Claude Opus 5.5 is real and was released on September 22, 2026. Anthropic positions it for long-running agentic coding and knowledge work: tasks where a model needs to work through multiple steps, use tools, and keep a large body of context in view. Its headline specifications are a 1 million-token context window, up to 128,000 output tokens, always-on adaptive thinking, and API list pricing of $4 per million input tokens and $20 per million output tokens. Those specifications describe capacity and price; they do not guarantee that a task will be correct, fast, or cheaper in practice.
The useful question is not simply whether Opus 5.5 is “better.” It is whether it produces a more reliable finished result for your workload at an acceptable cost and response time. This guide summarizes what Anthropic has published, what changes for developers coming from Opus 5, and how to evaluate the model before switching a production workflow.
Claude Opus 5.5 is Anthropic’s newest Opus model as of September 22, 2026. In Anthropic’s documentation, it is described as a model for long-running agentic coding and knowledge work. “Agentic” means that a system can take actions through tools, such as reading files, running code, or calling services, rather than only returning a one-shot text answer. The model is available through the Claude API and is listed for Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. Availability in an API or cloud service does not by itself establish which consumer subscription plans include it; check the model selector and current limits in the product you use.
For the official model identifier, platform list, current specifications, and status, see Anthropic’s Claude Opus 5.5 model page. It lists the model as active and latest, with a release date of September 22, 2026. Product and API details can change, so use that page as the reference before changing a deployment.
| Published detail | What it means for a user or developer |
|---|---|
| 1M-token context window | A large amount of input can fit in one request or conversation, subject to the platform’s limits and actual tokenization. |
| 128K maximum output | The response can be long, but the limit includes generated content and may be constrained by request settings. |
| Text and image input; text output | The model can work with text and images, then respond in text. The published overview does not list image generation as an output. |
| Adaptive thinking, always on | Thinking cannot be turned off on Opus 5.5; the effort setting is the documented control for reasoning depth. |
| Default effort: medium | Anthropic’s suggested starting point is medium. Adjust settings only after checking quality, latency, and token use on your own tasks. |
| API list price: $4 input / $20 output per million tokens | Input and output are billed at different rates. Thinking tokens count as output; cache writes, cache reads, batch use, or special modes have separate pricing. |
| Reliable knowledge cutoff: June 2026 | For later events or changing facts, provide current source material or a suitable retrieval tool and verify the cited evidence. |
Anthropic also lists up to 300,000 output tokens for the Message Batches API in beta, with a required beta header. That is a specific batch feature, not the standard maximum for an ordinary request. The same model page lists a 512-token minimum cacheable prompt length and separate cache-write rates. Check the API documentation before assuming those features are enabled in every integration.
A large context window can be useful when a coding agent needs project instructions, several related files, tool results, and an evolving plan available together. Opus 5.5 is specifically positioned for long-running agentic coding. That makes it a candidate for multi-file changes, debugging across modules, or a task that requires several tool calls. It does not mean the model will inspect every relevant file automatically. Give it the repository boundaries, the requested outcome, constraints, and a way to check its work, such as tests or a build command.
Judge the result by observable evidence: Did the requested behavior change? Did the relevant tests pass? Were files outside the intended scope modified? Did the agent leave placeholders, skip a failing test, or claim success without showing the check? For a production codebase, a clean patch and reproducible tests matter more than a confident explanation.
The context capacity may help when a task involves a long report, a set of related documents, or a research packet. It can support synthesis and comparison, but a larger context is not a substitute for source tracking. Ask for claims to be tied to specific passages or documents, then check those passages yourself when the decision matters. If the material exceeds the platform’s request limit, contains scanned pages, or has conflicting versions, divide the work into labeled sections and preserve source names and dates.
The official model specification lists text and images as input and text as output. That may suit tasks such as asking questions about a chart, screenshot, or photographed document. It should not be interpreted as a promise of image generation or perfect visual interpretation. For charts and interface screenshots, ask the model to state what it can read and what is uncertain, and compare numerical details against the source. If a decision depends on tiny labels or exact coordinates, use a human or a purpose-built visual tool to verify them.
Anthropic’s published API list price is $4 per million input tokens and $20 per million output tokens. Cache reads are listed at $0.20 per million tokens, while cache writes have separate prices; batch processing and fast mode also have their own terms. Thinking tokens are billed as output tokens. Therefore, the output price alone does not tell you the total cost of a coding or agent run.
Opus 5’s published API rates are $5 per million input tokens and $25 per million output tokens, so the standard input and output rates for Opus 5.5 are each 20% lower. That is a per-token comparison, not a promise that every completed task will cost 20% less. Opus 5.5 always uses adaptive thinking, and different prompts, tool loops, cache behavior, or retries can change how many tokens a task consumes. Anthropic’s own cost explainer for Opus 5.5 emphasizes that task costs depend on the work and the number of turns, not just the posted token rate.
For a fair comparison, measure cost per completed task, not only cost per million tokens. Include failed attempts and human review in your estimate. A model that costs less per token can still be the more expensive choice if it needs more retries; a pricier setting may be worthwhile if it prevents repeated work on a high-value task.
Developers should not assume an integration that worked on Opus 5 will behave identically on Opus 5.5. Anthropic’s model page identifies several compatibility changes. Before changing the model ID in a live system, test them in a staging environment:
computer_20251124 tool is not accepted on the Claude API and Google Cloud for Opus 5.5. Confirm the current tool version for the platform you deploy to.These are not just naming changes. They can affect error handling, streaming, tool loops, and the way an application stores and resubmits conversation blocks. The official Opus 5.5 documentation summarizes the changes and links to the detailed migration material.
Anthropic recommends choosing a model by balancing capability, speed, and cost, then testing it on the actual application. A practical evaluation does not need a broad leaderboard. Use a small, representative set of your own tasks and compare Opus 5.5 with the model you use today under the same inputs and tool access.
If Opus 5.5 consistently completes demanding, multi-step tasks with fewer corrections and the added cost fits your budget, keeping it for those tasks may make sense. If your work is short, repetitive, or latency-sensitive, compare it with a faster or lower-priced option in Anthropic’s current model lineup. Anthropic’s model selection guide recommends evaluating with real prompts and data, then weighing accuracy, response quality, edge cases, speed, and cost.
A one-million-token context window is a capacity limit, not a guarantee that the model will notice every detail in a large prompt. Important requirements should still be explicit, and critical facts should be checked against their source. The published reliable knowledge cutoff is June 2026, so information after that date needs current retrieval or user-provided material.
Likewise, always-on thinking does not remove the need for tests or review. The model can produce a plausible but mistaken answer, use a tool incorrectly, misunderstand an image, or overlook a requirement. Tool permissions also matter: only grant access needed for the task, and require approval for consequential actions such as deploying code, changing customer data, or sending external messages.
Finally, availability and pricing are time-sensitive. The values here were checked against Anthropic’s documentation on September 22, 2026. Cloud providers may have their own rollout timing, region limits, billing details, or deployment names. Before buying capacity or migrating an application, verify the exact model ID and terms with the provider you plan to use.
Specifications and prices checked September 22, 2026. Pricing, supported model IDs, and provider availability can change; confirm them before deployment or purchase.
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Claude Opus 5.5 launched September 22, 2026. Review its context window, pricing, adaptive thinking, API changes, availability, and how to test its value for your work.
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