Most Asia teams hit the same wall with frontier AI: it loses the plot halfway through a long task, can not actually read the small text on a screenshot, and quietly burns through a credit card when left running overnight. Claude Opus 4.7 is Anthropic's answer to all three problems in one release. The 1M token context window means you can feed it an entire codebase, a year of board minutes, or a hundred-page tender document and ask questions across the whole thing without chunking. The new high-resolution image support reads small Mandarin or Japanese UI text, dense Excel cells, and architecture diagrams that earlier models hallucinated their way through.
The second shift is economic. Task budgets cap how many tokens an autonomous agent can spend on a job, so a Singapore developer running an overnight refactor or a Manila support team running a CRM agent does not arrive at the office to a ten thousand dollar bill. Anthropic reports Opus 4.7 solves three times more production tasks than Opus 4.6, with users handing off their hardest coding work with confidence according to Anthropic's launch post.
For anyone in Asia who has been waiting for a model that can actually finish long jobs without supervision, this is the one to test this week.
Intermediate Platform Guide Claude Claude
Claude Opus 4.7: Long Context and Agent Budgets
A practical guide to Claude Opus 4.7's million-token context, sharper vision, and task budgets, with workflows for teams across Asia.
AI Snapshot
- ✓ Anthropic's Opus 4.7 launched on 16 April 2026 with a 1M token context window and high-resolution image input up to 2576px, scoring 70% on CursorBench, 87.6% on SWE-bench Verified, and 98.5% on visual acuity.
- ✓ Task budgets, /ultrareview, and the new xhigh effort mode let you cap how much money and time an agent spends before you wake up to a surprise bill.
- ✓ It costs the same as Opus 4.6 (USD 5 per million input tokens, USD 25 per million output tokens) and is the strongest general model for long, vision-heavy, multi-step work right now.
Why This Matters
How to Do It
1
Opus 4.7 is available on Claude.ai for Pro, Max, Team, and Enterprise plans, in Claude Code, and on the API as
claude-opus-4-7. It is also live on Amazon Bedrock, Google Vertex AI, and Microsoft Foundry. Asia developers can call it through the same Anthropic API endpoints, with the closest low-latency region typically being AWS Tokyo or GCP Singapore. Pricing matches Opus 4.6: USD 5 per million input tokens and USD 25 per million output tokens, with prompt caching cutting that by up to 90% on repeated context.2
In the Claude.ai web app, open the model picker and select Opus 4.7. For agentic and analysis work, enable extended thinking and pick the new xhigh effort mode for your hardest tasks. xhigh tells the model to think longer and verify its own outputs before answering. For everyday writing, leave it on standard, since extended thinking adds cost and latency. In the API, set
thinking: {type: "enabled", budget_tokens: 32000} and model: "claude-opus-4-7".3
If you are calling Opus 4.7 inside an agent loop, pass a task budget so the model stops itself when it has spent too much. In Claude Code or via the Agent SDK, use
--task-budget-tokens 200000 or set max_tokens and max_steps on the agent run. A safe starter for an overnight code refactor is 500k input tokens and 100k output tokens. The agent will report when it has used 80% of its budget and ask whether to continue, which prevents the runaway costs that scared finance teams off long agents in 2025.4
The 1M context window is the headline feature for knowledge work. Instead of summarising a tender or splitting a codebase into pieces, paste or attach the full source. Opus 4.7 can hold roughly 750,000 words at once, which is most book-length contracts, a full quarter of board minutes, or the source for a medium application. Ask cross-document questions like "compare clause 14.2 across all three vendor contracts and flag every difference". Quality stays consistent across the full window, unlike older models that forgot the middle.
5
Drop in screenshots at their original resolution up to 2576 pixels on the long edge. Opus 4.7 reads dense spreadsheets, finance dashboards, Mandarin and Japanese interfaces, and engineering diagrams that Opus 4.6 lost detail on. Visual acuity jumped from 54.5% to 98.5%. Avoid downsampling unless you are processing a huge batch and want to save tokens. For interface screenshots, ask the model to point at exact pixel coordinates so it can drive Claude Computer Use without scaling math.
6
Before sending a contract analysis to a client or merging a long code change, ask Opus 4.7 to /ultrareview its own output. This new command runs a structured self-review that catches the kind of small errors that slip through a normal sanity check. It is slow and expensive, so reserve it for outputs where mistakes have real cost, like legal summaries, financial models, and code that touches production. Treat it as a second pair of eyes, not a replacement for human review.
Common Mistakes
⚠ Treating it like Opus 4.6 with a bigger window
Opus 4.7's gains come from how you use the new features, not just from swapping models. Teams that paste the same prompts they used in 4.6 often see only a small lift. Spend an hour rewriting your prompts to use long context, high-res images, and task budgets explicitly.
