Claude Opus 4.7

Claude Opus 4.7 is an AI model verified from first-party Anthropic sources.

Claude Opus 4.7 verified specifications

Claude Opus 4.7 is an active legacy Anthropic model for complex reasoning and agentic coding, with 1M context, 128K output and adaptive thinking. Its verified context or usage limit is 1000000 tokens, with 128000 tokens maximum output.

Claude Opus 4.7 pricing and access

Claude Opus 4.7 costs $5 per 1M input tokens and $25 per 1M output tokens. Prompt caching and batch discounts are available.

Claude Opus 4.7 best uses

Agentic coding, Complex reasoning, Enterprise research, Long-context analysis, Tool use.

Claude Opus 4.7 limitations

Anthropic recommends migrating to Opus 5, Newer tokenizer changes token counts, Important outputs still require review.

Claude Opus 4.7 capabilities and use cases

In addition, its main capabilities include 1M context, 128K output, Adaptive thinking, Vision, Tool use and Agentic coding. For example, common use cases include Coding agents, Research, Enterprise analysis, Long-context work and Complex problem solving.

Who should consider Claude Opus 4.7?

In practice, this model may suit Agentic coding, Complex reasoning, Enterprise research, Long-context analysis and Tool use. Also, notable strengths include 1M context, Strong coding and reasoning and Broad cloud availability. However, review trade-offs such as Anthropic recommends migrating to Opus 5, Newer tokenizer changes token counts and Important outputs still require review before adopting it.

Claude Opus 4.7 pricing and access

Meanwhile, Claude Opus 4.7 costs $5 per 1M input tokens and $25 per 1M output tokens. Prompt caching and batch discounts are available. Standard Opus pricing is $5/M input and $25/M output, with 50% batch discounts.

Official resources and verification

Use the official model website, official documentation, pricing or release source and additional primary source to confirm current availability, limits and pricing. Product details can change after publication, so rely on primary documentation for final decisions.

Compare with other AI models

Next, continue your research in the AI models directory, Anthropic models and Reasoning Language Model models. Compare providers, pricing, modalities and practical limitations side by side to choose the right model for your workflow.