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Claude Opus 4.7 API
Claude Opus 4.7 is an Anthropic model for difficult software engineering, sustained agent workflows, and professional knowledge work. It is designed to follow detailed instructions closely, catch faults during planning, and verify results before handing work back.
On iCreat, developers can call claude-opus-4-7 through our Anthropic-compatible Messages API. Existing Anthropic SDK request patterns can be directed to the iCreat endpoint, with support for streamed responses and typed response-content handling.
Model Positioning
Claude Opus 4.7 is the direct successor to Opus 4.6. Its value is not simply a larger context window—the two generations publish the same context and maximum-output limits—but stronger execution on difficult coding, stricter instruction following, improved visual detail, and finer reasoning control.
Anthropic has since released newer Claude generations. Opus 4.7 remains relevant when teams want this specific model generation for evaluated production behavior, controlled migration from Opus 4.6, or demanding workloads that benefit from its verification-oriented approach.
Key Capabilities
Verification-Led Software Engineering
Claude Opus 4.7 is built for difficult engineering work that spans planning, implementation, debugging, and validation. It is more likely to inspect its own assumptions and devise a check before reporting completion, which is useful for repository-scale changes and hard-to-reproduce failures.
Strict Instruction Execution
The model follows detailed constraints more literally than Opus 4.6. This improves consistency when requirements are precise, but older prompts that depended on the model overlooking or loosely interpreting instructions should be retested.
Fine-Grained Reasoning Effort
Through iCreat, Opus 4.7 provides optional Adaptive Thinking and effort levels from low to max, including xhigh for long-running coding and agent tasks. Manual thinking budgets are not supported.
Long-Context Work with Extended Deliverables
Anthropic publishes a 1M-token context window and 128K maximum output for synchronous Messages requests. This combination is suited to large repositories, extensive source collections, and tasks that need both broad evidence intake and a substantial final artifact.
High-Resolution Visual Understanding
Claude Opus 4.7 accepts text and image input through iCreat and improves recognition of fine detail in screenshots, diagrams, and document images.
Pricing
iCreat bills Claude Opus 4.7 by input, output, cache-read, and cache-write tokens:
| Token Type | Price |
|---|---|
| Input | $1 per 1M tokens |
| Output | $5 per 1M tokens |
| Cache Read | $0.10 per 1M tokens |
| Cache Write | $1.25 per 1M tokens |
Use Cases
- Complex debugging and refactoring: investigate failures, coordinate changes, and validate results across a large codebase.
- Asynchronous engineering agents: support CI/CD, automation, and long-running development work when the application supplies tools and checkpoints.
- Instruction-heavy workflows: execute tasks with detailed acceptance criteria, formatting rules, or multi-step procedures.
- Professional document analysis: connect evidence across long financial, legal, research, or technical materials.
- Dense visual review: interpret detailed screenshots, document images, and complex diagrams.
These workflows still need application-provided tools, permissions, stopping rules, tests, and human review where outputs affect production systems or consequential decisions.
Model Comparison
Claude Opus 4.7 vs Claude Opus 4.6
| Factor | Claude Opus 4.7 | Claude Opus 4.6 |
|---|---|---|
| Positioning | Harder software engineering with stricter execution and verification | Complex agentic tasks and long-horizon work |
| Context Window | 1M tokens | 1M tokens |
| Maximum Output | 128K tokens | 128K tokens |
| Reasoning Control | Adaptive Thinking; manual thinking budget not supported | Adaptive Thinking; manual thinking budget supported but deprecated |
| Effort Range | low to max, including xhigh |
low to max |
| Migration Impact | Updated tokenizer may count the same text as 1.0–1.35× as many tokens | Previous tokenizer baseline |
| Best Fit | Difficult coding that benefits from strict instructions and explicit validation | Existing long-horizon workflows that do not need 4.7 behavior |
Claude Opus 4.7 vs GPT-5.4 and Gemini 3.1 Pro
| Factor | Claude Opus 4.7 | GPT-5.4) | Gemini 3.1 Pro |
|---|---|---|---|
| Positioning | Strict execution and verification for difficult engineering | Complex professional work | Complex problem-solving and multimodal agentic work |
| Context Window | 1M tokens | 1.05M tokens | 1,048,576 tokens |
| Maximum Output | 128K tokens | 128K tokens | 65,536 tokens |
| Official Input Modalities | Text and image | Text and image | Text, image, video, audio, and PDF |
| Reasoning Control | Adaptive Thinking; low to max, including xhigh |
Reasoning effort; none to xhigh |
Thinking supported |
| Best Fit | Instruction-heavy coding and review | General professional workflows in the OpenAI ecosystem | Multimodal analysis and Google-centered workflows |
Why Choose Claude Opus 4.7?
