Claude Opus 5 发布：以一半价格逼近 Claude Fable 5，面向长程 Agent 提供跃升式能力

Claude Opus 5 今日正式推出。这是一款深思熟虑且主动的模型，以一半的价格，接近 Claude Fable 5 的前沿智能水平。

在 Frontier-Bench 和 GDPval-AA 等编程与知识工作评测中，Opus 5 达到了新的业界最佳水平；不过，在网络安全任务上，它仍落后于 Mythos 5。

Opus 5 专为日常使用而设计：其工作效率高于其他模型。它现已成为 Claude Max 的默认模型，也是 Claude Pro 中能力最强的模型。

性能与成本效益

Claude Opus 5 以与前代 Opus 4.8 相同的价格，提供了大幅提升的性能。本节图表展示了性能如何随模型的 effort 设置而变化；客户可借此在智能水平与 Token 消耗之间进行优化，以获得更快、更低成本的结果。

Opus 5 在高价值软件工程任务中表现出色。例如，在 Frontier-Bench v0.1 上，Opus 5 超越了所有其他模型，并以更低的单任务成本，取得超过 Opus 4.8 两倍的性能。在 CursorBench 3.2 上，于最高 effort 设置下，该模型的得分距 Fable 5 峰值仅差 0.5%，但单任务成本仅为后者的一半；在 high、xhigh 和 max effort 设置下，它也在相同成本下实现了高于所有其他模型的性能。

我们在知识工作与问题解决任务中也观察到了类似结果。例如：

• 在 ARC-AGI 3 上——这项评测要求模型解决新颖问题——Opus 5 的得分是次优模型的三倍。
• 在 Zapier AutomationBench 上——该评测衡量模型能否端到端完成商业任务——在相同单任务成本下，Opus 5 的通过率约为次优模型的 1.5 倍。即使在最低 effort 设置下，Opus 5 通过的任务数量也多于任何其他模型。
• 在计算机使用基准测试 OSWorld 2.0 上，Opus 5 在任意既定成本下均优于其他所有模型，并以略高于 Fable 5 最佳结果三分之一的成本超越了该结果。

它在多项相关评测中也是我们性能最佳、成本效益最高的模型：

对于科学研究而言，Opus 5 相比 Opus 4.8 实现了显著提升。在我们所有生命科学评测中——涵盖结构生物学、有机化学和生物信息学等主题——它的表现均优于 Opus 4.8。其提升在有机化学任务上尤为显著，例如根据光谱数据推断分子结构（在我们的内部基准上，得分比 Opus 4.8 高 10.2 个百分点）；在蛋白质相关任务上同样明显，例如预测蛋白质序列变异如何影响其功能（此项得分高 7.7 个百分点）。

最后，Opus 5 还能生成强得多的视觉输出：

使用 Claude Opus 5

Claude Opus 5 在验证自身工作、仔细迭代直至成功方面能力显著增强。在评测及早期访问测试中，我们和用户发现了许多体现 Opus 5 主动性与严谨性的案例：

• 在一项 Frontier-Bench 任务中，Opus 5 获得了一张机器零件的图纸，并被要求编写代码，将其重建为 3D FreeCAD 模型。不过，该任务故意没有为模型提供直接 查看 图纸的方式。Opus 5 的回应是自行编写计算机视觉流水线，从原始像素中提取几何信息，随后重建完整机器零件。它多次成功完成了这一任务；在相同设置下，其他竞争模型经过五次尝试后仍无法解决。
• 面对一个流行开源包管理器中的真实 Bug，Opus 5 找到了根本原因，并修复了社区补丁遗漏的一个边缘情况。某竞争模型仅修复了表面症状，而非底层原因，随后便报告该 Bug 已解决。
• 一家交易公司的工程师使用 Opus 5，在一次会话中为一家新交易所构建了市场数据源。即使工程师提供了详尽计划，先前模型也完全无法完成该任务。由于找不到可用于验证的实时数据源，Opus 5 甚至自行构建了测试工具，以检查其代码能否正确解析交易所的数据。

以下是我们的早期访问客户进一步分享的 Opus 5 使用体验：

在 FrontierCode 1.1 上，Claude Opus 5 以一半成本实现了接近 Fable 级别的性能。在 Devin 内部，它在高难度调试和根因分析任务上也表现出特别的优势。

Claude Opus 5 以 Opus 的速度与成本，提供接近 Fable 5 的智能水平。在 CursorBench 上，它仅略低于 Fable 5，并具备许多相同的行为特征。我们很期待看到开发者如何在 Cursor 中使用它。

