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Today in AI | Anthropic launches Claude Opus 4.7 | Alibaba unveils Happy Oyster AI model - Storyboard18

Google News · April 17, 2026
Today in AI | Anthropic launches Claude Opus 4.7 | Alibaba unveils Happy Oyster AI model Storyboard18 [truncated: Google News RSS provides only a snippet, not full article

Detailed Analysis

Anthropic launched Claude Opus 4.7 on April 16, 2026, marking a significant iterative upgrade over its predecessor, Opus 4.6, with notable improvements across coding performance, visual processing, and complex multi-step task execution. The new model outperforms Opus 4.6 by 13% on a 93-task coding benchmark and successfully completes four tasks that prior versions could not handle, positioning it as a meaningfully stronger tool for software engineering workflows that require reduced human supervision. Among its most technically notable enhancements is a tripling of image processing capacity — now supporting images up to 2,576 pixels on the long edge — alongside higher scores on specialized benchmarks in finance agent evaluations and the GDPval-AA suite covering finance and legal knowledge work. New developer-facing features include a "xhigh" effort level for tuning the balance between reasoning depth and response speed, task budgets now available in public beta via the API, and an ultrareview command within Claude Code designed specifically for bug detection.

The release is priced identically to Opus 4.6 at $5 per million input tokens and $25 per million output tokens, signaling that Anthropic is not treating the upgrade as a premium tier jump but rather as a standard model iteration. Availability spans Claude's own product interfaces as well as major cloud and developer platforms — Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, and GitHub Copilot — the last of which applies the model at a 7.5× premium request multiplier for Pro+, Business, and Enterprise users through April 30, 2026. Opus 4.7 is expected to replace Opus 4.5 and 4.6 within GitHub Copilot Pro+ in coming weeks, consolidating the platform's offering around the latest generation. Developers should note that a new tokenizer introduced in this release increases token counts by a factor of 1.0–1.35×, which may require prompt adjustments to maintain cost and performance expectations.

Safety architecture remains a central design consideration for Anthropic with this release. Opus 4.7 includes cyber safeguards that block high-risk cybersecurity requests, and its cyber capabilities are deliberately constrained relative to the more powerful but limited-release Claude Mythos Preview. The accompanying Cyber Verification Program offers a pathway for credentialed security professionals to access capabilities that are otherwise restricted, reflecting Anthropic's ongoing effort to create tiered access structures that balance utility for legitimate researchers against misuse risk. This approach mirrors a broader industry pattern of differentiated access policies as frontier models grow increasingly capable in sensitive technical domains.

The launch fits squarely within Anthropic's accelerating cadence of model releases — Opus 4.7 arrived approximately 70 days after Opus 4.6's February 5, 2026 debut — illustrating how the competitive AI landscape is compressing development cycles across all major labs. The improvements in vision processing and domain-specific benchmarks in finance and legal contexts point toward Anthropic's deliberate push into enterprise verticals where accuracy, auditability, and multi-modal inputs matter most. Pre-launch reporting had speculated about the possible bundling of AI-powered design tools for websites and presentations, but the final release focused exclusively on model-level improvements, suggesting Anthropic is prioritizing foundational capability gains before layering in higher-order application features. The simultaneous mention of Alibaba's Happy Oyster model in regional coverage underscores the increasingly crowded global frontier, where Chinese and American AI developers are releasing competitive models in near-simultaneous windows, intensifying pressure on each lab to demonstrate measurable, benchmark-validated progress with each iteration.

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