claude-opus-4-6

Name: claude-opus-4-6
Brand: Anthropic

AnthropicChatReasoningVision

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claude-opus-4-6

A deep reasoning model for complex programming and long-form material analysis

Claude Opus 4.6 is Anthropic's Opus series model for intensive reasoning, complex programming, and knowledge work. It focuses on improving task planning, large codebase understanding, review and debugging, and long-text information integration, making it suitable for work that requires sustained analysis rather than quick short answers. Applications can be integrated according to the public request format in this page's API section.

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AnthropicModel brand

ChatModel type

Reasoning, visual understandingTask capabilities

STANDARD APIs · QUICK SETUP

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Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.

API host: api.acedata.cloud
model: claude-opus-4-6

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OpenAI Python SDK

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ACEDATACLOUD_API_KEY"],
    base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
    model="claude-opus-4-6",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.

Specifications and interface features

Clarify capacity, input and output, and calling methods before choosing a model.

Input and output: Text and image input; text responses and code generation
Native long context: 1 million tokens; available as a Claude Developer Platform Beta capability at launch
Native maximum output: 128k tokens
Native reasoning controls: Adaptive thinking; effort supports low, medium, high, and max, with high as the default
Programming focus: Task planning, large codebase understanding, code review, debugging
Chat access: Chat Completions or Messages API

Native capacity and thinking controls describe the model capabilities published by Anthropic; this platform's image-text, file, and tool workflows are used according to the selected access point and authorization scope.

Core Capabilities

Learn what claude-opus-4-6 can bring to your work.

Plan first, then handle complex code

Opus 4.6's programming improvements are reflected not only in writing code, but also in understanding the task first, locating relevant modules, checking edge cases, and then proposing changes. When facing an unfamiliar codebase or cross-file issues, have it progressively review errors, implementations, and tests to deliver fix recommendations and verification steps, rather than just isolated snippets.

Build connections from extensive materials

It enhances information retrieval and subsequent reasoning for long texts, making it suitable for placing scattered requirements, contract terms, technical records, or financial materials in the same analysis task. In prompts, you can ask it to distinguish original facts, inferences, and items to be confirmed, while retaining chapter or paragraph references to make synthesized conclusions easier to trace and discuss further.

Connect images and text across multi-step work

Opus 4.6 can connect code, interface screenshots, and charts to analyze implementation issues behind visible phenomena. Describe the abnormal location, relevant logs, and modification goals together, then ask the model to propose an investigation order and verification points; if execution tools are connected, subsequent analysis should be based on newly returned results.

Applicable Scenarios

Start with specific tasks to find where the model can be effective.

Large-project reviews and troubleshooting

Provide relevant code, error logs, reproduction conditions, and expected behavior, and have the model first list an investigation path, then explain possible root causes and affected modules. It is suitable for delivering code review checklists, candidate patches, regression test recommendations, and migration plans; actual changes should still undergo testing and team review to avoid treating inferences as verified conclusions.

Research materials and contract comparison

Submit contract versions, research texts, or technical specifications, and ask it to compare differences by clause, definition, and evidence. Opus 4.6 can help identify conflicting requirements and organize question lists and argument outlines; retain original locations, and leave legal or business judgments to the appropriate responsible parties for review.

Financial analysis and decision memoranda

Submit financial data, business assumptions, and charts, and ask the model to distinguish data facts, calculation assumptions, and decision recommendations to produce an analytical explanation or management memorandum. For complex tasks, discuss definitions first and then refine conclusions iteratively; when key figures are involved, verify them using calculation tools or spreadsheet results rather than relying solely on textual reasoning.

How to choose this model

Choose based on task complexity, input materials, and expected results.

Compared with Opus 4.5, prioritizes sustained progress on complex tasks

If you already have an Opus 4.5 workflow and the main difficulties are unfamiliar codebases, missed details in long materials, or drifting off course during multi-step tasks, Opus 4.6 is more worth evaluating. Its clearly improved areas are planning, review and debugging, and long-context understanding. Upgrade testing should use real cases and compare issue identification, missed items, and delivery quality, rather than only looking at response length.