Claude Opus 5 登顶 Zapier 的 AutomationBench 排行榜，所消耗的 Token 并未多于此前的 Claude 模型。它接收了一个原始账户健康度工作簿，并端到端执行了完整的流失预防流程：标记存在风险的账户、提醒合适的负责人，并为客户留存运营团队生成摘要。此前模型均未能通过；Opus 5 达到了 100%。

在我们的基因组学分析工作中，Claude Opus 5 的行为比我们运行过的任何模型都更像一位严谨的科学家。它会选择恰当的统计检验来排除混杂因素，通过独立方法交叉核验自己的结果，并能在漫长的多步骤分析中始终保持方向正确。

在我们的内部评测中，Claude Opus 5 超过了其家族中的每一款模型。它不只是在最困难的 Agent 式编程任务上表现更好，比 Opus 4.7 提升 22%；它也更稳定，每次运行之间的方差低得多。对于 Lovable 上数百万构建者而言，这种一致性就是关键所在：一次又一次构建，都能获得可靠结果。

Claude Opus 5 是 Opus 系列自 4.5 以来最大的一次跃升。在相同的全栈应用构建任务中，前端首先展现了这种提升：它带来了我们在 Opus 模型中见过的最佳动画、游戏和 3D 作品。

我们非常喜欢 Claude Opus 5。对于我们的 Agent 所处理的开放式分析工作，它相较 Opus 4.8 是严格意义上的升级，而增益最大的恰恰是最重要的部分：更困难、更模糊的任务。回复更清晰、更简洁，我们还观察到它在较高 effort 水平下的效率有所提升。

对于分析师每天运行的金融研究工作流，Claude Opus 5 相较 Opus 4.8 有显著提升。它在数值推理、表格处理以及精度至关重要场景中的批判性思维方面尤为突出。

Claude Opus 5 提供了分析专业企业内容所必需的行业智能与准确性。Box 发现，Opus 5 的性能比 Opus 4.8 高 8%，并在科技、医疗保健和公共部门组织日常依赖的数据分析工作流（提升 11%）与尽职调查工作流（提升 17%）中实现了显著性能增益。

Claude Opus 5 相对 Opus 4.8 是一次明确的代际升级。一个周末里，我让它担任开发环境的办公厅主任：它构建了自己的监控器，驱动每一台机器，只有需要判断决策时才会把我拉进来。

Claude Opus 5 对我们的 Fundamental Research Assistant 代码库进行了大规模修改，在 Agent 工作流中持续适应反馈，且比我们使用过的任何模型都更清晰地解释其推理过程。它能够处理原本我们通常会拆分成许多更小部分的工作。

在我们一些最困难的金融建模任务中，Claude Opus 5 在准确性和效率上均明显优于 Opus 4.8。它的性能下限显著更高，尤其是在复杂金融领域逻辑中。跨不同 effort 水平，其平均准确率高出 9 个百分点，同时回合数和工具调用次数减少三分之一，耗时降低 60%。

Claude Opus 5 会像真正的前端开发者那样检查自己的工作。在我们的基准中，它以桌面和手机宽度在浏览器中打开页面，发现一个产品被隐藏在移动端首屏下方，以及一个结账按钮位于屏幕外；它在交付工作之前修复了这两个问题。

与此前 Opus 模型相比，Claude Opus 5 在法律 Agent 工作上的性能有明显提升，我们在公司治理和仲裁等业务领域看到了最大增益。Opus 5 在较低推理等级下维持质量的能力也令我们印象深刻：与 Opus 4.8 的最高推理设置相比，它在取得相近性能的同时，平均生成的 Token 少了 26%。

Claude Opus 5 对我们最大的增益体现在更长周期的工作上：构建一整套演示文稿，然后再进行修订。交付物质量决定了我们最终部署哪一款模型，而这正是我们看到过最明确的一次提升——视觉理解更好、格式更整洁、幻灯片问题更少。

Claude Opus 5 最突出的特质是判断力。接手一个 PR 时，它不会急于发布：它会验证分支、检查模板，并思考测试影响，确保交接干净利落。旧模型往往会抢先行动，继而在我们的检查环节中受阻。

在一次架构重构会话中，Claude Opus 5 对我提出的设计方案提出了异议；即使我坚持己见，它也没有让步。相反，它准确解释了我方案中有价值的部分，将反对意见收窄到一个设计问题，并提出了兼顾方案优点、同时修复缺陷的折中方案。这种判断力让我们可以在更少监督下信任它。

