DeepSeek-V3 is DeepSeek's general-purpose language model, suitable for turning natural-language requirements into answers, documents, code, and summaries. It can serve as an everyday multi-turn assistant and is also well suited for explaining, rewriting, and technically analyzing existing materials.
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.acedata.clouddeepseek-v3import 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-v3",
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 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 Chat Completions output, and the client is responsible for storing message history.
Learn what deepseek-v3 can bring to your work.
V3 can be used for conversations, summaries, translations, and code drafts. Business applications can select different prompting requirements by task while still storing source materials and outputs separately; when new information is needed, provide retrieved text first rather than treating ordinary Q&A as web search.
It can be used for code generation, issue analysis, debugging suggestions, and technical documentation. Providing target behavior, existing code, and error information is more targeted than simply asking to “fix the program”; responses can be organized into root-cause analysis, modification plans, and testing suggestions to help developers move forward with subsequent validation.
For summaries, translations, content rewrites, and report drafts, DeepSeek-V3 can transform expression based on the text provided. Specifying the audience and preserving terminology and chapter structure can produce deliverables better suited to the intended use; for fact-sensitive materials, you can request that original conclusions be distinguished from model suggestions.
Start with specific tasks to identify where the model can be effective.
Enter product documentation, business rules, and user questions, and have the model first organize the user's intent before generating an explanation or reply draft. Use presets or system messages to specify wording and response boundaries, making it suitable for building a continuous consultation experience; for questions not covered by the materials, it should guide users to provide additional information rather than filling in rules on its own.
Submit relevant functions, the runtime environment, and error logs, and ask the model to analyze possible causes and produce repair candidates and verification steps. It can also turn API requirements into sample code or documentation. The delivery focus is on checkable recommendations and code drafts, rather than assuming an issue has been resolved without running the code.
Use meeting notes, article text, or report excerpts as text input, ask it to extract themes, action items, or section summaries, and then rewrite them for the target audience. When processing large amounts of material, first extract key points paragraph by paragraph, then consolidate them into a document, making it easier to retain critical details and trace each conclusion back to its corresponding source text.
Choose based on task complexity, input materials, and expected results.
If the goal is text Q&A, document processing, and coding assistance, deepseek-v3 can be considered as a general-purpose model candidate. It should be evaluated separately from IDs with dates or suffixes, such as deepseek-v3-250324 and deepseek-v3.2-exp; do not assume identical behavior based solely on the series name. Before switching versions in an existing application, compare format compliance, answer quality, and code usability using real tasks.
Keep the version of the materials submitted to deepseek-v3 along with the actual responses, and distinguish between original facts, model recommendations, and actions already completed by the application. Before structured results enter the system, check required fields, value types, and business rules to avoid turning missing information directly into definitive records.
Arrange the inputs first, then connect them to the corresponding application workflow.
Organize product documentation, return and exchange policies, and customer questions, and mark content that cannot be promised.
Explicitly select deepseek-v3 in the Chat Completions request, and organize the background, materials, and requirements for this output into messages. First use a clearly scoped task to check the response, then include actual review or test feedback in the next round of messages.
Design tasks directly from the inputs and acceptance priorities below.
Answer customers based only on the information below. First provide a short sendable draft, then list the terms used and items that customer service still needs to confirm; explain what is missing when information is unavailable.
Check whether the answer invents policies, omits conditions, or changes numbers; keep records of manual edits to improve prompts for subsequent batches.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions about using deepseek-v3.
When calling DeepSeek-V3 here, use deepseek-v3. deepseek-chat and models with date suffixes should not be treated as the same version merely because their names are similar. If you need to switch IDs, it is recommended to first run regression tests on typical questions, formatting requirements, and coding tasks to avoid changes in application default behavior.
Submit model=deepseek-v3 and messages to /v1/chat/completions. For regular results, read from choices[].message.content; for streaming calls, obtain incremental results through stream. Use this platform's API Key and configure the complete base URL according to the SDK you use.
Set stream=true in the request body for /v1/chat/completions, and have the client read and concatenate incremental content from the stream. Receiving [DONE] indicates that the current response has ended. Do not parse the entire response as a single ordinary JSON object, and do not append text that has already been accumulated repeatedly.
It is recommended to provide the language and dependency environment, expected behavior, relevant code, and the complete error message, and specify whether you want a patch or an explanation. You can ask for test ideas and edge cases in the response, but you still need to run and verify it yourself; generated code will not execute automatically because of a single question-and-answer exchange.
For this model, prioritize using the main text or recognized text as input, then request summarization, classification, or rewriting. Attachment fields do not equal native visual or file-understanding capabilities; if the task depends on layout, charts, or image details, choose an appropriate processing method for that input and retain necessary textual descriptions.
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
$0.24per 1M tokens
Official $0.27
Output
$0.96per 1M tokens
Official $1.10
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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