Claude 3.7 Sonnet is a chat model in Anthropic's Sonnet series. claude-3-7-sonnet-20250219 is a date-fixed version that combines reasoning and visual understanding capabilities. It is suitable for organizing text, screenshots, and task constraints into analyses, code suggestions, or document drafts, and is also suitable for existing projects to retain a specific version for regression comparisons; new projects should consider newer Sonnet models in light of its lifecycle.
Clarify capacity, input/output, and invocation methods before selecting a model.
The date version is part of the model identity; image-text messages, streaming responses, and conversation management are invocation methods for the relevant interfaces and are not equivalent to all native capabilities.
Learn what claude-3-7-sonnet-20250219 can bring to your work.
Suitable for placing the problem background, decision criteria, and expected results in the same task, allowing the model to organize analysis steps, compare options, and produce written conclusions. When handling code or business rules, you can ask it to separately list known conditions, assumptions, and items to be verified, making the response easier to review rather than receiving only an unexplained recommendation.
Text and images can be combined as input to ask specific questions about interface screenshots, document screenshots, or charts. Compared with describing the image separately, attaching it directly makes it easier to discuss layout, visible text, and information relationships. Deliverables are still primarily text-based, and you can request a list of issues, chart descriptions, or recommendations for subsequent revisions.
When you need to organize history yourself, you can use Chat Completions messages; when you want to simplify multi-turn interactions, you can use AI Chat v2 to save a conversation and continue the discussion with the same id. Streaming responses are suitable for progressively displaying longer answers, while conversation management makes it easier to continuously advance between drafts, supplementary conditions, and revision feedback.
Start with specific tasks to find where the model can be useful.
Enter relevant code snippets, error messages, and expected behavior, and ask the model to first explain possible causes before providing modification suggestions and testing checklist items. Suitable for organizing a troubleshooting session into a repair draft for discussion; code execution, test runs, and deployment should still be completed in the development environment, and generated suggestions should not be treated directly as verified results.
Submit product screenshots or charts and explain what needs to be checked, such as information hierarchy, field meanings, or whether the presentation is easy to misunderstand. Ask the model to describe issues by location and separate visible information from inferences, ultimately producing a review checklist or explanatory draft. It is best to also provide key values as text for item-by-item comparison.
Provide requirement descriptions, meeting notes, or materials delivered through the AI Chat v2 file reading workflow, and ask for organized summaries, action items, and questions requiring clarification. Then add target readers and writing requirements in the same conversation to develop a deliverable draft over multiple rounds. File reading is an API workflow, and whether materials have been successfully parsed should be confirmed separately.
Choose based on task complexity, input materials, and expected results.
If existing prompts, review records, or regression samples were built around Claude 3.7 Sonnet, explicitly using the date ID helps organize same-version comparisons. It should not be mixed with claude-3-7-sonnet-thinking, nor should specifications from later Sonnet versions be applied to it. A fixed version clarifies the test subject, but does not guarantee word-for-word identical responses or long-term availability.
Anthropic has listed this model as retired, with claude-sonnet-4-6 as the officially recommended replacement. New projects should prioritize evaluating later Sonnet versions; existing projects can compare answer quality and API compatibility using real code, image-and-text samples, and multi-turn tasks. Do not judge benefits solely by the version name, and do not reuse old configurations while skipping migration testing.
From a small-scale task to production integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to view the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before production use, understand output quality and capability boundaries.
Answers to common questions when using claude-3-7-sonnet-20250219.
claude-3-7-sonnet-20250219 is the date-pinned model of Claude 3.7 Sonnet, not a general name for subsequent Sonnet models. When calling it, use the full ID to clearly identify the testing and integration target; a fixed date does not mean the output is completely deterministic, nor is it a version name that is continuously upgraded automatically.
They should not be used interchangeably. They are different call IDs, and this page introduces the date-pinned endpoint. Reasoning capability and how Extended Thinking is enabled are not the same concept; if you need dedicated thinking controls, use an endpoint that explicitly supports that feature and revalidate the parameters and returned content.
In Chat Completions, content can be written as an array containing text and image_url; AI Chat v2 uses structured message. In addition to the image, clearly state the question, such as which area to inspect or which fields to extract. The response is image-analysis text, not generated or edited images.
If you already have messages history management logic, choose /v1/chat/completions for a more direct approach; if you want managed multi-turn records, choose /aichat2/conversations and use stateful and id to continue the conversation. Their input organization and response structures differ, so clients should handle them separately rather than only replacing the path.
The officially recommended replacement model is claude-sonnet-4-6. During migration, prioritize comparing code suggestions, screenshot understanding, instruction following, and multi-turn consistency in real tasks, while also checking response parsing and error handling. The old version has entered official retirement status, so you should also verify whether the replacement model can cover core workflows.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with clear goals and judge whether it suits your work based on real results.
Input
$0.60per 1M tokens
Official $3.00
Output
$3.00per 1M tokens
Official $15.00
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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