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Model details

Claude Opus 4.7

Claude Opus 4.7 is the latest generation of Anthropic's flagship Opus family, purpose-built for long-running asynchronous agents and complex enterprise workflows. It advances beyond earlier Opus versions with stronger performance on coding, vision, and multi-step reasoning tasks. The model is designed to maintain consistency and reliability across extended operations, making it well-suited for applications that demand persistence, judgment, and sustained follow-through. Its architecture supports tasks that unfold over time, from navigating large codebases to orchestrating end-to-end projects.

The model builds directly on the agentic strengths established in the Opus 4.6 lineage, bringing improved knowledge work capabilities across document drafting, presentation building, and data analysis. Claude Opus 4.7 maintains coherence across very long outputs and extended sessions, handling complex professional work with greater thoroughness and consistency. It remains a strong default choice for teams deploying AI agents in production environments, particularly where tasks require sustained autonomy and reliable execution over extended timeframes.

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Quick Info

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Provider
Databricks
Model key
databricks-claude-opus-4-7
Release date
Apr 16, 2026
Last updated
Apr 16, 2026
Knowledge cutoff
2026-01-31
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$5.00
Output token cost
$25.00

Limits

Output tokens
128,000 tokens
Context window
1,000,000 tokens

OpenCode

Model variants

Fast

Input
$30.00
per 1M tokens
Output
$150.00
per 1M tokens
Cache Read
$3.00
per 1M tokens
Cache Write
$37.50
per 1M tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Claude Opus 4.7

Databricks

CoverageAnalysis

Caylent's deep dive frames Opus 4.7 as Anthropic's most capable generally available model for coding, enterprise workflows, multimodal reasoning, financial analysis, life sciences, cybersecurity, and long-running agentic work. At the spec level it supports a 1M context window with no long-context pricing premium, up to Migration is treated as a real engineering task with two breaking API changes: sampling parameters are deprecated (non-default temperature, top_p, or top_k will return 400), and manual thinking budgets are removed in favor of adaptive thinking. Developers must remove sampling parameter overrides from their harness and

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