deepseek-v4.1-flash is the compatible invocation name for this platform's DeepSeek Flash tier, and the original deepseek-v4-flash name continues to be supported. It is suitable for text Q&A, ticket categorization, document organization, and code assistance in existing Flash applications. When selecting it, first verify the invocation ID, actual responses, and task performance; the compatible name itself does not represent an independent official architecture or new visual capabilities.
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="deepseek-v4.1-flash",
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.
First, clarify this model's input and standard invocation method.
Native model characteristics are used for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Use stream for continuous output from Chat Completions, and the client is responsible for preserving message history.
Learn what deepseek-v4.1-flash can bring to your work.
Put relevant code, change snippets, and requirement descriptions into the same set of messages, then continue asking about risks, implementation approaches, or test plans. It is better suited to engineering assistance tasks with clear boundaries: require answers to identify related files, explain the reasons for changes, and distinguish items to be verified from recommendations, making it easier for developers to review them one by one.
Provide document content and an organization goal for summarization, classification, field extraction, or item comparison. When results need to be consumed programmatically, first specify field names, missing-value handling, and output format, then use appropriate format controls. This can turn open-ended responses into content that is easier to validate, store, and process further.
This name is a compatible invocation ID for Flash in the platform guide. During migration, first confirm the application's model configuration, message format, and downstream result handling, then check real task performance; do not treat the minor version number in the name as evidence of new native capabilities.
Start with specific tasks to find where the model can be effective.
Submit code differences, API contracts, and known failures, and request a risk list, modification suggestions, and a draft of test cases. Add logs afterward to continue diagnosing the issue. Deliverables should retain the corresponding code locations and verification steps so engineers can actually perform the checks, rather than receiving only general code evaluations.
Input contract clauses, ticket content, or product descriptions, specify the topics, fields, and decision rules to extract, and generate summaries, comparison tables, or JSON drafts. When materials have missing items, require them to be explicitly left blank and retain the original-text basis; then perform business validation on amounts, dates, and key conclusions before passing them to subsequent processes.
Retain the versions of materials submitted to deepseek-v4.1-flash and the actual responses, distinguishing original facts, model suggestions, and actions already completed by the application. Before structured results enter the system, check required fields, value types, and business rules to avoid directly turning missing information into definitive records.
Choose based on task complexity, input materials, and expected results.
deepseek-v4.1-flash and deepseek-v4-flash are both available invocation IDs, with the former serving as a compatibility endpoint. Existing applications do not need to switch entirely just because the name changes; use the same code review, field extraction, and multi-turn samples to compare instruction following, response completeness, and format validity, then decide whether to adjust. Do not interpret the same billing tier as meaning completely identical output.
Use /v1/chat/completions and explicitly set model=deepseek-v4.1-flash. Existing OpenAI-compatible applications can continue using their messages and result handling; when integrating, configure the platform address, API Key, and exact model ID.
Prepare the inputs first, then connect them to the corresponding application workflow.
Keep the current Flash requests and a set of real samples covering short Q&A, summaries, code modifications, and invalid inputs.
Explicitly select deepseek-v4.1-flash in the Chat Completions request, organizing the context, materials, and output requirements for this request into messages. First use a clearly scoped task to check the response, then put actual review or test feedback into the next round of messages.
Design tasks directly from the following inputs and acceptance priorities.
Please classify the following tickets as account, billing, functionality, or other, and return the category, original-text evidence, and a flag indicating manual handling is needed.
Check the request, response fields, and classification results against the original call name; compatibility for this ID does not mean automatically gaining different visual capabilities.
Before formal use, understand output quality and capability scope.
Answers to common questions when using deepseek-v4.1-flash.
On this platform, it is a compatible invocation ID for the DeepSeek Flash tier. When using it, enter the exact deepseek-v4.1-flash, but do not infer the native version, capacity, or upgrade scope from the name alone. It is better suited to selection based on actual results for code, documents, and multi-turn tasks.
Both IDs can continue to be used for invocation and use the same pricing tier. Existing Flash applications can retain their current configuration first, then compare responses, formatting, and usage on the same set of real tasks. The same tier does not mean results will be identical word for word, nor should the V4.1 name alone be taken to mean that every task is improved.
Submit model=deepseek-v4.1-flash and messages to /v1/chat/completions. Read regular results from choices[].message.content; for streaming calls, use stream to obtain incremental results. Use this platform's API Key and set the full base URL according to the SDK you use.
Have the application save the message history, and include the user and assistant messages relevant to the current question in messages. Keep the current Flash request and a set of real examples covering short Q&A, summarization, code changes, and invalid input. When materials or constraints change, update them with the next request.
You can clearly specify fields, types, and missing-value rules in the prompt, and request a JSON draft. When using response_format, select only format types supported by this endpoint; do not treat json_schema fields or strict settings as constraints that will necessarily take effect. Before storing, you must still parse the JSON and check required fields, field types, and business conditions; valid formatting does not mean the content is correct.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗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 modelInput
$0.088per 1M tokens
Official $0.44
Output
$0.26per 1M tokens
Official $1.32
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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