On April 27, 2026, Anthropic announced a new version of its AI model, Claude Opus 4.7, which offers significant improvements in programming, visualization, and cybersecurity.
On April 27, 2026, Anthropic introduced the updated Claude Opus 4.7 model, significantly surpassing its predecessor, Claude Opus 4.6, in programming performance. The new version has an increased ability to solve complex tasks that previously required close human oversight. Users are already noting the successful delegation of difficult tasks to AI without concerns about the accuracy of the results.
One of the main improvements of Opus 4.7 is the model's ability to handle complex and lengthy tasks with high precision. It not only carefully follows given instructions but also develops methods for automatically verifying its conclusions. Additionally, the model has become more efficient in working with images, providing superior quality visual content.
Cybersecurity remains a priority for Anthropic. The new release includes automatic mechanisms to prevent the use of AI in high-risk scenarios. As part of the cybersecurity verification program, specialists can legitimately use the functionality of Opus 4.7 to investigate vulnerabilities and test systems.
The company has maintained its previous pricing policy: using Opus 4.7 will cost $5 per million input tokens and $25 per million output tokens. The model is available through the Claude API, as well as on Amazon Bedrock, Google Cloud’s Vertex AI, and Microsoft Foundry platforms.
Initial feedback from testers confirms that Opus 4.7 demonstrates increased efficiency in multitasking and timing. The new model helps to identify logical errors more quickly during the planning stage and accelerates task execution, which is crucial for the development of financial technologies and software solutions.
The Anthropic team is confident that Claude Opus 4.7 sets a new standard in programming, offering high power and intuitive interaction. The new model is a reliable assistant in complex professional tasks, demonstrating less dependence on the input data.
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