在首轮修订意见测试中，Claude Opus 5 的得分高于我们测试过的任何模型，几乎是 Opus 4.8 的两倍。它的评论能力也更好：在 NDA 上，它以更少轮次、更短时间完成修订，同时保持或提高准确性。

Claude Opus 5 编写的 diff 干净、精炼，没有死代码；在发现细微且与特定代码库相关的风险问题方面，它也更强。我们正将其用于生产工作负载。

我们肯定会在统一 Agent 平台 Cosmos 中迁移一批使用场景。我们期待越来越多地使用 Claude Opus 5 进行代码审查；我有信心地说，我们宁愿用户使用 Opus 5，而不是 Opus 4.8。

Claude Opus 5 最突出的地方是判断力。它在写下第一行代码前会进行更深入的思考，在规划阶段便能发现自身的逻辑缺陷，而不是事后才发现；它会推理一个答案为何正确，而不仅是它是否可用。这是我们所见 Claude 模型代际之间问题解决能力最明显的一次跃升，我们期待看到它被应用于 JetBrains IDE。

Claude Opus 5 是我们在交易基准上测试过的最强 Opus 模型；它达到这一水平时，使用的推理 Token 约为 Opus 4.8 的七分之一，延迟不足后者的一半。以极少的计算资源，获得更好的答案。

Claude Opus 5 使监控 Agent 能够在生产环境中管理部分自身记忆，从而在更长时间跨度内变得更自主、更可靠。该 Agent 将其上下文视为一份动态文档：在标记我们某项服务中的潜在异常后，它在生产环境中复核了自己的假设，发现该信号是良性的，将修正结果写入自身记忆，并自行停止了监控查询。

Claude Opus 5 是一款强大的 Agent 式编程模型，专为长时间运行、多步骤工作而构建。它能够深入理解你的代码库，在复杂任务中保持上下文主线，并且比 Opus 4.8 更有效地确定功能开发和 Bug 修复的需求。开发者现在可在 Kiro 中使用 Opus 5，利用其高级能力处理雄心勃勃的项目。

对齐与安全

对齐。 在部署前测试期间，我们的自动化行为审计发现，Opus 5 是迄今对齐度最高的模型（如下图所示）。与 Opus 4.8、Sonnet 5 或 Fable 5 相比，它更好地遵循 Claude 宪法，展现出最低的欺骗行为发生率，也最不容易被诱导滥用。在避免可能造成难以逆转副作用的鲁莽行为方面，它也是我们迄今最安全的模型。

在我们的自动化行为审计中，Opus 5 的总体失配行为得分为 2.3，是近期模型中的最低值。

安全。 Opus 5 并未在高风险双用途能力上推进前沿。在与私营部门及政府合作伙伴共同开展的严格评测中，我们发现它在生物学研究和进攻性网络安全两方面仍落后于 Mythos 5。有关这些评测的更多信息可见我们的 系统卡。

与前代 Opus 4.8 一样，我们有意避免使用网络安全任务来训练 Opus 5。不过，由于通用能力增强，该模型在此类任务上的表现仍有实质提升；在 发现 网络安全漏洞方面，它已接近 Mythos 5。然而，在 利用 这些漏洞——即将漏洞转化为实质性网络威胁——方面，它仍明显落后于 Mythos 5。

这一点体现在 Opus 5 在 OSS-Fuzz 上的表现。OSS-Fuzz 是我们开发的一项评测，用于评估模型在无需大量人工指导的情况下发现并利用漏洞的能力。尽管 Mythos 5 和 Opus 5 在识别漏洞方面的成功率相近，但 Opus 5 在开发漏洞利用程序方面的得分远低于 Mythos 5。

在我们的网络安全评测之一 OSS-Fuzz 上，Opus 5 在识别软件漏洞（左）方面接近 Mythos 5，但在为这些漏洞开发利用程序（右）方面的成功率低得多。

Opus 5 的安全护栏

Claude Opus 5 的安全护栏旨在支持该模型在网络安全和生物学领域的有益使用。它们与我们应用于 Opus 4.8 的护栏类似，但在一小部分网络安全任务上采用了更严格的限制措施。

网络安全。 Opus 5 的网络安全分类器在比例上较少……

Claude Opus 5 is available today. It’s a thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price.

