Anthropic Claude Opus 5.5
Anthropic's Opus model for long-running agentic coding and knowledge work, with always-on adaptive thinking, vision, and a 1M-token context window.
Claude Opus 5.5 is a cloud text model built by Anthropic. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 1M input tokens and up to 128K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions. It runs through OpenRouter and Anthropic using your own API key, from $4.00 per million input tokens.
- Released
- this month (Sep 2026)
- Pricing
- $4.00 / 1M in · $20.00 / 1M out
- Inputs
- Text, Images
- Context window
- 1M in · 128K out
- Reasoning
- Adjustable effort
- Controls
- System prompt
Examples
Generated with Claude Opus 5.5 via OpenRouter. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a set of rules that lets one program ask another for something. Think of a vending machine: you press buttons, and it gives you a snack without you seeing the inner workings. Apps use APIs the same way, requesting weather data or maps from other services.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
Use a hosted provider such as Auth0, Clerk, Supabase Auth, or AWS Cognito. With two people, your scarcest resource is focused engineering time, and authentication is a deceptively deep problem: password hashing, session management, token rotation, MFA, account recovery, rate limiting, and a steady stream of security patches. Getting any of it wrong can end a young company, and none of it differentiates your product. A hosted provider gives you a secure, compliant baseline in an afternoon, letting you spend your energy on what customers pay for. The strongest argument against this is lock-in. Your user table becomes someone else's database, pricing often jumps once you pass free-tier limits, and migrating later can be painful because password hashes and session logic may not export cleanly. If the provider raises prices or shuts down, you inherit an urgent, risky migration exactly when you have real users to lose.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Your AI spending is hard to predict because most AI services are billed like a utility rather than a subscription. You pay for every request, measured in small units of text, so costs rise and fall with how heavily employees and customers use the tools. A popular new feature can double the bill in a month without anyone approving a larger budget. A second cause is that the work itself varies. The same question can produce a short answer or a long one, and newer "agent" tools may quietly take dozens of steps to finish a single task, each step adding cost. Third, vendors change prices and models frequently. A cheaper model may appear, or a team may switch to a more capable and more expensive one because it performs better. Finally, adoption is often scattered. Different departments sign up for different services on corporate cards, so no one sees the full picture. You can regain control with a few steps. Assign one owner to track all AI spending centrally. Require teams to set monthly limits and alerts with each vendor. Ask for cost estimates per task, not just totals. Negotiate committed-use discounts once patterns stabilize. Review usage quarterly, retiring tools that deliver little value.
Anthropic
Anthropic builds the Claude family of large language models, focused on safety, helpfulness, and honesty.
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