GPT‑5.6 树立性价比新标杆
GPT‑5.6 模型系列让前沿级智能体性能变得大幅更实惠，同时也在推进可能性的边界。
在本指南中，我们展示了初创企业如何利用更智能的模型选择以及新的 API 控制项（这些控制项有助于推理连续性、多智能体编排和程序化工具调用），以极低的成本构建更快、能力更强的智能体。
更出色的开箱即用体验
自 GPT‑5 以来，每一代模型都致力于用更少的 token 处理更长周期的任务。GPT‑5.6 延续了这一轨迹：更强的智能体性能、更低的成本，且底层框架几乎无需改动。
顶层成本效率的提升与更低推理强度下准确率的提高相辅相成。例如，在 Agents' Last Exam 基准测试中，当框架保持不变时，GPT‑5.6 Sol 在“低”推理强度下的表现超过了 GPT‑5.5 在“高”推理强度下的表现。我们在生产环境测试中也看到了类似的成功案例，初创企业报告称，通过将推理强度从之前的默认值降低，在一系列工作流程中实现了显著的成本改善。
模型选择
从历史上看，升级到旗舰模型并使用最高可用推理强度一直是长周期用例的最佳选择。这在很大程度上是因为这些模型在处理更长上下文和工具调用方面明显优于成本优化型模型。这一情况随着 5.6 系列的出现而改变：借助更多的测试时计算，Luna 和 Terra 通常可以达到与 GPT‑5.4 和 5.5 相近的表现，同时成本大幅降低。
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以 BrowseComp 中的任务为例：这是一个基于搜索的基准测试，用于检验模型搜索冷门事实的能力。三个月前，GPT‑5.5（Extra High）在该基准测试中得分 84.36%，总成本为 33.27 美元。发布时，GPT‑5.6 Luna（Extra High）实现了基本相同的性能，得分 84.04%，成本仅为 1.33 美元。此后我们进一步降低了价格。请阅读更多了解我们最新的降价信息。
5.6 系列中较小的模型非常适合高吞吐量工作负载、延迟敏感型交互以及智能体工作流中的重复步骤。例如，如果你经营一家法律科技初创公司，在智能体分析之前需要解析手写备忘录，那么现在可以使用 Terra 或 Luna 进行提取，而不是在整个用例中使用前沿模型，从而节省大量成本。
演进 Responses API 以构建更高效的智能体
除了让 GPT‑5.6 开箱即用地表现更佳之外，我们还为 Responses API 增加了新的原语以释放更多增益。我们通过三项互补的架构干预对 GPT‑5.6 进行了端到端训练，使智能体能够更高效地运行：
- 复用已完成的工作： 通过允许推理在模型轮次之间持久化（在新窗口中打开），并使用原生压缩（在新窗口中打开）来压缩长时间运行的对话，模型可以在更长的任务周期内保持工作的连贯性，而不会混淆或需要重建先前的上下文。
- 在适当情况下进行并行分解： 使用原生多智能体编排（在新窗口中打开），可以跨并行工作流协调多个智能体，从而更快地完成复杂任务。
- 将确定性工作转移到代码中： 使用程序化工具调用（在新窗口中打开）在模型的上下文窗口之外过滤、聚合和编排工具输出，将模型 token 保留用于判断，从而降低成本和延迟，并减少上下文退化。
组合使用时，效果可能非常显著。例如，在 ARC-AGI-3 上，GPT‑5.6 Sol 使用标准框架得分为 13.3%。然而，在启用保留推理和压缩后，得分跃升至 38.3%——同时使用的输出 token 减少了约 6 倍。模型没有变化，但性能几乎提升了三倍。你可以在此处阅读更多关于我们的 ARC-AGI-3 框架调查的内容。
程序化工具调用
智能体工作流通常涉及两类工作：
- 需要判断力的任务
- 主要涉及移动、过滤和组合数据的工作
当智能体检索 100 份文件、按日期过滤并识别相关交易时，模型不应在其上下文窗口中对每个中间结果进行推理。程序化工具调用让 GPT‑5.6 能够编写 JavaScript 来编排工具、并行运行独立调用，并在上下文窗口之外处理其输出。模型得以专注于需要智能的部分：运用判断力。
多智能体
在复杂的可并行化任务上，将操作和推理分布到多个智能体工作流中，可以实现更快的任务完成速度和更高的智能水平。在这些设置中，主智能体负责编排子智能体并向它们委派任务。子智能体并行追求各自的目标，最后将输出传回主智能体进行最终综合。团队可以通过在 Responses API 中启用多智能体（在新窗口中打开）来原生地利用多智能体功能。这也是 ChatGPT 中超能力设置的工作方式。
尽管 GPT‑5.6 对合适的子智能体数量以及何时生成它们有很强的判断力，但多智能体行为是高度可引导的。指示模型何时调用子智能体，可以增加仅在额外 token 支出能带来更好性能的情况下才生成智能体的可能性。
提示缓存
在整个模型系列中，提示缓存 TTL 已延长至至少 30 分钟，并且现在可以在模型的上下文窗口内确定性地设置缓存断点。这使得初创企业能够显著提高缓存命中率。
除了设置缓存断点之外，继续使用合适的 prompt_cache_key（在新窗口中打开） 可以增加请求落在与之前服务相同前缀的同一推理引擎上的可能性，从而降低延迟。
结论
这些示例中突出的一点是，构建智能体的经济性已经发生了多么巨大的变化。
曾经每一步都需要前沿模型的用例，如今通过使用更小的模型、调整推理力度以及做出高效的架构选择，就能以极低的成本获得相当甚至更好的结果。
我们很期待看到大家的创作！
GPT‑5.6 sets a new standard for price-performance
The GPT‑5.6 model family makes frontier-level agent performance dramatically more affordable, while also advancing the frontier of what is possible.