Choose based on task difficulty and integration method

Opus 4.6 is suitable for in-depth analysis and complex programming; simple classification, short summaries, or low-latency Q&A may not require equally deep reasoning. Native thinking levels and API parameters should be understood separately and should not be directly interchanged.

Start with a specific task

Based on the characteristics of claude-opus-4-6, first validate small tasks whose results can be checked.

Plan a large codebase fix

You can ask directly: Based on the module description, relevant code, and failing tests, first understand the call relationships, then propose a modification plan. List shared logic and validation points that are easy to overlook, and avoid fixing only local symptoms.

Prepare inputs that support decisions

Provide engineering context and real failure information; verify the plan, scope of changes, and actual test results.

Then integrate it into your workflow

Use the full model ID claude-opus-4-6, first confirm the public request format and available parameters on the API page, then connect the application. Preserve result parsing, exception handling, and relevant evidence, and evaluate with the same set of real samples whether it is suitable for continued use.

Usage boundaries

Before formal use, understand output quality and capability boundaries.

Long-context capability does not mean every detail will be retained accurately. Important terms, cross-file dependencies, and key figures should still be requested for item-by-item citation or verification; when there is a large amount of material, clearly defining the question, scope, and delivery structure first is more conducive to review than simply piling up text.
Deeper reasoning may increase wait time and consumption for simple tasks. For clear, low-risk requests, reduce unnecessary analysis requirements; for complex tasks, generate a plan first and then check in stages, without needing to squeeze all work into a single response.
Code analysis does not mean automatically executing code, and image understanding does not mean generating images. Tool and file access depend on the selected workflow and permissions; authorization boundaries should be set for publishing, writing, or other side-effecting operations, and generated patches must be tested in actual execution.

Frequently Asked Questions

Answers to common questions about using claude-opus-4-6.

What programming tasks is Opus 4.6 better suited for than Opus 4.5?

The focus is on tasks that require planning, exploration, and repeated checking, such as understanding unfamiliar codebases, fixing issues across modules, reviewing changes, and diagnosing complex failures. You can provide the implementation, logs, and testing goals at the same time, and ask it to explain its rationale and validation steps; this better leverages the areas of improvement than simply asking it to continue a piece of code.

Can 1 million context and 128k output be treated directly as the limit for every call?

No. 128k is the officially announced native maximum output, while 1 million context was a Beta capability of the Claude Developer Platform at launch. Actual tasks should arrange input and output according to the available limits of the selected access point, and long-form deliverables can also be split into outline, chapter, and review stages.

What is the difference between Adaptive thinking and effort?

Adaptive thinking lets the model determine when deeper thinking is needed based on the task, while effort adjusts the level of investment. It natively provides four levels—low, medium, high, and max—with high as the default; these are native model control concepts and should not be directly treated as values for identically named parameters in all interfaces.

How do I submit screenshots or PDFs to Opus 4.6?

Prepare document text, table data, or clear page screenshots relevant to the question, and specify whether you need a summary, comparison, or extraction of particular information. Submit them in the content formats supported by the selected public interface; PDF addresses cannot be used as image_url. Ask the results to preserve original-text locations, field evidence, and unconfirmed items, and verify key numbers against the source materials.

How do the two conversation endpoints return results differently?

Applications that already use the OpenAI messages structure can use Chat Completions; when you need Claude-native content blocks, thinking, or tool_use/tool_result workflows, check support for this model in the Messages API. Handle the request, response, and parameter formats separately, and retain the complete model ID.

Use it in your favorite tools

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Input

$1.00per 1M tokens

Official ~~$5.00~~

Output

$5.00per 1M tokens

Official ~~$25.00~~

−80%vs official

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claude-opus-4-6

A deep reasoning model for complex programming and long-form material analysis

Keep your SDK. Connect in minutes.

Specifications and interface features