deepseek-v3-250324

Name: deepseek-v3-250324
Brand: DeepSeek

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deepseek-v3-250324

Date-Version Conversational Model for Long Chinese Text and Frontend Development

deepseek-v3-250324 corresponds to DeepSeek-V3-0324 and is a date-fixed update of the V3 series. It retains the V3 model architecture, with key improvements in medium- and long-form Chinese writing, interactive rewriting, frontend code, and function calling, as well as better performance in math and knowledge reasoning evaluations. It is suitable for applications that specifically want to use this version for content creation, programming assistance, and text analysis.

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DeepSeekModel brand

ChatModel type

ChatTask capability

STANDARD APIs · QUICK SETUP

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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 host: api.acedata.cloud
model: deepseek-v3-250324

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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-v3-250324",
    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.

Specifications and API Features

First, clarify this model's input and standard calling method.

Model ID: deepseek-v3-250324
Input and Output: Text message input; assistant text output
Standard API: POST /v1/chat/completions; submit model and messages
Reading Results: choices[].message.content; usage provides usage statistics
Multi-turn Conversations: The application passes relevant history and the current question in messages
Version Features: Corresponds to DeepSeek-V3-0324, with key improvements in Chinese writing, frontend generation, and function calling

Native model features 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 output from Chat Completions, and the client is responsible for preserving message history.

Core Capabilities

Learn what deepseek-v3-250324 can bring to your work.

Better suited for iterative refinement of long Chinese-form writing

This version's Chinese improvements focus on the quality of medium- to long-form content and multi-turn interactive rewriting, while also optimizing translation and letter writing. You can first provide the audience, outline, and tone, then request paragraph adjustments, tighter wording, or retention of key arguments in successive rounds. It is suitable for gradually turning a first draft into a complete article, rather than merely generating short sentences.

Front-end code balances functionality and visual appeal

Compared with the initial V3 version, the 0324 version focuses on improving code executability and enhancing the visual presentation of web and game front ends. After entering the page structure, interaction rules, and styling requirements, you can have it generate code and then continue revising it based on runtime errors; the delivery focus is inspectable implementation text, not an already deployed website.

Integrate structured tasks into business workflows

The model supports JSON output and function calling, and this version also improves the accuracy of function selection. When used for information extraction, you can specify field meanings and missing-value rules; when used for tool collaboration, you can define function names and parameters, then have the application execute calls and populate the results, grounding responses in actual business data.

Applicable Scenarios

Start with specific tasks to find where the model can be effective.

Report rewriting and Chinese editing

Enter the report body, target readers, and revision requirements, and let the model organize an outline, rewrite paragraphs, and standardize terminology. You can subsequently request that it retain data, condense repetitive discussion, or turn it into a formal letter, ultimately producing a text draft that is convenient for human review. It is best to retain numbering for source materials to facilitate item-by-item checking of correspondence between facts and the original text.

Web prototypes and interaction iteration

Provide page requirements, component hierarchy, color scheme, and interaction instructions to generate implementation code for a website or simple game front end. Add browser errors, existing code, and expected behavior to subsequent conversations to continue locating issues and adjusting the layout. It is suitable for prototyping and code assistance, but runtime testing and dependency checks are still required before release.

Document analysis and field extraction

Organize document text or already obtained search results into text, attach clear questions and output fields, and generate summaries, comparison tables, or JSON records. For multiple materials, you can request citations by material number and distinguish facts from inferences. It is particularly suitable for converting materials into written conclusions; whether information is current depends on the input materials.

How to choose this model

Choose based on task complexity, input materials, and expected results.

Focus on specific tasks when switching from the initial V3 version

If your main tasks are long Chinese texts, frontend generation, or function calling, the 0324 version is worth trying first. In the official comparison, MMLU-Pro rose from 75.9 to 81.2, and LiveCodeBench rose from 39.2 to 49.2, indicating that the update involves more than just writing style. Evaluation scores are not project success rates, so compare using your own articles, code, and tool definitions.

