Plain-English Explainer
What is Claude Opus 5.5? Anthropic's starting-point model explained
The model Anthropic tells you to try first, and what that actually means for your Tuesday.
You saw the name Claude Opus 5.5 and want a straight answer. This page covers what it is, who makes it, when it came out, what it is built for, how it compares with Fable 5.1, Sonnet 5.5, and Haiku 5.5, and which practical prompts a working professional can run on it today.
Claude Opus 5.5 is a large language model made by Anthropic and released on September 22, 2026. It is the first model in Anthropic's Claude 5.5 family and is built for long-running agentic coding and knowledge work. It has a 1M token context window, accepts text and image input, produces text output, and uses the API model ID claude-opus-5-5. Anthropic recommends starting with it for most workloads.
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Free lesson · no account neededMeet ClaudeWhy Claude?6 minStart the free lessonKey Takeaways
- Claude Opus 5.5 is Anthropic's model for long-running agentic coding and knowledge work, released September 22, 2026 as the first model in the Claude 5.5 family.
- Anthropic says it performs at the level of Claude Fable 5.1 on most work and generates output more than 30% faster than Claude Opus 5.
- Specs: 1M token context window, 128K max output tokens, text and image in, text out, always-on adaptive thinking, and a reliable knowledge cutoff of June 2026.
- Anthropic's own guidance: if you are unsure which model to use, start with Opus 5.5, and move to Fable 5.1 only if evals at higher effort still fall short.
- It is available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS, and will not be retired sooner than September 22, 2027.
Claude Opus 5.5 at a glance
| Attribute | Claude Opus 5.5 |
|---|---|
| Made by | Anthropic |
| Release date | September 22, 2026 |
| Model family | First model in the Claude 5.5 family |
| What Anthropic says it is built for | Long-running agentic coding and knowledge work |
| Context window | 1M tokens |
| Maximum output | 128K tokens |
| Input and output | Text and image input, text output |
| Thinking | Adaptive thinking, always on and cannot be turned off; depth is controlled with the effort setting, which defaults to medium |
| Speed | Moderate latency in Anthropic's lineup; output more than 30% faster than Claude Opus 5 |
| Reliable knowledge cutoff | June 2026 |
| API model ID | claude-opus-5-5 |
| Where it is available | Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS |
| Earliest retirement date | Not sooner than September 22, 2027 |
Copy and Paste
Prompts You Can Use Today
Turn a messy thread into a decision memo
You are a chief of staff writing for a VP who has two minutes. Below is a 40-message Slack thread about whether to delay our Q4 launch. Write a one-page decision memo: the recommendation in the first sentence, then the three strongest arguments on each side, the open risks, and who needs to decide by when. Plain language, no jargon. Thread: [paste thread]
Swap in your own audience and decision. Opus 5.5 tends to put the important information first, so keep that instruction explicit anyway.
Audit a codebase for one specific class of problem
You are a senior backend engineer reviewing this repository for places where user input reaches a SQL query without parameterization. Search every file, list each finding with the file path, line number, and a two-sentence explanation, then rank them by how easy they would be to exploit. Do not change any code. Report only what you can point to in the files.
Replace the problem class with whatever you are auditing for, such as deprecated API calls, missing null checks, or hard-coded secrets. The 'do not change any code' line keeps the job read-only.
Plan a framework migration in safe steps
We are migrating this service from Express to Fastify. Read the routes, middleware, and tests, then propose a migration plan in ordered steps where the test suite passes after each step. For each step name the files touched, the risk, and how to roll it back. Stop and ask me before making any change that cannot be undone.
Name your actual source and target frameworks. The explicit stop-and-ask line matters on long jobs, where you want checkpoints rather than surprises.
Analyze a spreadsheet export and flag what looks wrong
Attached is a CSV export of our last 12 months of support tickets with columns for date, category, priority, resolution time, and agent. Summarize the three biggest trends, flag any rows or columns that look like data-entry errors, and tell me which conclusions you are confident about versus which would need more data. Present the summary as a short table followed by a paragraph.
