Claude Opus 4.7 Unleashed: Self‑Checking, Enhanced Vision, and Code Review Power

Overview

Anthropic has launched Claude Opus 4.7, a new generation of its large language model that focuses on reducing the need for human supervision during long‑running tasks. The pricing remains the same as the previous 4.6 version: $5 per million input tokens and $25 per million output tokens.

Self‑Verification and Longer Tasks

Opus 4.7 adds a self‑checking capability: before returning a result, the model validates its own output and fixes detected issues internally. This reduces the number of intervention points for users running multi‑hour workflows. Rakuten reports a three‑fold increase in task completion rate and more than a ten‑order‑of‑magnitude improvement in code quality when using Opus 4.7 for production workloads. In multi‑task pipelines, success rates rose 14%, tool‑call error rates dropped by roughly one‑third, and token consumption decreased.

Improved Visual Capabilities

The new model supports images with a maximum long side of 2576 pixels (about 3.75 million pixels), more than three times the resolution of the previous version. On a computer‑vision benchmark, Opus 4.7 scored 98.5% compared with 54.5% for Opus 4.6, effectively eliminating a major pain point for users who need high‑fidelity visual analysis such as UI screenshots, technical diagrams, or chemical structures.

Enhanced Programming Ability

Programming performance also improves. On CursorBench, Opus 4.7 achieved 70% versus 58% for Opus 4.6. Notion observed a 14% overall performance boost and a one‑third reduction in tool‑call errors. CodeRabbit reported over a 10% increase in recall for the most complex pull‑requests without losing precision, and Databricks saw a 21% drop in document‑reasoning errors.

A new command /ultrareview enables deep code‑review cycles: the model reads the entire change set, identifies bugs and design flaws, and returns a reviewer‑level report without additional prompts.

API Changes

The API now offers a new inference tier xhigh positioned between the existing high and max levels, giving developers finer control over latency, depth, and cost. The default effort level also moves from high to xhigh, so many workloads automatically benefit from higher quality without configuration changes.

A beta feature called Task Budgets lets Claude manage token consumption during long tasks, allocating more tokens to critical steps and fewer to trivial ones. Tokenizer updates may increase token counts for the same input by 0%–35%, improving model stability at the possible expense of higher cost.

Strategic Direction

All of these updates—self‑verification, longer context, visual perception, code‑review automation, and finer‑grained API controls—converge on a single goal: reducing the frequency of human intervention and turning Claude into a more autonomous agent capable of end‑to‑end execution.

Claude Code Explained

Claude Code is no longer just a chat interface; it functions as an autonomous programming agent that can read repositories, search files, modify code, execute commands, run tests, and perform deep code reviews, all while being orchestrated by Routines, long‑context windows, and sub‑agents.

Pricing and Access

The API pricing remains $5 per million input tokens and $25 per million output tokens. While the official subscription requires an overseas credit card and stable network access, alternative services such as Code80 provide a proxy that converts existing subscriptions to API access.

FAQ

What is the biggest change in Opus 4.7?

Beyond benchmark improvements, the model now systematically enhances long‑task execution with self‑verification, more reliable tool calls, and stronger visual capabilities.

Why is self‑verification important?

Long tasks can drift silently; self‑checking allows the model to catch and correct errors before they propagate, reducing the need for constant human monitoring.

What does the /ultrareview command do?

It runs a thorough code‑review pass, reading the full diff and surfacing reviewer‑level issues such as bugs and design flaws.

How does the xhigh inference tier help?

It fills the gap between high and max, giving developers finer balance between response speed, reasoning depth, and cost.

What impact does this update have on developers?

Developers will see models better suited for long‑chain workflows and will need to adapt to a new paradigm where AI agents handle more of the execution autonomously.