On coding and knowledge work evaluations like Frontier-Bench and GDPval-AA, Opus 5 is the new state-of-the-art, though it remains behind Mythos 5 on cybersecurity tasks.

Opus 5 is designed to be used every day: it works more efficiently than other models. It’s the new default model on Claude Max, and the strongest model on Claude Pro.

Performance and cost-effectiveness

Claude Opus 5 provides greatly improved performance for the same cost as its predecessor, Opus 4.8. The charts in this section show how performance changes according to the model’s effort setting, which customers can use to optimize for intelligence or conserve tokens for faster and cheaper results.

Opus 5 excels on valuable software engineering tasks. For example, on Frontier-Bench v0.1, Opus 5 surpasses all other models, and more than doubles Opus 4.8’s performance at a lower cost per task. On CursorBench 3.2 , at max effort, the model performs within 0.5% of Fable 5’s peak score, but at half the cost per task; it also achieves greater performance at a given cost than all other models on high, xhigh, and max effort.

We see similar results on knowledge work and problem-solving tasks. For example:

• On ARC-AGI 3 , an evaluation where the model has to solve novel problems, Opus 5’s score is three times as high as the next-best model.
• On Zapier AutomationBench , which measures whether models can complete business tasks from start to finish, Opus 5’s pass rate is around 1.5× the next-best model for the same cost per task. Even at its lowest effort setting, Opus 5 passes more tasks than any other model.
• On OSWorld 2.0 , a computer use benchmark, Opus 5 outperforms every other model at any given cost, surpassing Fable 5’s best result at just over a third of the cost.

It’s also our best and most cost-efficient model on several related evaluations:

Opus 5 is a meaningful improvement over Opus 4.8 for scientific research. It shows better performance than Opus 4.8 on every one of our life sciences evaluations, which cover topics including structural biology, organic chemistry, and bioinformatics. Its improvements are most notable on organic chemistry tasks, like inferring molecular structures from spectroscopy data (it scores 10.2 percentage points higher than Opus 4.8 on our internal benchmark), and on protein-related tasks like predicting how variations in a protein’s sequence affect how it functions (here, it scores 7.7 percentage points higher).

Finally, Opus 5 is capable of producing much stronger visual outputs:

Working with Claude Opus 5

Claude Opus 5 is much stronger at verifying its work and iterating carefully until it succeeds. In evaluations and early-access testing, we and our users found many examples of Opus 5’s agency and thoroughness:

• On one Frontier-Bench task, Opus 5 was given a drawing of a machine part and asked to write code to rebuild it as a 3D FreeCAD model. However, in this task, the model was intentionally given no way to directly view the drawing. Opus 5 responded by writing its own computer vision pipeline to pull the geometry from the raw pixels, then reconstructed the full machine part. It succeeded in doing so repeatedly; no competing model with the same setup could solve it after five attempts.
• Given a real bug in a popular open-source package manager, Opus 5 found the root cause and fixed an edge case that the community’s patch had missed. A competing model fixed only the surface symptom (not the underlying cause), then reported the bug resolved.
• An engineer at a trading firm used Opus 5 to build a market data feed for a new exchange in a single session. Previous models could not complete this task at all, even given extensive plans from the engineer. Finding no live feed to validate against, Opus 5 even built its own test harness to check that its code parsed the exchange’s data correctly.

Below are further reports from our early-access customers on their experience of working with Opus 5:

On FrontierCode 1.1, Claude Opus 5 approaches Fable-level performance at half the cost. Within Devin, it also shows particular strength on difficult debugging and root-cause analysis tasks.

Claude Opus 5 delivers near Fable 5 intelligence at Opus speed and cost. On CursorBench it’s just under Fable 5 and has many of the same behaviors. We are excited to see how developers use it in Cursor.

Claude Opus 5 topped Zapier’s AutomationBench leaderboard without spending more tokens than prior Claude models. It took a raw account-health workbook and ran a full churn-prevention sequence end to end: flagging at-risk accounts, alerting the right owner, and summarizing for retention ops. Previous models didn’t pass; Opus 5 hit 100%.

On our genomics analysis work, Claude Opus 5 behaves more like a careful scientist than any model we’ve run. It reaches for the right statistical tests to rule out confounders, cross-checks its own results by independent methods, and stays on track through long multi-step analyses.

Claude Opus 5 came out ahead of every model in its family on our internal evals. It isn’t just better on our hardest agentic coding tasks, up 22% over Opus 4.7, it’s steadier, with far less variance run to run. For the millions of builders on Lovable, that consistency is the whole game. Reliable results, build after build.

