What Is Claude Opus 4.7? Features, Benchmarks, Pricing, and Everything You Need to Know

TL;DR

Claude Opus 4.7 is Anthropic’s most capable generally available model, released April 16, 2026. It adds high-resolution vision (up to 3.75 megapixels), a new xhigh effort level, task budgets for agentic loops, and a new tokenizer. It keeps the 1M token context window and $5/$25 per million token pricing from Opus 4.6 but introduces several breaking API changes, including the removal of extended thinking budgets and sampling parameters.

Try Apidog today

Introduction

Anthropic released Claude Opus 4.7 on April 16, 2026. It replaces Opus 4.6 as the flagship Claude model and is built for developers creating autonomous agents, knowledge-work assistants, and vision-heavy applications.

This release is important for:

1. High-resolution image support (pixel budget increased from 1.15 MP to 3.75 MP).
2. Introduction of task budgets, allowing you to control the token allowance for entire agentic loops.
3. Breaking API changes that require code updates if migrating from Opus 4.6.

💡 This guide covers Opus 4.7 features, differences from 4.6, costs, migration requirements, and how to test your Claude API integration with Apidog. Apidog supports multi-turn conversations and tool-use payloads, making it ideal for Opus 4.7 workflows.

Core Specifications

Specification	Value
API model ID	claude-opus-4-7
Context window	1,000,000 tokens
Max output tokens	128,000 tokens
Input pricing	$5 per million tokens
Output pricing	$25 per million tokens
Batch input pricing	$2.50 per million tokens
Batch output pricing	$12.50 per million tokens
Cache read pricing	$0.50 per million tokens
5-min cache write	$6.25 per million tokens
1-hour cache write	$10 per million tokens
Release date	April 16, 2026
Availability	Claude API, Amazon Bedrock, Google Vertex AI, Microsoft Foundry

Opus 4.7 uses a new tokenizer that can produce up to 35% more tokens for the same input compared to Opus 4.6. The per-token price is the same, but your effective cost per request may increase depending on your content.

What’s New in Claude Opus 4.7

High-Resolution Image Support

• Image input cap increased from 1,568 (1.15 MP) to 2,576 pixels (3.75 MP) on the long edge.
• Enables higher-fidelity screenshots, mockups, documents, and photos.
• 1:1 pixel coordinate mapping removes the need for scale-factor calculations.
• Improved accuracy in:
  • Low-level perception: pointing, measuring, counting
  • Image localization: bounding-box detection, localization in natural images

Tip: Higher resolution means higher token usage per image. Downsample images if you don't need maximum fidelity to save costs.

New xhigh Effort Level

• The effort parameter now supports: low, medium, high, and xhigh.
• Use xhigh for tasks where quality outweighs latency, such as code generation and complex agentic workflows.
• At xhigh, the model uses more tokens for internal reasoning, improving output for difficult problems.
• For intelligence-sensitive work, use at least high. Lower levels trade accuracy for speed and cost.

Task Budgets (Beta)

• Prevents agentic loops from consuming unlimited tokens.
• Specify a token budget for the entire loop (minimum 20,000 tokens).
• The model sees a running countdown and prioritizes accordingly.
• Budget is advisory (not a hard cap); model may overshoot.
• Not the same as max_tokens, which is a hard per-request ceiling unseen by the model.
• Requires the beta header: task-budgets-2026-03-13.

Use task budgets to control spend for open-ended agentic tasks. For quality-first tasks, you can skip the budget.

Adaptive Thinking as the Only Thinking Mode

• Extended thinking (thinking: {"type": "enabled", "budget_tokens": N}) is removed; attempting to use it returns a 400 error.
• Only adaptive thinking is supported: thinking: {"type": "adaptive"}
• Adaptive thinking allocates reasoning tokens dynamically, resulting in better performance.
• Adaptive thinking is off by default—enable it explicitly.
• By default, thinking content is omitted from responses. To see reasoning (e.g., for streaming), set display: "summarized" in the config.

Improved Memory

• Better reads/writes to file-system-based memory.
• If your agent uses a scratchpad, notes file, or structured memory, Opus 4.7 updates and references notes more accurately.
• Useful for long-running coding agents, research assistants, and workflows needing persistent context.

Knowledge Work Improvements

• Document redlining: More accurate tracked changes in .docx files.
• Slide editing: Improved .pptx layout generation and validation.
• Chart analysis: Enhanced pixel-level chart analysis and data transcription using image-processing libraries (e.g., PIL).

What Changed from Opus 4.6

Breaking API Changes

These affect the Messages API. No breaking changes for Claude Managed Agents.