Choose the date-specific version and the series name separately

When you need to maintain prompts and evaluation sets around the same version, explicitly use deepseek-v3-250324, and do not treat the undated series name as its synonym. Its writing style is closer to R1, but it is not equivalent to DeepSeek-R1; if the task requires specialized reasoning control, choose a model that matches that working style separately.

Get started: turn Chinese requirements into a runnable page

Plan the input first, then connect it to the appropriate application workflow.

Prepare the input

Prepare the page functionality, target framework, copy, and interaction states; specify component and dependency constraints.

Organize the call and subsequent workflow

Explicitly select deepseek-v3-250324 in the Chat Completions request, and organize the background, materials, and output requirements for this run 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.

Practical task example: turn Chinese requirements into a runnable page

Design the task directly from the following inputs and acceptance priorities.

Suggested task

Please implement an order list using existing components, including filtering, empty, loading, and error states; explain the structure first, then provide the complete code, without adding dependencies.

Key checks

Run the build and actually click filtering and retry, checking the copy, layout, and state transitions; then request one round of revisions for the issues found.

Usage Boundaries

Before formal use, understand the output quality and capability scope.

This model focuses on text understanding and generation. Image or audio message fields should not be interpreted as meaning it can directly view images, listen to audio, or generate speech. When processing documents, extract the main text before submission; scanned documents need to be converted to text first to avoid having the model guess based on incomplete content.
Function-calling output represents the intended call and parameters; it does not mean that dedicated chat requests will automatically execute programs, access business systems, or click interfaces. Applications need to execute tools, validate parameters, and return results; for write operations, permissions and confirmation steps should be configured.
Improvements in frontend quality and reasoning performance do not mean code requires no testing or that mathematical answers are necessarily correct. Complex tasks should specify the environment, constraints, and acceptance criteria, and long texts should retain key evidence; JSON results should also be checked for fields and types to avoid using them directly as trusted business records.

Frequently Asked Questions

Answers to common questions when using deepseek-v3-250324.

What is the relationship between deepseek-v3-250324 and DeepSeek-V3-0324?

The former is the model ID used when making calls, while the latter is DeepSeek's publicly released native model name. It is a date-based update of V3, with the same model architecture as the initial V3, but with improvements to writing, frontend development, reasoning, and function calling. It should not be confused with other date-based V3 versions.

Where are its improvements in Chinese writing reflected?

The focus is on style and content quality for medium- and long-form writing, as well as continuous multi-turn rewriting, translation, and letter writing. When using it, first specify the audience, style, and information that must be retained, then refine the structure and wording over successive turns. Its style is closer to R1, but this does not mean it is an R1 reasoning model.

How do I call deepseek-v3-250324 with the standard API?

Submit model=deepseek-v3-250324 and messages to /v1/chat/completions. Read ordinary 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 in use.

Can it generate JSON or call business functions?

It supports JSON output and function calling. You can set response_format in the dedicated endpoint, or provide tools and function parameter definitions. The prompt should still specify field requirements; after receiving tool_calls, the application executes the function and submits the results, and business data must be validated.

Can it directly search the web or read PDFs?

It can analyze web results and document bodies that have been organized into text, but text analysis does not mean the model can independently access the internet or natively parse PDFs. When real-time information is needed, first obtain the relevant content and then provide it to the model; when integrating reading tools, distinguish between the tool obtaining the body text and the model analyzing it.

Use it in your favorite tools

Choose a development approach or application suited to this model and follow its setup guide.

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deepseek-v3-250324

Date-Version Conversational Model for Long Chinese Text and Frontend Development

Keep your SDK. Connect in minutes.

Specifications and API Features

Core Capabilities

Better suited for iterative refinement of long Chinese-form writing

Front-end code balances functionality and visual appeal

Integrate structured tasks into business workflows