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Claude Opus 4.7
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AnthropicAnthropicClaude ModeratedReleased 2026-04-16

Claude Opus 4.7

1M context$5.00/M input$25.00/M output

Claude Opus 4.7 is the latest iteration in Anthropic's Opus family, designed for long-running, asynchronous agents. It excels in handling complex, multi-step tasks and extended workflows, making it particularly effective for asynchronous agent pipelines such as debugging large codebases and orchestrating end-to-end projects. Additionally, it offers improved capabilities for knowledge work, including drafting documents, analyzing data, and maintaining coherence across long outputs and extended sessions.

What is Claude Opus 4.7?

Claude Opus 4.7 is an AI model from Anthropic that Agent Mag tracks for pricing, context window, modalities, benchmarks, and API compatibility. Builders can use this page to compare Claude Opus 4.7 against other models for agent workflows and production deployments.

Model ID

Claude Opus 4.7 is the latest iteration in Anthropic's Opus family, designed for long-running, asynchronous agents. It excels in handling complex, multi-step tasks and extended workflows, making it particularly effective for asynchronous agent pipelines such as debugging large codebases and orchestrating end-to-end projects. Additionally, it offers improved capabilities for knowledge work, including drafting documents, analyzing data, and maintaining coherence across long outputs and extended sessions.

Architecture & Specifications
Tokenizer
Claude
License
Proprietary
Released
2026-04-16
Modalities
Input
textimage
Output
text
Supported Parameters
include_reasoningmax_tokensreasoningresponse_formatstopstructured_outputstool_choicetoolsverbosity
Strengths
  • Effective for asynchronous agent pipelines and extended workflows
  • Improved performance on complex, multi-step tasks
  • Enhanced knowledge work capabilities like drafting and data analysis
  • Maintains coherence across long outputs and extended sessions
  • Reliable agentic execution for large-scale projects
Limitations
  • Limited information on training data and architecture
  • Hallucination rate of 63.8% in knowledge-based tasks
  • Lower performance in research-level physics reasoning (12.0%)
  • Moderate coding capability compared to specialized models
  • Requires user IDs for anonymity
Recommended Use Cases
Debugging large codebases
End-to-end project orchestration
Drafting documents and presentations
Analyzing complex datasets
Executing multi-stage workflows

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Data enriched Apr 24, 2026. Pricing from OpenRouter API.