What is Claude Opus 4.7?
Claude Opus 4.7 is a high-capability reasoning model from Anthropic. It is designed for work that benefits from careful multi-step analysis rather than only quick text generation. Typical tasks include advanced software engineering, repository-scale code review, research, complex planning, document analysis, and agentic workflows in which the model repeatedly calls tools and evaluates the results.
The canonical Claude API model ID is claude-opus-4-7. Anthropic released the model on April 16, 2026. It is also available through Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS, although those services may use platform-specific identifiers and interfaces.
Claude Opus 4.7 sits in Anthropic’s premium Opus family. That positioning is important: it is intended for difficult, intelligence-sensitive workloads, not for every routine request. Smaller or newer models may be preferable when response speed, high-volume economics, or current-generation compatibility matters more than maximum capability within this model family.
Supported modalities and core capabilities
Claude Opus 4.7 accepts text and image input and produces text output. Image understanding can be used for screenshots, visual document inspection, supported PDF content, diagrams, and computer-use workflows. It does not natively generate images, audio, or video. Tool calls do not change that output classification: tools allow the model to request actions or information, while its direct model response remains text.
The model supports adaptive thinking, which allows the application to configure how much reasoning effort Claude should apply. Documented effort levels range from low through max. Anthropic identifies high as the API default and recommends xhigh for coding and agentic tasks. Lower settings can be useful for narrowly scoped or cost-sensitive requests, while higher settings are intended for problems where additional reasoning is worth the additional latency or token use.
Tool use is supported through client-side tools and Anthropic server-side tools, including web search where enabled by the platform and account configuration. In practice, this makes Opus 4.7 suitable for agent loops: the model can inspect a task, request a tool operation, receive the result, and continue reasoning. Tool availability and behavior can vary by interface, account, and cloud platform.
Context window and output limits
Claude Opus 4.7 has a 1-million-token context window. A context window is the amount of input and conversation material the model can consider during a request. This limit is useful for large source collections, long technical specifications, extended conversations, and codebases that would otherwise need to be divided into many smaller prompts.
The standard maximum output is 128,000 tokens. Anthropic’s documentation also describes a beta maximum output of up to 300,000 tokens for the Batch API. These are maximum allowances, not a promise that every response will use the full amount. Actual output length depends on the request, configuration, tool activity, and platform behavior.
A large context window does not eliminate the need for good prompt design. Applications still need to select relevant material, manage repeated tool results, and monitor token costs. Sending a very large context can also increase expense and may not improve a narrowly defined task.
Pricing and cost trade-offs
Standard Anthropic API pricing is $5 per million input tokens and $25 per million output tokens. Input tokens are the text or other supported content sent to the model; output tokens are the content it generates. Because output pricing is substantially higher than input pricing, applications should avoid requesting unnecessarily long answers, especially in automated workflows.
Prompt caching is available for repeated context. The documented five-minute cache-write rate is $6.25 per million tokens, the one-hour cache-write rate is $10 per million tokens, and cache reads cost $0.50 per million tokens. The minimum cacheable prompt length for Claude Opus 4.7 is 2,048 tokens on supported Anthropic platforms. Caching can be useful when an application repeatedly supplies the same system instructions, reference documents, or codebase context.
The Batch API applies a 50% discount to input and output token pricing. That produces effective rates of $2.50 per million input tokens and $12.50 per million output tokens. Batch processing is more appropriate for work that does not require an immediate response, such as queued document analysis or large evaluation runs.
These prices place Opus 4.7 in a premium tier. Its cost is easier to justify when a failure is expensive, the task requires substantial reasoning, or a single request can replace a long chain of simpler model calls. For routine classification, short summaries, and high-volume generation, a faster or less expensive model may provide better overall economics.
Developer features
Claude Opus 4.7 supports the main API features needed for production agent and content workflows:
- Streaming responses through the Messages API.
- Client-side and server-side tool use.
- Adaptive thinking and configurable reasoning effort.
- Structured outputs for responses that follow a defined schema.
- Strict tool use for more predictable tool-call arguments.
- Prompt caching with five-minute and one-hour cache durations.
- Batch processing with discounted input and output pricing.
