychangg) 为此博客提供的平面设计。","性能比较基于第三方基准测试或内部测试。与基于 GPU 系统相比，观察到的推理速度提升可能因工作负载、配置、日期和被测试模型的不同而有所差异。","1237 E. 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