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Model details

claude-opus-4-1-20250805

Claude 4.1 Opus is engineered as a high-capacity model focused on delivering deep reasoning and sophisticated language understanding. It is designed to handle intricate tasks that require a nuanced grasp of context, making it a robust choice for users who need to process dense information or manage complex workflows. By prioritizing clarity and precision, the model serves as a versatile tool for professional environments where accuracy and logical consistency are paramount.

The model benefits from a refined development lineage that emphasizes reliability in demanding applications. Its architecture is optimized to maintain performance across varied inputs, ensuring that it remains a dependable asset for tasks ranging from detailed analysis to creative synthesis. As a forward-looking solution, it is well-suited for integration into systems that require consistent, high-quality output, providing a stable foundation for users looking to leverage advanced language processing in their daily operations.

302.AIclaude-opus-4-1-20250805claude-opus

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Provider
302.AI
Model key
claude-opus-4-1-20250805
Release date
Aug 5, 2025
Last updated
Aug 5, 2025
Knowledge cutoff
2025-03-31
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$15.00
Output token cost
$75.00

Limits

Output tokens
32,000 tokens
Context window
200,000 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-4-1-20250805

Jiekou.AI

Coverage

Anthropic retired the claude-opus-4-1-20250805 model ID on August 5, 2026, completing its previously scheduled deprecation cycle on the Claude API, Claude Platform on AWS, and Microsoft Foundry. The Kingy AI lifecycle record carries the stable event ID ksr-anthropic-claude-opus-4-1-2026-08-05 and was corrected post-dea The tracker prescribes a concrete migration path: replace claude-opus-4-1-20250805 with claude-opus-5, follow Anthropic's model migration guidance, and regression-test prompts, tool calls, safety behavior, latency, token use, and cost before full cutover. Source evidence reviewed consists of Anthropic's official releas

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