What Is Claude Opus 4.7 — and Why It's Different from Every Previous Upgrade
Claude Opus 4.7 is Anthropic's most capable generally available model as of April 2026. It introduces high-resolution image analysis (up to 3.75MP), task budgets for agentic work, a new xhigh effort level, and the /ultrareview command in Claude Code. It runs on a 1M-token context window at the same $5/$25 per million token pricing as Opus 4.6.
Released in April 2026, Opus 4.7 is not a minor patch. The coding benchmarks tell the story clearly: SWE-bench Verified jumped from 80.8% to 87.6%, and SWE-bench Pro from 53.4% to 64.3% — the largest single-version coding leap in Claude's history, according to Anthropic's official release notes.
But the practitioner-level story is not about benchmarks. It's about four specific features that change how you structure your prompts and workflows — if you know they're there. Most people upgraded and kept prompting exactly the same way. That's leaving real capability on the table.
High-Resolution Vision: Finally Analysing Documents at Full Detail
Claude Opus 4.7 is the first Claude model to support high-resolution images up to 2,576px / 3.75MP — more than triple the previous 1.15MP ceiling. Charts, slide decks, and dense infographics are now readable at full detail, without the compression artifacts that caused misreadings in Opus 4.6. Anthropic's internal data shows visual navigation accuracy jumped from 57.7% to 79.5% on full-resolution inputs.
In practice, this matters most for a specific pain point that many practitioners have quietly worked around: sending photos of dense tables, scanned documents, or small-text slides to Claude and getting back vague, partially-wrong answers.
With Opus 4.7, a 600dpi scan of a financial statement or a 1920x1080 screenshot of a complex dashboard is now analysed at full fidelity. If you work with contracts, whitepapers, research reports, or any document-heavy workflow, this is the change most worth testing immediately.
Try sending a high-resolution screenshot of a dense spreadsheet and asking Claude to extract every number, note column headers exactly as written, and flag anomalies. The difference versus Opus 4.6 is immediately noticeable.
What Are Task Budgets — and How to Stop Agentic Jobs from Burning Your Token Limit?
Task budgets are a beta feature in Opus 4.7 that let you set a hard token ceiling on an agentic loop. Claude sees a running countdown and prioritises work accordingly, finishing gracefully as the budget is consumed rather than stopping mid-task or generating surprise costs.
If you've run long agentic tasks in Claude — web research, document processing, multi-step analysis — you've probably hit one of two failure modes. Either the task runs indefinitely and costs far more than expected, or it cuts off abruptly mid-step with no clean handoff.
Task budgets solve this. By setting a token ceiling before the task starts, you give Claude the information it needs to make smart tradeoffs: what to prioritise, when to summarise rather than elaborate, and how to wrap up cleanly when the budget is almost exhausted.
This is especially valuable for practitioners who run Claude as part of automated pipelines or batch-processing workflows. Instead of building your own cutoff logic, you can define the budget at the prompt level. Task budgets are currently in public beta under Anthropic's standard managed-agents header.
The xhigh Effort Level: Getting More Depth Without Burning Max Tokens
Opus 4.7 adds an xhigh effort setting that sits between the existing high and max options. Anthropic's data shows xhigh achieves approximately 75% on challenging coding tasks — meaningfully better than high but significantly more token-efficient than max. Use it for hard analytical problems where latency is less critical but accuracy matters.
Before Opus 4.7, the effort controls were effectively binary for most use cases: standard for everyday tasks, max for the hardest problems. Max is expensive and slow. The new xhigh tier sits between them — and for most difficult-but-not-extreme tasks, it hits a better point on the effort-to-token-cost curve.
In practice, reach for xhigh when:
--- You're doing deep document analysis that requires careful cross-referencing across a long source
--- You're building complex structured outputs — reports, frameworks, multi-part analyses
--- You're running code-related reasoning that doesn't need the full max treatment
For everyday prompts — summaries, email drafts, copy editing — xhigh is overkill. Reserve it for the 20% of tasks where depth genuinely changes the output quality.
What Is /ultrareview in Claude Code — and Can Non-Developers Use It?