Change	Before (Opus 4.6)	After (Opus 4.7)
Extended thinking	thinking: {"type": "enabled", "budget_tokens": 32000}	Must use thinking: {"type": "adaptive"}
Sampling parameters	temperature, top_p, top_k accepted	Non-default values return 400 error
Thinking display	Thinking content included by default	Omitted by default; opt-in with display: "summarized"
Tokenizer	Standard tokenizer	New tokenizer (up to 35% more tokens for same text)

Behavior Changes

• More literal instruction following (less generalization).
• Response length scales with task complexity.
• Fewer tool calls by default; increase effort to encourage more tool use.
• More direct, opinionated responses (less emoji, less validation phrasing).
• Fewer subagents spawned by default.

If you previously added prompt scaffolding for behaviors (e.g., “double-check slide layout”), try removing it—Opus 4.7 often handles these natively.

Pricing Breakdown

Opus 4.7 pricing matches Opus 4.6/4.5:

Usage type	Cost
Standard input	$5 / MTok
Standard output	$25 / MTok
Batch input	$2.50 / MTok
Batch output	$12.50 / MTok
Cache read	$0.50 / MTok
5-min cache write	$6.25 / MTok
1-hour cache write	$10 / MTok
Fast mode input (4.6)	$30 / MTok
US data residency	1.1x multiplier

The new tokenizer is the key cost variable: it can produce up to 35% more tokens for the same input, so your effective per-request cost may rise. Use the /v1/messages/count_tokens endpoint to check token counts for your prompts.

The 1M token context window is priced the same as smaller contexts—there’s no long-context premium.

Where to Use Opus 4.7

Strong Use Cases

• Autonomous coding agents: xhigh effort and task budgets provide granular control.
• Computer use: High-fidelity vision and 1:1 pixel mapping improve screen reliability.
• Document processing: Better .docx, .pptx, and chart analysis for automation.
• Long-context retrieval: 1M token window handles large codebases, legal docs, and research.
• Multi-session agents: Improved file-based memory for workflows spanning many conversations.

When Opus 4.7 May Be Overkill

• Simple Q&A or classification: Use Haiku 4.5 ($1/$5 per MTok) or Sonnet 4.6 ($3/$15 per MTok) for cost efficiency.
• Low-latency chatbot flows: Adaptive thinking and high effort add latency.
• Batch analytics on structured data: Batch API with Sonnet is generally more cost-effective.

How to Test Your Claude Opus 4.7 Integration with Apidog

Switching the model ID from claude-opus-4-6 to claude-opus-4-7 is straightforward, but validating your prompts, tool definitions, and error handling after the breaking changes is critical.

Apidog streamlines testing:

• Import your API schema: Use your OpenAPI spec or manually define Claude API endpoints. Apidog auto-generates Messages API request templates.
• Create test scenarios: Build multi-turn conversations to test your tool-use patterns. Chain requests, pass context, and validate response schemas.
• Compare model versions: Run the same tests against claude-opus-4-6 and claude-opus-4-7 side by side to check token counts, response structure, and output quality.
• Validate breaking changes: Ensure your updated thinking config works, removed sampling parameters aren't present, and the new tokenizer doesn't exceed your max_tokens limits.
• Debug tool-use payloads: Inspect full request and response bodies for tool-use conversations. Apidog’s visual interface helps spot malformed tool results or missing tool_use_id references.

Migration Checklist

If you're upgrading from Opus 4.6:

• [ ] Change your model ID to claude-opus-4-7
• [ ] Replace thinking: {"type": "enabled", "budget_tokens": N} with thinking: {"type": "adaptive"}
• [ ] Remove temperature, top_p, and top_k parameters (or set to defaults)
• [ ] If streaming thinking, add display: "summarized" to your thinking config
• [ ] Increase max_tokens headroom for the new tokenizer (up to 35% more tokens)
• [ ] Test prompt caching—token counts will differ
• [ ] Remove prompt scaffolding for behaviors now handled natively
• [ ] Run your test suite with Apidog to validate end-to-end behavior

Conclusion

Claude Opus 4.7 is Anthropic’s most powerful generally available model. The improved vision, task budgets, and xhigh effort level make it ideal for autonomous agents. The breaking changes—especially removal of extended thinking and sampling parameters—require code updates, but migration is straightforward.

Monitor the new tokenizer's impact on costs, as increased tokenization can raise your effective spend. Always test your workloads before moving to production.

For robust API integration testing and debugging, Apidog gives you the environment to validate your migration and compare model versions.