Works equally well with an Excel sheet saved as CSV. Always ask for the confident-versus-uncertain split so you know what to verify.
Rewrite dense writing for a different reader
Rewrite the following technical incident report for our customers, who are non-technical operations managers. Lead with what happened and whether it is fixed, then what we are doing to prevent it. Keep it under 250 words, use no acronyms without explaining them, and keep every factual claim from the original unchanged. Report: [paste report]
Change the audience, the length, and the tone. Add 'list any claim you were unsure about' if the source is sensitive.
Review a contract clause with a clear boundary
I am a procurement manager, not a lawyer. Read this vendor agreement and list every clause that affects termination, liability caps, auto-renewal, or data handling. For each, explain it in one plain sentence and note anything unusual compared with typical commercial software agreements. Mark clearly that this is a reading aid and not legal advice.
Use it to prepare better questions for your legal team, not to replace them.
Set up a long-running research task with checkpoints
Research how three competing project-management tools handle permissions for external guests. Work through the vendors' public documentation one at a time. After each vendor, give me a short summary with the exact page you used. At the end, build a comparison table and list anything you could not confirm from documentation.
Swap in your own vendors and question. If your setup has no web access, paste the documentation text into the prompt instead.
What it does well
What Claude Opus 5.5 is built for
Anthropic describes Opus 5.5 as a model for long-running agentic coding and knowledge work. Here is what that phrase covers in practice.
Agentic coding
Agentic means the model works through a multi-step task, reading files, running tools, and checking its own output, rather than answering a single question. Opus 5.5 is aimed at this kind of sustained engineering work.
Sprawling, long jobs
Anthropic says Opus 5.5 is particularly good at long and sprawling jobs like codebase-wide migrations and audits, where the work touches many files and must stay consistent from start to finish.
Knowledge work
Analysis, research, drafting, and review are the everyday tasks of analysts, managers, and operators. Opus 5.5 is positioned for this work, not only for software engineers.
Clearer writing
Anthropic says early testers found its writing clearer and easier to follow, and that it puts the most important information up front. That helps when the output is a memo or summary someone else must read quickly.
Large inputs
A 1M token context window lets you give it very large amounts of material in one session, such as a long report set, a large repository, or a stack of documents, with up to 128K tokens of output.
Careful behavior
Anthropic says it is much less likely than recent models to take hard-to-reverse actions or act outside the boundaries it has been given. That matters when a model is allowed to act on your behalf.
Plain-English definition
The name, decoded
Claude is the name of Anthropic's family of AI models. Opus is the tier name Anthropic has used for its larger, more capable models, and 5.5 is the generation number. Anthropic calls Claude Opus 5.5 the first model in its new Claude 5.5 family, and it was released on September 22, 2026. Sonnet 5.5 and Haiku 5.5 followed it, as covered in the model comparison below.
A large language model is software trained on a very large amount of text so that it can read instructions and produce useful text in response. You talk to it in ordinary language. You do not need a special syntax. What changes from one model to the next is how well it follows complicated instructions, how long it can stay on task, how much it can hold in view at once, and how quickly it responds.
The name Opus 5.5 can sound like a product you buy, but it is a model, not a separate product. You reach it through the Claude apps, through developer tools such as Claude Code, or through the API using the model ID claude-opus-5-5. The same engine sits behind different surfaces, and which surfaces you can use depends on your plan and your setup.
What changed
What is different from Claude Opus 5
Anthropic describes Opus 5.5 as a major step up from Claude Opus 5. Three differences are worth knowing in plain words. First, speed: Anthropic says Opus 5.5 generates output more than 30% faster than Opus 5. For a short question you will barely notice. For a long job that produces pages of output or runs for many steps, waiting less adds up.
Second, communication. Anthropic says Opus 5.5 communicates more naturally than prior models, and that early testers found its writing clearer and easier to follow. It puts the most important information up front. If you have ever scrolled through a long AI answer hunting for the actual conclusion, this is the change aimed at you.
