Claude Opus 5.5

5.5 · Family: Claude

Anthropic's Claude-family large language model (5.5, September 2026) for long-running agentic coding, computer use and knowledge work. 1M-token context window.

✓ Active✓ Public accessLLMMultimodalReasoning modelTool-using model📁 Claude

Context window

tokens

Max output

128,000

tokens

Release date

22 September 2026

🏢AnthropicProducer

Access:APIHostedDeployment:☁ Cloud

Overview

Claude Opus 5.5 is a large language model in the Claude family developed by Anthropic, announced on 22 September 2026. Anthropic positions it for long-running agentic work: agentic coding, computer use, and knowledge work.

The model offers a 1M-token context window and up to 128K output tokens in synchronous mode (up to 300K via the Message Batches API with a beta header). It supports text and image input, text output, adaptive thinking (default effort "medium"), tool use, and multilingual capabilities.

Its Claude API model ID is claude-opus-5-5. The model is also available on Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry. Weights are not publicly available (closed model). The reliable knowledge cutoff and training data cutoff are June 2026.

Claude API pricing is $4 per 1M input tokens and $20 per 1M output tokens; prompt cache reads cost 5% of the base input price ($0.20 per 1M tokens). Batch API requests are 50% cheaper.

Classification

LLMMultimodalReasoning modelTool-using model

Family: Claude

Access & deployment

APIHosted

Cloud

Weights: Closed

Key parameters

📏 Context: 1M

✓ Tools

📥 Input: text, image

Technical specification

Context window

tokens

Max output tokens

128,000

tokens per response

Knowledge cutoff

1 Jun 2026

Knowledge boundary

License

Proprietary (Anthropic Commercial Terms of Service)

Hardware requirements

Closed model, available only via cloud (Claude API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry). No local deployment.

Features:✓ Tool use

Modalities

⬇ Input

textimage

⬆ Output

textcode

Capabilities and applications

Native model capabilities

Reasoning

The model's ability to reason logically and solve complex problems.

Category: reasoning

Advanced reasoning

The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.

Category: reasoning

Multi-step reasoning

Carrying out multi-step chains of reasoning across long, complex tasks.

Category: reasoning

Extended thinking mode

A reasoning-model variant with a larger inference budget: more thinking cycles, higher answer precision at the cost of response time. Choice between 'standard' and 'extended' thinking is left to the user (e.g. the selector in GPT-5.2 Pro).

Category: reasoning

Coding

Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.

Category: coding

Agentic coding

Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).

Category: coding

Agentic capability

The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.

Category: planning

Tool use

The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.

Category: planning

Computer use

The model's ability to operate a computer interface by interpreting screenshots and generating actions such as clicks, typing, and navigating applications.

Category: planning

Long context

Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.

Category: language

Structured output

Producing data in structured formats such as JSON.

Category: structured_generation

Image understanding

Analysing and interpreting the content of images.

Category: vision

Chart understanding

Reading and interpreting charts, tables and diagrams.

Category: vision

OCR

Recognising text within images and documents.

Category: vision

Multilingual

Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.

Category: language

Benchmark results

8 benchmarks

Terminal-Bench 4.0

accuracy

66.4%