⚠ Forgetting to set a task budget
Long agent runs without a budget can quietly spend hundreds of dollars. Always pass `budget_tokens` or `max_steps` for any agent that runs unattended. The token count is the new equivalent of a credit card limit, and forgetting it is the most common reason finance teams pull the plug on AI projects.
⚠ Downsampling images before upload
Many SDKs and apps automatically resize images. If you are using Opus 4.7 for vision tasks, check that your client is sending the original resolution up to 2576px. Otherwise you are paying for a frontier vision model and feeding it Opus 4.6 inputs.
⚠ Using xhigh effort for everything
xhigh effort and /ultrareview are powerful but slow and expensive. Use them for hard, high-stakes tasks: legal analysis, complex coding, financial modelling. Do not enable them by default, or your costs will jump 3 to 5x for no quality gain on simple tasks.
⚠ Skipping the human review on long agent runs
Even with /ultrareview, Opus 4.7 can confidently make mistakes on long autonomous tasks. Treat its output as a strong first draft, not a finished deliverable. Build a checkpoint into your workflow where a human signs off before anything ships to a client or production.
Recommended Tools
Claude.ai
The web app where most Asia users will access Opus 4.7 day to day. Pro at USD 20 per month or Max at USD 100 per month for heavier use. Includes Projects, Cowork, and Computer Use.
Visit →Claude Code
Anthropic's CLI for agentic coding. Best place to use Opus 4.7's task budgets and long context for real codebase work. Free tier available, paid usage on the API.
Visit →Anthropic API
Direct API access for building your own apps. Model id `claude-opus-4-7`. Supports prompt caching and the 1M token context. Pay per token at USD 5 input / USD 25 output per million.
Visit →Amazon Bedrock
Opus 4.7 is available in Bedrock with the model id `anthropic.claude-opus-4-7-v1:0`. Useful for Asia teams that already have AWS data residency or Singapore/Tokyo/Mumbai region requirements.
Visit →Google Vertex AI
Same model on GCP, with Vertex AI's pipeline tools for evaluation and monitoring. Useful for teams running A/B tests against **[Gemini 3.1 Pro](https://aiinasia.com/guides/learn/gemini-3-1-pro-practical-guide-real-work)**.
Visit →Microsoft Foundry
Opus 4.7 inside the Azure ecosystem. The fastest path for enterprises in Asia that have standardised on Microsoft 365 and want Claude alongside Copilot.
Visit →FAQ
Should I upgrade from Opus 4.6 to 4.7?
Yes, for any work that involves long documents, high-resolution images, or autonomous agents. Opus 4.7 is a clear upgrade with the same pricing. For short writing tasks, the difference is small and you can stay on 4.6 if you have it pinned.
How much does it cost to run a long agent task?
A typical overnight code refactor consuming 500k input tokens and 100k output tokens costs about USD 5 plus output. A 1M token context analysis with extended thinking can run USD 10 to USD 30. Use prompt caching to cut input costs by up to 90% on repeated context. Always set a task budget.
Is Opus 4.7 better than Gemini 3.1 Pro or GPT-5.4?
On coding and long autonomous work, yes. Opus 4.7 hit 87.6% on SWE-bench Verified and leads on production task completion. Gemini 3.1 Pro has a larger 2M context window and tighter Google Workspace integration. GPT-5.4 is competitive on general reasoning. Best practice in Asia teams is to keep all three available and route by task. See our ChatGPT vs Claude vs Gemini comparison for more.
Does Opus 4.7 work in Mandarin, Japanese, Korean, and Southeast Asian languages?
Yes. Opus 4.7 handles all major Asian languages including Mandarin, Cantonese, Japanese, Korean, Thai, Vietnamese, Bahasa Indonesia, and Tagalog. The high-res image input is especially useful for reading dense character-based UIs and documents. Output quality is strongest in English but very usable across these languages.
Is my data safe when I use Opus 4.7?
On the API and Bedrock, your inputs are not used for training and are encrypted in transit and at rest. On Claude.ai consumer plans, conversations may be reviewed for safety but not used for training unless you opt in. Enterprise plans add zero-data-retention options. For PDPA, GDPR, or country-specific compliance in Asia, deploy via Bedrock or Vertex AI in a regional zone.
Next Steps
If you have not used a frontier model for a real overnight task before, start small: pick one document or one repo, set a USD 10 task budget, and see what comes back in the morning. Read our guide on AI Agents Explained for the broader pattern, and Claude Computer Use if you want to give Opus 4.7 hands as well as eyes.