Anthropic released Claude Opus 4.7 on April 16, 2026, and the reception has been unusually mixed for a flagship AI model launch. On paper, the release looks like a solid step forward: significant coding improvements, a sharper vision model, and new features like effort control and a dedicated code review command. In practice, many power users, particularly developers who rely on Claude Code, are calling it a regression. The core issue is that the model’s headline improvements come with a new tokenizer that consumes between 1.0x and 1.35x more tokens for the same input, while Anthropic held pricing steady. The result is a silent price increase that’s hitting Pro and Max subscribers at exactly the usage levels where they feel it most.
The coding gains are real. SWE-bench Pro jumped 11 points, CursorBench climbed from 58% to 70%, and Rakuten-SWE-Bench shows Opus 4.7 resolving three times more production tasks than Opus 4.6. On Terminal Bench, the model passed tasks previous Claude models failed entirely. For finance and economic knowledge work, Opus 4.7 sets state-of-the-art marks on GDPval-AA and Finance Agent evaluations. Vision also got a major upgrade: the model now accepts images up to 2,576 pixels on the long edge, which is 3.75x more pixels than prior Claude versions, making it far more useful for dense diagrams, screenshots, and multimodal documents.
But two regressions have dominated developer conversations. The first is long-context retrieval. On the MRCR benchmark (multi-round coreference resolution), Opus 4.7 dropped from 78.3% to 32.2%. For users whose workflows depend on feeding large codebases or long documents to Claude and expecting accurate recall across that context, this is a meaningful step backward. The second is the tokenizer change. Anthropic says the update improves text processing efficiency and notes that net token usage actually improved on internal coding evaluations. But real-world user reports tell a different story: the same prompts now cost up to 35% more tokens, which for subscribers on capped plans translates directly to hitting weekly limits faster.
The Reddit thread r/ClaudeCode titled “Claude Opus 4.7 is dogshit” and a parallel wave of Hacker News discussion illustrate the sentiment. Developers running agent-heavy workloads, particularly those who burned through tokens on long-running coding tasks, are reporting that their effective monthly usage has dropped noticeably even as their bills remain the same. For enterprise customers paying per-token, the math is different, but for flat-rate Pro and Max subscribers, the economics have quietly shifted.
Pricing remains $5 per million input tokens and $25 per million output tokens, unchanged from Opus 4.6. The model is available across Claude.ai, the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry, making it one of the most broadly distributed releases Anthropic has shipped. New capabilities include a xhigh effort level sitting between high and max, a /ultrareview slash command in Claude Code for dedicated code reviews, auto mode extended to Max users, and task budgets in public beta for controlling token spend.
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The broader context matters. Anthropic is simultaneously maintaining Claude Mythos Preview as its most capable model while positioning Opus 4.7 as the workhorse for general-purpose coding and agentic tasks. Opus 4.7 is described as less broadly capable than Mythos, with cybersecurity capabilities intentionally reduced. That positioning is strategic: Mythos remains gated through Project Glasswing, while Opus 4.7 is the model most users will actually interact with day to day.
For teams in LATAM and globally evaluating which model to standardize on, the Opus 4.7 launch illustrates a growing tension in the AI model market. Benchmark improvements no longer translate cleanly into user-perceived quality, and tokenizer changes can reshape the economics of subscriptions overnight. The lesson for buyers is to monitor actual token consumption on representative workloads, not just published pricing, when comparing models across vendors. For Anthropic, the feedback from power users suggests that the next release will need to address the long-context retrieval regression directly if the company wants to retain developer mindshare in an increasingly competitive field.