Claude Opus 5 is the biggest leap in the Opus family since 4.5. On the same full-stack app builds, the front end shows it first: the best animations, games, and 3D work we have seen from an Opus model.

We’re loving Claude Opus 5. For the kind of open-ended analytical work our agent handles, it’s a strict upgrade over Opus 4.8, and the gains are biggest exactly where it matters: the harder, vaguer tasks. Responses are clearer and more concise, and we see improved efficiency at higher effort levels too.

Claude Opus 5 is a striking improvement over Opus 4.8 for the financial research workflows our analysts run every day. It stands out on numerical reasoning, table work, and sharper critical thinking where precision matters.

Claude Opus 5 delivers the industry intelligence and accuracy that is essential for the analysis of specialized enterprise content. Box found that Opus 5 outperforms Opus 4.8 by 8% and delivers notable performance gains in the data analysis (11% improvement) and due diligence (17% improvement) workflows that technology, healthcare, and public sector organizations rely on daily.

Claude Opus 5 is a clear generational step up from Opus 4.8. Over one weekend I gave it a chief-of-staff role over my dev environments: it built its own monitor, drove each box, and pulled me in only for the judgment calls.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant codebase, adapting to feedback throughout an agentic workflow and explaining its reasoning more clearly than any model we’ve used. It handled work we would normally have broken into much smaller pieces.

On some of our hardest financial-modeling tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both accuracy and efficiency. Its performance floor is materially higher, especially on deep finance domain logic. Across effort levels it averaged 9 percentage points higher accuracy with a third fewer turns and tool calls and 60% less time.

Claude Opus 5 checks its own work the way a real frontend developer would. On our benchmark it opened its pages in a browser at desktop and phone widths, caught a product hidden below the mobile fold and an off-screen checkout button, and fixed both before handing the work back.

Claude Opus 5 is a clear step up in performance on legal agent work compared to prior Opus models, and we saw the biggest gains in practice areas like corporate governance and arbitration. We were also impressed with Opus 5’s ability to maintain quality at lower reasoning levels, achieving similar performance while generating 26% fewer tokens on average compared to Opus 4.8 at max reasoning.

Claude Opus 5’s biggest gains for us are on longer-horizon work: building a full deck, then revising it. Artifact quality is what decides which model we ship, and this is the clearest step up we’ve seen — better visual understanding, cleaner formatting, fewer slide issues.

Claude Opus 5’s judgment is what stands out. Handing off a PR, it doesn’t rush to publish: it verifies the branches, checks the template, and thinks through test implications so the handoff is clean. The older models tended to jump ahead and get caught on our checks.

During a rearchitecting session, Claude Opus 5 pushed back on a design I proposed, and it didn’t fold when I insisted. Instead, it explained exactly what was valuable in my idea, narrowed its objection to a single design question, and proposed a compromise that kept the good part while fixing the flaw. That’s the kind of judgment that lets us trust it with less oversight.

On first-turn redlines, Claude Opus 5 scored the highest of any model we tested, nearly double Opus 4.8. Commenting is better too: on NDAs it gets to the redline in less time and with fewer passes, with accuracy maintained or better.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger hazard spotter on subtle, codebase-specific issues. We’re adopting it for production workloads.

We will definitely migrate a number of use cases in Cosmos, our unified agent platform. We’re looking forward to increasingly using Claude Opus 5 for code review, and I am confident in saying we would rather people be using Opus 5 than Opus 4.8.

What stands out about Claude Opus 5 is judgment. It thinks harder before it writes a single line, catches its own logical faults during planning rather than after the fact, and reasons about why an answer is right, not just whether it works. It’s the clearest jump in problem-solving we’ve seen from one Claude model to the next, and we’re looking forward to seeing it adopted in JetBrains IDEs.

Claude Opus 5 is the strongest Opus model we’ve tested on our trading benchmark, and it gets there using roughly a seventh of the reasoning tokens and under half the latency of Opus 4.8. Better answers at a fraction of the compute.

Claude Opus 5 lets monitoring agents manage parts of their own memory in production, making them more autonomous and reliable over longer horizons. The agent treats its context as a living document: after flagging a potential anomaly in one of our services, it re-checked its own assumption against production, found the signal was benign, wrote the correction into its memory, and retired its monitoring queries on its own.