Third, behavior around risk. On Anthropic's automated behavioral audit, which is its alignment test suite, Opus 5.5 achieved the best scores of any model Anthropic had tested to date. Anthropic says it is more resistant than Opus 5 to prompt injection, which is when text hidden inside a web page or document tries to trick a model into following instructions you did not give. This matters most when the model reads untrusted content while doing a task for you.
Anthropic also says Opus 5.5 performs at the level of Claude Fable 5.1 on most work. That is the headline claim behind the recommendation to start with Opus 5.5, because it suggests you may not need the slower, top-of-lineup model for most of what you do. This page does not repeat benchmark numbers, and you should treat any claim about specific scores skeptically unless you can trace it to Anthropic's own pages.
The lineup
Where Opus 5.5 sits among the four current Claude models
These descriptions use Anthropic's own one-line positioning from its models overview.
Claude Fable 5.1
Anthropic positions it for demanding reasoning and long-horizon agentic work. It has the slowest latency of the four, which is the trade-off for being the model Anthropic points to when the hardest work falls short elsewhere.
Claude Opus 5.5
Positioned for long-running agentic coding and knowledge work, with moderate latency. Anthropic says to start here for most workloads if you are unsure.
Claude Sonnet 5.5
Anthropic calls it the best combination of speed and intelligence, with fast latency. It was released September 28, 2026, six days after Opus 5.5.
Claude Haiku 5.5
Positioned for high-volume, latency-sensitive tasks such as classification, extraction, and routing, and it has the fastest latency. It was released October 7, 2026.
Choosing a model
Which Claude model should you use?
Anthropic's guidance is direct: if you are unsure which model to use, start with Claude Opus 5.5 for most workloads. Move to Claude Fable 5.1 for demanding reasoning and long-horizon agentic work, or when evals on Opus 5.5 at higher effort still fall short. The second half of that sentence is the useful part. It tells you how to decide: run your real task on Opus 5.5, raise the effort setting if the result is thin, and only then consider Fable 5.1.
If you are a professional using Claude for documents, analysis, and planning, Opus 5.5 is a sensible default. Memos, reports, spreadsheet reviews, research summaries, and policy drafts all fall under knowledge work. If you are a developer or an analyst running multi-step jobs, such as reviewing a large repository or processing a long set of files, Opus 5.5 is also where Anthropic points you first.
If you run something at scale, such as sorting thousands of support emails into categories, pulling fields out of invoices, or routing requests to the right team, Haiku 5.5 is the model Anthropic positions for that kind of high-volume, latency-sensitive work. If you want a balance of speed and intelligence for everyday tasks and quick back-and-forth, Sonnet 5.5 is Anthropic's best-combination choice. If you hit a wall on a genuinely hard reasoning problem or a very long agentic run, Fable 5.1 is the escalation path.
A practical habit: pick one real task from your week, run it on two models, and compare. Judging a model on your own work tells you far more than any announcement, including this one.
Specs explained
What the numbers on the spec sheet mean for you
The 1M token context window is how much text the model can hold in view at once. A token is a chunk of text, roughly a short word or part of a longer one. A window of this size means you can place a very large body of material in front of the model, such as many documents or a big codebase, instead of feeding it in pieces. It does not mean every detail is weighted equally, so for important questions it still helps to tell the model which part of the material matters.
The 128K max output is the longest single response it can produce. That is long enough for substantial deliverables, such as a full draft report or a large batch of generated code, without being cut off. Input is text and images and output is text, so you can show it a chart, a screenshot, or a scanned page and ask questions about it, but it will answer in text rather than producing images.
Adaptive thinking is always on and cannot be turned off. In plain words, the model decides how much to reason before answering, and you steer that with the effort setting, which defaults to medium. For a quick lookup, medium is plenty. For a tricky analysis or a plan with many dependencies, raising the effort tells the model to think harder, and Anthropic's own advice about moving to Fable 5.1 refers to trying higher effort on Opus 5.5 first.
The reliable knowledge cutoff of June 2026 is the point up to which the model's built-in knowledge is dependable. Anything that happened after that, such as a new software release or a policy change, it may not know about unless you provide it. For current facts, paste the source text into the conversation or use a tool that can look things up.