Choose Claude Opus 4.7 when difficult work must follow a detailed brief, continue across many steps, and include a deliberate validation pass before completion. Its distinguishing combination is strict instruction execution, verification-oriented software engineering, and an xhigh effort level designed for sustained agentic work.
For short transformations or latency-sensitive traffic, compare a faster model. For a migration from another generation, use the same production tasks to re-evaluate prompt behavior, token counts, latency, and completion quality before switching traffic.
Specifications
| Category | Description |
|---|---|
| Model Name | Claude Opus 4.7 |
| Provider | Anthropic |
| Model ID | claude-opus-4-7 |
| Model Type | Capability-focused reasoning LLM |
| Context Window | 1M tokens |
| Maximum Output | 128K tokens |
| Official Input Modalities | Text and image |
| Output Modalities | Text |
| Reliable Knowledge Cutoff | January 2026 |
| Official Reasoning Control | Optional Adaptive Thinking; low to max, including xhigh |
| iCreat Input Modalities | Text and image |
| iCreat API Features | Anthropic-compatible Messages API, text and image input, streaming, Adaptive Thinking, effort control, thinking/text content-block handling |
| Best Suited For | Difficult software engineering, asynchronous agents, instruction-heavy professional work |
Architecture
Anthropic does not publish Claude Opus 4.7's parameter count or underlying network architecture. The visible changes most relevant to developers are its updated tokenizer and its Adaptive Thinking behavior: thinking is optional, must be explicitly enabled on Anthropic's native API, and is guided by effort rather than a manual thinking-token budget.
The updated tokenizer can produce a different token count from Opus 4.6 for identical text. This is a migration characteristic, not evidence that every request will cost a fixed percentage more.
Production Notes
Retest prompts written for earlier Claude versions. Opus 4.7's more literal instruction following can expose contradictory requirements or change the result of prompts that previously relied on implicit interpretation.
Re-baseline token usage instead of carrying forward Opus 4.6 estimates. Anthropic reports that the same text can map to roughly 1.0–1.35 times as many tokens with the updated tokenizer, depending on content.
Treat model-level self-verification as one layer of quality control, not a replacement for executable tests, source checks, access controls, or human approval. Confirm the current iCreat request limits before production rollout.
FAQ
Which effort level should I start with for coding agents?
Start with xhigh for sustained coding and agentic work. Use high when quality and token efficiency need a closer balance, and reserve max for cases where evaluation shows a measurable gain beyond xhigh.
What should I do when a response stops at max_tokens?
Increase max_tokens so the request has room for both thinking and the final answer, or lower the effort level if the task does not need as much reasoning. Re-test latency and completion quality after either change.
Must thinking blocks be preserved between tool calls?
Yes. Pass the returned thinking and text blocks back unchanged when continuing the assistant turn with tool results. Dropping or rewriting the thinking blocks can break the model's reasoning continuity.
What image resolution can Claude Opus 4.7 accept?
Claude Opus 4.7 accepts images up to 2,576 pixels on the long edge, approximately 3.75 megapixels. Preserve enough source detail for dense screenshots and diagrams instead of downscaling them more than necessary.