The Claude Sonnet 4 date-fixed version is Anthropic's general-purpose conversational model, balancing text reasoning and visual understanding. It is suitable for code analysis, material organization, and screenshot interpretation. claude-sonnet-4-20250514 explicitly specifies this version, making it easier to maintain existing applications and testing baselines. Applications can be integrated using the public request format in this page's API section.
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.
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-sonnet-4-20250514",
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.
Clarify capacity, input/output, and invocation methods before choosing a model.
200K is Sonnet 4's publicly available native standard context. Applications organize relevant history in messages, and handle images and streaming returns according to the selected protocol; actual requests must still meet platform limitations.
Learn what claude-sonnet-4-20250514 can bring to your work.
For text tasks involving multiple conditions, Sonnet 4 can organize responses by “conditions, assessment, and conclusions.” It is well suited to organizing requirements, explaining solutions, and analyzing code logic. Clearly requesting assumptions and items requiring confirmation helps turn recommendations into reviewable work materials rather than merely receiving a general response.
Submit interface screenshots, charts, or document images together with questions to interpret their visible content. For example, identify anomalies in a form, explain chart trends, or turn a screenshot into a text checklist. Images provide the subject of observation, while text defines the task and evaluation criteria; the final output remains primarily text-based analysis.
Existing Sonnet 4 applications can continue to maintain configurations and testing baselines by precise version. Chat Completions can retain OpenAI-style messages; Messages uses native system prompts and content blocks. When migrating protocols, check system prompts, tool returns, and response parsing rather than only changing the URL.
Start with specific tasks to find where the model can be effective.
Provide relevant code, error messages, and expected behavior, and have the model first explain the failure path before offering modification suggestions and a testing checklist. Deliverables can include code snippets, issue descriptions, and regression check items. Suitable for assisting developers with reviews; whether a repair succeeds should still be confirmed by actual execution and test results.
Provide business charts or product screenshots, specify the metrics, controls, or anomalous areas that need attention, and have the model produce an observation checklist and explanations. Suitable for turning visual materials into discussion drafts, ticket descriptions, or initial report drafts. For small text, crop key areas first to reduce interference from irrelevant parts of the image.
Use product specifications, research notes, or report excerpts as discussion materials, answer specified questions first, then compare different explanations based on feedback. Attach supporting evidence to each conclusion, and list items requiring confirmation when facts are missing; this type of continuous Q&A is suitable for knowledge assistants and also helps maintain an application's original answer baseline.
Choose based on task complexity, input materials, and expected results.
If an application has already established prompt templates, test samples, and output review processes around Sonnet 4, this date ID can be used to clearly identify the original version and is not equivalent to Sonnet 4.5 or 4.6. Anthropic retired this version from the Claude API on June 15, 2026; this platform's two entry points still explicitly list this ID, but being listed in the catalog does not constitute a commitment to continued availability. Existing projects should retain regression tests and prepare a migration plan; a fixed version also does not mean that the same question will generate exactly identical wording every time.
Sonnet 4 has a public standard context of 200k tokens, so tasks should not be designed around million-scale context. Sonnet 4.6 natively supports 1M context, making it a candidate for tasks involving very long materials. New projects should compare results using their own code, images, and document samples rather than directly applying newer-version specifications to this date version.
Based on the characteristics of claude-sonnet-4-20250514, first validate small tasks whose results can be checked.
You can ask directly: Compare this interface implementation against the specification item by item, and list behavioral inconsistencies, error-handling gaps, and testing recommendations. Review only the given version, without introducing assumptions about newer API versions.
Specify the code version and protocol version; when maintaining compatibility, check current platform access and actual returned results.
Use the full model ID claude-sonnet-4-20250514, 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 whether it is suitable for continued use with the same set of real samples.
Before formal use, understand the range of output quality and capabilities.
Answers to common questions when using claude-sonnet-4-20250514.
No. claude-sonnet-4-20250514 specifies the Claude Sonnet 4 dated version and is used separately from Sonnet 4.5 and 4.6. When changing versions, retest prompt templates, image-and-text tasks, and output formats; do not assume behavior is exactly the same just by changing the model name.
Yes. Chat Completions content can combine text and image_url blocks, and messages in the conversation endpoint can also combine text and images. Clearly specify the area of focus and the desired deliverable in the text, for example, ask for a list of anomalous fields rather than simply asking, “How is this image?”
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 content in formats supported by the selected public API; a PDF URL cannot be used as image_url. Require results to retain original locations, field evidence, and unconfirmed items, and verify critical numbers against the source material.
Include user and assistant messages relevant to the current task in Messages or Chat Completions messages, using the format required by the selected public API. Retain the latest code, interim conclusions, and important constraints; when necessary, summarize long histories again to avoid relying on outdated information.
You should not plan Sonnet 4 tasks around that capacity. Its public standard context is 200k tokens, and the historical million-token Beta should not be used as a basis for application design. For large-scale material, extract it in segments first and then consolidate the answers; if truly long context is needed, compare related versions such as Sonnet 4.6.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗
↗Claude Code CLI Setup Guide
Coding tool setup · requires a Coding Plan application key
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and model↗Input
$0.60per 1M tokens
Official $3.00
Output
$3.00per 1M tokens
Official $15.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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