Claude Opus 5 is a strong agentic coding model built for long-running, multi-step work. It deeply understands your codebase, holds the thread across complex tasks, and pins down requirements for feature development and bug-fixing more effectively than Opus 4.8. Developers can now build with Opus 5 in Kiro, accessing its advanced capabilities to tackle ambitious projects.

Alignment and safety

Alignment. During pre-deployment testing, our automated behavioral audit found Opus 5 to be our most aligned model to date (as shown in the graph below). It adheres to Claude’s Constitution better than Opus 4.8, Sonnet 5, or Fable 5; exhibits the lowest rates of deceptive behavior; and is the least susceptible to being tricked into misuse. It’s also our safest model yet in terms of avoiding reckless actions that could have hard-to-reverse side effects.

On our automated behavioral audit, Opus 5 scores 2.3 on overall misaligned behavior, the lowest of our recent models.

Safety. Opus 5 does not advance the frontier in risky, dual-use capabilities. In rigorous evaluations conducted alongside private-sector and government partners, we found it remains behind Mythos 5 in both biology research and offensive cybersecurity. More information about these evaluations can be found in our System Card.

As with its predecessor, Opus 4.8, we’ve intentionally avoided training Opus 5 on cyber tasks. The model has nevertheless improved substantially on these tasks as a result of becoming more generally capable, and it comes close to Mythos 5 at finding cybersecurity vulnerabilities. However, it remains substantially behind Mythos 5 on the exploitation of those vulnerabilities—that is, in turning vulnerabilities into material cyber threats.

This is illustrated by Opus 5’s performance on OSS-Fuzz, an evaluation we’ve developed to assess how well models can find and then exploit vulnerabilities without extensive human guidance. Although Mythos 5 and Opus 5 identify vulnerabilities with similar success, Opus 5’s score on the development of exploits is far behind that of Mythos 5.

On OSS-Fuzz, one of our cybersecurity evaluations, Opus 5 is close to Mythos 5 at identifying software vulnerabilities (left), but is considerably less successful at developing exploits for them (right).

Safeguards for Opus 5

Claude Opus 5’s safeguards are designed to allow beneficial uses of the model in both cybersecurity and biology. They are similar to those we applied to Opus 4.8, with the exception of some stronger guardrails on a narrow range of cyber tasks.

Cybersecurity. Opus 5’s cyber classifiers are proportionally less restrictive than those on Fable 5. They allow Opus 5 to find vulnerabilities in source code, but block “binary-based” vulnerability scanning (a method more likely to be associated with malicious actors), penetration testing, and exploit generation.

Based on our testing, we expect the classifiers to intervene around 85% less often than they do for Fable 5. In Claude.ai, Claude Code, and Claude Cowork, any flagged requests will fall back to Opus 4.8 by default. Fallbacks to Opus 4.8 can also be enabled on the API.

Our Cyber Verification Program (CVP) facilitates cybersecurity work that would otherwise be impeded by the model’s safeguards. Enterprises and researchers who are already part of the CVP have immediate access to a version of Opus 5 with fewer security restrictions.

Biology. Since Opus 5 has a similar suite of safeguards to Opus 4.8, it is now our most capable generally available model for scientific research. Nevertheless, the model still shows important limitations on long-running, autonomous research tasks, which is where we expect AI models to pose the most substantial biology-related risks. (Mythos 5 remains the stronger model for this type of biological work.) As part of this launch, biology-related requests that are blocked on Fable 5 will now route to Opus 5 rather than Opus 4.8.

Getting started

Claude Opus 5 is available today on all platforms, priced at $5 per million input tokens and $25 per million output tokens (the same as Opus 4.8). Developers can get started with claude-opus-5 on the Claude API.

It’s also offered in Fast mode, where it runs around 2.5 times the default speed. As with Opus 4.8, Fast mode is available at twice Opus 5’s base price on the Claude Platform and through usage credits in Claude Code.

Alongside Opus 5, we’re releasing two updates in beta:

• Mid-conversation tool changes on the Claude Platform. Within a conversation, developers can now change which tools Claude can use without invalidating the prompt cache.
• Automatic fallbacks on the API. Users can now choose to have requests that are flagged by our safety classifiers on Opus 5 (or Fable 5) automatically route to another model. With automatic fallbacks on, API requests always route to the best available model by default rather than being blocked.

Consistent with prior Opus models, Opus 5 does not have data retention requirements for general access.

For more guidance on how to get the best out of Opus 5, see our prompting guide.