- Text-and-image input for multimodal analysis.
Structured outputs should not automatically be described as a separate legacy JSON mode. Anthropic documents structured JSON responses and strict tool use, but a distinct standalone JSON-mode capability is not independently verified for this model. Applications that need machine-readable results should use the documented structured-output mechanism and validate responses in their own code.
Reasoning and coding profile
Anthropic positions Opus 4.7 for difficult reasoning and agentic coding. Its adaptive effort controls are particularly relevant when a task has several dependencies, requires comparing alternatives, or involves repeated interaction with tools. Examples include tracing a bug across a large repository, planning a multi-stage migration, reviewing a complex pull request, or synthesizing evidence from a large document set.
For coding, the model is suited to repository-scale software engineering rather than only generating isolated snippets. It can help inspect existing code, explain interactions between components, propose changes, review implementation details, and work through tool-assisted development loops. The supplied research supports a strong coding evaluation, but that score is an editorial or database assessment rather than an Anthropic-published benchmark result. It should therefore be treated as a comparative guide, not as a formal provider claim.
The same distinction applies to qualitative ratings for reasoning, speed, and cost. The documented specifications are the context window, output limits, pricing, modalities, and supported features. Statements about being better or worse for a particular workload are practical evaluations based on the model’s positioning and trade-offs.
Availability and lifecycle status
As of September 24, 2026, Claude Opus 4.7 is accessible through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. Availability, account requirements, regional access, and feature support can differ between those platforms.
Anthropic’s lifecycle documentation lists the model as active with a tentative retirement commitment of not sooner than April 16, 2027. “Not sooner than” is a minimum commitment rather than a guaranteed shutdown date. The model is nevertheless classified as legacy because newer Opus models are available. Anthropic recommends Claude Opus 5.5 as the migration target.
This status does not mean that Opus 4.7 is immediately unavailable. It does mean that teams beginning a new integration should assess whether the recommended successor offers a better long-term choice. Existing applications should record the exact model ID and test compatibility before changing models, particularly if they depend on effort controls, structured outputs, tool behavior, or output-length characteristics.
Best use cases for Claude Opus 4.7
Claude Opus 4.7 is most suitable when a high-quality answer can justify premium token pricing or slower response times. Strong use cases include:
- Advanced software engineering and code review.
- Repository-scale analysis and debugging.
- Complex research and evidence synthesis.
- Long-document and visual document analysis.
- Multi-step planning and decision support.
- Agentic workflows that require repeated tool calls.
- Computer-use workflows involving screenshots or other visual inputs.
- High-stakes knowledge work where a capable model can reduce manual investigation.
Its 1-million-token context window is particularly useful when splitting a source collection would lose relationships between sections. Image input also makes it more practical for workflows involving screenshots, visual PDFs, or interface inspection than a text-only model.
When another option may be better
Opus 4.7 is not the best default for every request. A smaller Claude tier may be more appropriate for simple classification, routine summarization, short transformations, or high-volume generation where the premium Opus price is difficult to justify. A faster model may also be preferable for latency-critical interactive applications.
Teams starting a new frontier-model integration should compare Opus 4.7 with Anthropic’s recommended Claude Opus 5.5 migration target. The newer model may offer a better long-term support position, but the supplied research does not provide a detailed performance or pricing comparison, so no specific superiority claim can be made here.
Opus 4.7 is also unsuitable when the application requires native image, video, or audio generation. It can understand images and generate text, but those capabilities should not be confused with direct non-text media output. Human review remains important for consequential decisions because tool access and large context do not guarantee factual accuracy.
Overall assessment
Claude Opus 4.7 is a premium, text-output reasoning model built for demanding coding, research, document, and agent workflows. Its strongest practical differentiators are the 1-million-token context window, configurable reasoning effort, image understanding, tool support, and high output limits. Prompt caching and discounted batch processing provide ways to manage the cost of repeated or asynchronous work.
The main trade-offs are premium pricing, generally higher latency than smaller Claude models, text-only output, and legacy lifecycle status. It remains a capable choice for existing systems and difficult workloads, but new adopters should evaluate Anthropic’s recommended Opus 5.5 migration path before committing to Opus 4.7 as a long-term default.