Worked example
What a long, sprawling job looks like
Anthropic specifically says Opus 5.5 is particularly good at codebase-wide migrations and audits. Consider a concrete version: a team needs to find every place in a large repository where a deprecated library function is called, decide which calls can be swapped mechanically and which need human judgment, and then make the safe changes in small, reviewable pull requests.
This kind of job is hard for a model because the task does not fit in one answer. It requires reading many files, remembering the rules it set for itself early on, applying them consistently late in the job, and noticing when something does not fit the pattern. A long context window helps with the first part. Staying consistent over many steps is the quality Anthropic is describing when it calls the model built for long-running work.
The same shape appears outside engineering. A finance analyst reconciling a year of exports, a compliance lead checking a policy set against a new regulation, or an operations manager reviewing dozens of vendor contracts for renewal terms all face a sprawling job with a consistent rule to apply. The best prompts for these jobs state the rule, the output format, and what to do with anything ambiguous. Asking the model to report ambiguous items rather than guess is one of the most useful lines you can add.
For work that runs inside Claude Code, Anthropic also offers a fast mode for Opus 5.5, available in Claude Code and the Claude Platform with up to 2.5x speed. It is useful when you are iterating quickly and waiting is the main friction. It is an option, not a requirement, and the standard mode remains the baseline described in the spec table.
Safety and trust
What Anthropic says about how Opus 5.5 behaves
Capability is only half of what makes a model usable for real work. The other half is whether it stays inside the lines you draw. Anthropic says Opus 5.5 is much less likely than recent models to take hard-to-reverse actions or to act outside the boundaries it has been given. If you hand a model the ability to edit files or run commands, that is the behavior you want, and it is worth still setting your own limits explicitly in the prompt.
Anthropic also says Opus 5.5 is more resistant than Opus 5 to prompt injection. If your workflow has the model read emails, web pages, or uploaded documents from outside your organization, that is a real risk, and a more resistant model is a meaningful improvement. It is not a guarantee. Good practice still means keeping sensitive actions behind a human approval step.
Because Opus 5.5 is comparable to Claude Mythos 5.1 in biology and cybersecurity, Anthropic says it ships with safeguards similar to those on Claude Fable 5.1. For most professional users this changes nothing day to day. If you work in a security or life-sciences field, you may find that some borderline requests are handled more cautiously, and it is worth knowing why.
Anthropic also publishes the system prompt it uses on claude.ai and the Claude apps. That is a useful transparency practice: you can read the standing instructions the consumer apps give the model, which helps explain why it behaves as it does there compared with the raw API.
Access
Where you can use Claude Opus 5.5
Developers reach Opus 5.5 through the Claude API with the model ID claude-opus-5-5. Anthropic also lists availability on Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. If your company already standardizes on one of those cloud platforms, you can usually use the model within the account and security setup you already have.
Non-developers usually meet Claude through the Claude apps and through tools built on top of it. Which models appear in a model picker can depend on your plan and the app version, so check the picker in your own account rather than assuming. Claude Code, Anthropic's coding tool, is where the fast mode for Opus 5.5 is available.
On longevity, Anthropic says it will not retire Opus 5.5 sooner than September 22, 2027. If you are building a workflow or a product on a specific model, that is the commitment to plan around. Model IDs and retirement notices live in Anthropic's documentation, which is the place to check before you depend on anything.
Honest limits
What Opus 5.5 does not do for you
Opus 5.5 can still be wrong. A more capable model makes fewer errors, but its mistakes can sound just as confident as its correct answers. For anything with consequences, such as a contract, a financial figure, a medical question, or code headed to production, treat the output as a strong first draft and verify the claims that matter.
It does not know what happened after its June 2026 knowledge cutoff unless you tell it. It does not see your files unless you provide them or connect a tool that can read them. A common cause of disappointing answers is not model weakness but missing context: the model was never shown the document, the constraint, or the audience.