The /ultrareview command in Claude Code launches multiple specialised AI reviewers simultaneously — one focused on security, one on logic errors, one on test coverage, and one on code quality and style. For non-developer practitioners who use Claude Code to build automation scripts or lightweight workflow tools, /ultrareview is the fastest way to catch errors before they cause problems in production.
Most practitioners assume /ultrareview is only for software engineers. It isn't. If you've used Claude Code to write automation scripts, Zapier integrations, or lightweight tools for your team, this command is now a standard part of your pre-deployment checklist.
The command runs parallel AI review agents in under 60 seconds. The security agent checks for exposed credentials and permission errors. The logic reviewer catches off-by-one errors and faulty conditionals. The test coverage agent flags untested edge cases. Even if you don't understand every flag it raises, running /ultrareview before deploying a new script functions as a peer review from five different specialists — for free, in under a minute.
How Opus 4.7 Changes Your Everyday Prompting Approach
Claude Opus 4.7 is better at understanding implicit goals — meaning you can be slightly less prescriptive in long prompts without sacrificing output quality. It also handles longer context more coherently, retaining earlier instructions and constraints further into a 1M-token window than Opus 4.6 could manage.
Beyond the headline features, Opus 4.7 produces measurably better output on knowledge-worker tasks — particularly document editing, slide creation, and structured analysis. Anthropic specifically highlighted improvements to .docx redlining and .pptx editing, where the model now self-checks its tracked changes against the original document before finalising output.
For practitioners using Claude to produce polished work products — client reports, presentations, proposals — this means fewer revision rounds to get from first draft to send-ready quality.
One practical note on memory: Opus 4.7 is meaningfully better at using file system-based memory across long, multi-session work. If you've set up a Claude Projects workflow with persistent context files, you'll notice the model is more reliable at pulling from earlier sessions without needing reminders.
What to Watch Out For Before You Switch
Setting temperature, top_p, or top_k to non-default values in the Messages API now returns a 400 error in Opus 4.7. If your prompts or automation scripts relied on custom sampling parameters, update them before migrating — otherwise your integrations will silently fail.
The temperature constraint is the only breaking change that affects practitioners without a developer background. If you use Claude via Zapier, Make, or any custom integration that passes temperature settings, those calls will break on Opus 4.7 until you remove the parameter.
Check your integrations before switching the model string from claude-opus-4-6 to claude-opus-4-7. It's a five-minute fix — but the kind that causes a silent failure if you forget. Pricing remains identical: $5 per million input tokens, $25 per million output tokens.
Also note the tokenizer change: Opus 4.7 uses 1x–1.35x more tokens than Opus 4.6 on identical content, depending on the text type. Monitor your usage for the first week after migration if you're on a token-budget plan.
Try It Now: A Prompt That Uses Three Opus 4.7 Upgrades Simultaneously
Paste this prompt into Claude Opus 4.7 together with a high-resolution screenshot of any complex document, dashboard, or data table:
---
Prompt:
"You are analysing a document image at full resolution. Complete the following steps in order:
Step 1: List every piece of text visible in the image, preserving exact formatting and numbers. Do not approximate.
Step 2: Identify the document type and its primary purpose in one sentence.
Step 3: Flag any data point that appears inconsistent, missing, or potentially erroneous. Cite the specific location in the document.
Step 4: Summarise the three most important takeaways from the document in plain language.
If any value is illegible, say so explicitly. Do not guess."
---
This prompt activates high-resolution vision (Step 1), structured reasoning (Steps 2–4), and Opus 4.7's improved self-correction on visual content (the illegibility instruction). Run the same prompt in Opus 4.6 and compare — the difference is clearest on documents with small fonts or dense tables.
Is Claude Opus 4.7 Worth Switching To Right Now?
Yes — especially if you handle image-heavy workflows or run agentic tasks in Claude. The vision upgrade alone resolves a frustration that intermediate AI users have been quietly managing with workarounds for over a year. The task budgets and xhigh effort level give you precision controls that simply didn't exist in any previous Claude version.
The upgrade path is frictionless: same pricing, same API, one model string change. The only thing to check is whether any of your integrations pass temperature parameters.
懂AI的冷，更懂你的難 — UD同行28年，讓科技成為有溫度的陪伴. We write these guides because knowing a model launched is very different from knowing how to use it. Start with the prompt above, then explore xhigh on your next complex analysis task. That's where you'll feel the difference.
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