In this guide, we show how startups are using smarter model selection and new API controls that help with reasoning continuity, multi-agent orchestration, and programmatic tool calling to build faster, more capable agents at a fraction of the cost.
A better out-of-the-box experience
Since GPT‑5, each model generation has sought to tackle longer-horizon tasks with fewer tokens. GPT‑5.6 continues that trajectory: stronger agent performance, lower costs, with minimal changes to the underlying harness.
The improvements in top-line cost efficiency are compounded with increased accuracy at lower reasoning efforts. For example, on Agents’ Last Exam, GPT‑5.6 Sol at “low” reasoning outperformed GPT‑5.5 at “high” reasoning when the harness was kept constant. We’ve seen similar success stories in production testing where startups report seeing significant cost improvements across a range of workflows by reducing the reasoning effort from the prior defaults.
Model Selection
Historically, upgrading to a flagship model at the highest reasoning available has been the best option for long-horizon use cases. This has been in large part due to these models being significantly more capable than cost-optimized models at handling longer contexts and tool calling. This has changed with the 5.6-family: with more test-time compute, Luna and Terra can often perform similar to GPT‑5.4 and 5.5 while being significantly cheaper.
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Consider tasks in BrowseComp: a search-based benchmark that tests a model’s ability to search for obscure facts. Three months ago, GPT‑5.5 (Extra High) scored 84.36% on this benchmark for a total cost of $33.27. At launch, GPT‑5.6 Luna (Extra High) delivers essentially the same performance, scoring 84.04% at a cost of $1.33. We’ve since reduced prices further. Read more on our latest price cuts.
The smaller 5.6-family models are a strong fit for high-volume workloads, latency-sensitive interactions, and repeated steps within agentic workflows. For example, if you’re operating a legal-tech startup that parses handwritten memos prior to agentic analysis, instead of using a frontier model for the entire use case, you can now use Terra or Luna for extraction and register significant cost savings.
Evolving the Responses API to architect more efficient agents
In addition to making GPT‑5.6 more performant out of the box, we also shipped new primitives to the Responses API to unlock further gains. We trained GPT‑5.6 end-to-end with three complementary architectural interventions that enable agents to operate more efficiently:
- Reuse work already performed: by allowing reasoning to be persisted(opens in a new window) across model turns and using native compaction(opens in a new window) to compress long-running conversations, the model can maintain coherence in its work across longer task horizons without getting confused or having to reconstruct prior context.
- Parallel decomposition where appropriate: using native multi-agent orchestration(opens in a new window) allows coordinating multiple agents across parallel workstreams to finish complex tasks faster.
- Move deterministic work into code: using programmatic tool calling(opens in a new window) to filter, aggregate, and orchestrate tool outputs outside the model’s context window, reserving model tokens for judgment and reducing cost, latency, and context rot.
Used together, the difference can be dramatic. For example, on ARC-AGI-3, GPT‑5.6 Sol scored 13.3% with the standard harness. After enabling retained reasoning and compaction, however, the score jumped to 38.3%—while using roughly 6× fewer output tokens. No changes to the model, but nearly three times the performance. You can read more on our ARC-AGI-3 harness investigation here.
Programmatic Tool Calling
Agentic workflows often involve two kinds of work:
- Tasks that require judgment
- Work that mostly requires moving, filtering, and combining data
When an agent retrieves 100 filings, filters them by date, and identifies relevant transactions, the model shouldn’t have to reason over every intermediate result in its context window. Programmatic Tool Calling lets GPT‑5.6 write JavaScript to orchestrate tools, run independent calls in parallel, and process their outputs outside the context window. The model is left to focus on what requires intelligence: applying judgment.
Multi-agent
On complex, parallelizable tasks, distributing actions and reasoning across multiple agent workstreams enables faster task completion as well as higher intelligence. In these setups, the primary agent is responsible for orchestrating the subagents and delegating tasks to them. The subagents pursue their objectives in parallel and finally pass back their output to the primary agent for final synthesis. Teams can start leveraging multi-agent natively by enabling multi-agent(opens in a new window) in the Responses API. This is also how the ultra capability setting in ChatGPT works.
Although GPT‑5.6 has a strong sense of the appropriate number of subagents and when to spawn them, multi-agent behavior is very steerable. Instructing the model on when to invoke subagents can increase the likelihood of spawning agents only in situations where the additional token expenditures would result in better performance.
Prompt Caching
Across the entire family of models, the prompt cache TTL has been extended to a minimum of 30 minutes and cache breakpoints can now be set deterministically within a model’s context window. This has enabled startups to significantly improve their cache hit rate.
In addition to setting cache breakpoints, continuing to use an appropriate prompt_cache_key(opens in a new window) increases the likelihood of requests landing on the same inference engine as one that previously served the same prefix, thereby reducing latency.
Conclusion
What stands out across these examples is how much the economics of building agents have changed.
Use cases that once required a frontier model at every step can now achieve comparable or better results at a fraction of the cost by using smaller models, tuning reasoning effort, and making efficient architectural choices.
We're excited to see what you all build!
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