A large context window is not a substitute for a clear question. Pasting a thousand pages and asking 'thoughts?' produces a vague answer. State what you want, for whom, in what format, and what to do when the material is unclear.
Finally, the strongest model is not always the right one. For simple, high-volume tasks, the faster models in the lineup exist for good reason, and choosing them is not settling for less. Match the model to the job, and measure on your own work.
Learn to get real work out of Claude
Reading about Claude Opus 5.5 and being good at using it are different things. The free Nightschool AI curriculum is hands-on from the first lesson, so you practice prompts on tasks like the ones on this page. Start with a free lesson and build the habit.
Frequently Asked Questions
What is Claude Opus 5.5?
Claude Opus 5.5 is a large language model made by Anthropic, released on September 22, 2026. Anthropic calls it the first model in its new Claude 5.5 family and says it is built for long-running agentic coding and knowledge work. It has a 1M token context window, takes text and image input, and produces text output.
When was Claude Opus 5.5 released?
Anthropic released Claude Opus 5.5 on September 22, 2026. It was the first model in the Claude 5.5 family. Claude Sonnet 5.5 followed on September 28, 2026, and Claude Haiku 5.5 on October 7, 2026.
What is Claude Opus 5.5 best at?
Anthropic says it is built for long-running agentic coding and knowledge work, and that it is particularly good at long and sprawling jobs like codebase-wide migrations and audits. Anthropic also says its writing is clearer and easier to follow, with the most important information put up front.
How big is the Claude Opus 5.5 context window?
The context window is 1M tokens, and the maximum output is 128K tokens. A token is a chunk of text, roughly a short word or part of a longer one. That window lets you provide very large amounts of material, such as many documents or a big codebase, in one session.
Is Claude Opus 5.5 better than Claude Fable 5.1?
Anthropic says Opus 5.5 performs at the level of Claude Fable 5.1 on most work, while Fable 5.1 remains the model for demanding reasoning and long-horizon agentic work and has slower latency. Anthropic's advice is to start with Opus 5.5 and move to Fable 5.1 if evals at higher effort still fall short.
How is Opus 5.5 different from Claude Opus 5?
Anthropic calls Opus 5.5 a major step up from Claude Opus 5. It says Opus 5.5 generates output more than 30% faster, communicates more naturally with clearer writing, and is more resistant to prompt injection. It also achieved the best scores Anthropic had tested on its automated behavioral audit at the time of release.
Which Claude model should I use if I am not sure?
Anthropic's models overview says to start with Claude Opus 5.5 for most workloads if you are unsure. Choose Sonnet 5.5 for the best combination of speed and intelligence, Haiku 5.5 for high-volume, latency-sensitive tasks like classification, extraction, and routing, and Fable 5.1 for the most demanding reasoning and long-horizon agentic work.
Can you turn off thinking in Claude Opus 5.5?
No. Opus 5.5 uses adaptive thinking that is always on and cannot be turned off. You control how much it reasons with the effort setting, which defaults to medium. Raise the effort for harder problems, and keep the default for routine tasks where speed matters.
What is the API model ID for Claude Opus 5.5?
The API model ID is claude-opus-5-5. It is available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. Anthropic says it will not retire the model sooner than September 22, 2027.
What is the knowledge cutoff for Claude Opus 5.5?
Anthropic lists a reliable knowledge cutoff of June 2026. The model may not know about events, software releases, or policy changes after that date unless you provide the information in your prompt or use a tool that can look things up.
Is there a faster version of Claude Opus 5.5?
Anthropic offers a fast mode for Opus 5.5 in Claude Code and the Claude Platform with up to 2.5x speed. It is useful when you are iterating quickly and waiting is the main friction. The standard mode remains the baseline for the model's specifications.
Is Claude Opus 5.5 safe to let act on its own?
Anthropic says Opus 5.5 is much less likely than recent models to take hard-to-reverse actions or act outside the boundaries it has been given, and more resistant than Opus 5 to prompt injection. It is not a guarantee, so keep human approval on sensitive actions and state your limits clearly in the prompt.
Try it on a real task this week
The fastest way to understand a model is to give it your actual work.