Fixed-version conversational model for complex reasoning and image-text analysis
claude-opus-4-20250514 is a date-fixed version of Anthropic Claude Opus 4, designed for tasks that require combining context, breaking down conditions, and forming written conclusions. It has reasoning and visual capabilities, allowing text, screenshots, and charts to be included in the same analysis workflow, making it suitable for complex problem discussions, code reviews, and material organization. The fixed model is convenient for maintaining existing applications and conducting version comparisons, but it is not equivalent to later Opus models.
Clarify capacity, input and output, and invocation methods before selecting a model.
Version identity
Claude Opus 4 date-fixed version: claude-opus-4-20250514
Core capabilities
Text chat, reasoning, visual understanding
Image-text input
Text messages and image_url image content blocks
Result format
Text responses; Chat Completions returns choices/message
Invocation endpoints
/v1/chat/completions、/aichat2/conversations
Sessions and streaming
Supports streaming reception; AI Chat v2 can continue sessions through stateful and id
Version identity is part of the model specification, while message format, session persistence, and response format are invocation methods for the corresponding platform endpoints.
Core Capabilities
Learn what claude-opus-4-20250514 can bring to your work.
Turn complex conditions into clear conclusions
For reasoning tasks, you can submit goals, constraints, existing judgments, and unresolved questions together, allowing the model to break down problems by condition, compare options, and form explanations. This is better suited to analyses that need to explain the basis for judgments rather than simply provide short question-and-answer responses; asking answers to distinguish known facts, assumptions, and items to be verified also makes human review easier.
Make images part of the discussion
Visual inputs can be combined with text questions, such as attaching an interface screenshot and asking about the location of an anomaly, or discussing trends and presentation meaning around a chart. The output remains text analysis, not image generation. When submitting, describing the area of focus, business context, and expected answer format can make the discussion more relevant to the specific task.
Structure conversations according to application needs
Applications with existing message-management logic can use Chat Completions to supply conversation history themselves and receive responses; applications that want to save sessions can use AI Chat v2 and continue the discussion through the returned id. The two approaches are suitable respectively for fine-grained message control and reducing session-management work, and both can use streaming display.
Use Cases
Start with specific tasks to find where the model can be effective.
Code review and troubleshooting discussions
Provide the relevant code, error logs, expected behavior, and runtime environment, and ask the model to organize possible causes, suggested changes, and verification steps. Deliverables can be set as review comments or a troubleshooting checklist, with test results added in successive rounds. The focus here is analysis and recommendations; actually running code and confirming fixes are still completed through the development process.
Screenshot and chart interpretation
Provide report screenshots, product interfaces, or flowcharts, and clearly specify the metrics, interaction issues, or logical relationships you want explained, so the model can produce observation notes and a list of questions. For local details, you can crop them separately and continue asking; when accurate numbers are needed, it is best to also provide text data to avoid treating visual estimates as exact values.
Iterative proposal refinement
Submit requirement descriptions, candidate proposals, and constraints as text materials, first obtaining a comparison framework, then adding budgets, dependencies, and exceptions in successive rounds to form a decision memo. Using saved sessions allows continuous revision around the same issue, but each round should still clearly state the new conditions and the content you expect to receive this time.
How to choose this model
Choose based on task complexity, input materials, and expected results.
Choose it when you already have a fixed-version task
If your application has already built prompts, acceptance examples, and output-processing logic around claude-opus-4-20250514, using the exact ID helps keep the version selection clear. It is not a compatible alias for Opus 4.1 or later Opus models; when switching models, compare answer quality, format stability, and tool-interaction performance again rather than assuming results will remain unchanged.
How to weigh new projects and version upgrades
This version is better suited to maintaining existing tasks and comparing historical versions; new projects should also evaluate newer Opus models. Anthropic has retired it from the native Claude API and recommends Opus 4.8. Longer context, image processing, or reasoning controls in later versions cannot automatically be considered capabilities of this version; model selection should be based on real task examples.
Get started
From a small-scale task to production integration.
01
Prepare tasks and materials
Define the objective, required inputs, and output requirements, using real business examples as a starting point.
02
Try it in the API testing area
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
03
Integrate according to the API documentation
Keep the full model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Usage boundaries
Before production use, understand output quality and the scope of capabilities.
A fixed version does not automatically gain new features as the Opus series is updated. Do not apply later models' long context, high-resolution images, or new reasoning tiers to this version; for longer materials, segment them by task, define the scope of analysis first, and then consolidate conclusions from each part.
Visual understanding is suitable for helping interpret screenshots and charts, but should not replace precise data verification. When text is too small, images are blurry, or key areas are obscured, provide clearer images or text records; retain a human review step for amounts, coordinates, and subtle differences.
Reasoning capability does not mean requests will automatically run code, click interfaces, or perform external writes. File reading and tool operations require appropriate workflows and authorization; modification plans provided by the model should be tested, and tool execution results should be checked separately from the final text response.
Frequently Asked Questions
Answers to common questions about using claude-opus-4-20250514.
How is this ID related to Claude Opus 4?
claude-opus-4-20250514 is a date-pinned version of Claude Opus 4, with the date suffix used to clearly identify the version. It is not a name that “always uses the latest Opus,” nor is it an alias for Opus 4.1 or Opus 4.8. It is suitable for applications that need to explicitly record the model version.
Can it understand images and generate images too?
It has visual understanding capabilities and can combine images with text questions to produce analysis, such as explaining screenshots, discussing charts, or describing scenes. This image-and-text workflow produces text answers and does not involve image generation; when submitting images, you should also specify what to examine and what conclusion is needed.
How can I continue discussing the same question?
When using Chat Completions, have the application continue submitting the required messages history; when using AI Chat v2, you can enable stateful and include the conversation id in subsequent requests. The former makes it easier to control context, while the latter facilitates ongoing conversations. It is best to clearly state any new conditions in the current question.
Can I put a PDF directly into an image message?
Do not use a PDF address as image_url. Image-and-text messages in Chat Completions use image content blocks; when files need to be submitted, AI Chat v2 provides file_url file blocks, which are handled by the file-reading workflow. File content entering analysis and the model directly receiving images are different input methods.
Does having reasoning capabilities mean I can set a native thinking budget?
Reasoning capabilities and thinking budget parameters are two different things. When using this version for complex analysis, you can clearly specify goals, constraints, and review requirements in the prompt, but do not directly interpret the shared reasoning_effort field as Anthropic's native thinking budget, nor do you need to rely on displaying thinking content to judge answer quality.
Model information · Updated: 2026-10-01. For invocation parameters and billing rules, see the API and pricing sections.
