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deepseek-v3-250324

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deepseek-v3-250324

Date-Version Conversational Model for Long Chinese Text and Front-End 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, front-end code, and function calling, as well as enhanced math and knowledge reasoning evaluations. It is suitable for applications that want to explicitly use this version for content creation, programming assistance, and text analysis.

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DeepSeekModel brand
ChatModel type
ChatTask capability

Specifications and API Features

Clarify capacity, input and output, and invocation methods before selecting a model.

Version positioning
DeepSeek-V3-0324; the invocation ID is deepseek-v3-250324
Input and output
Text message input, generating assistant text; supports multi-turn conversations
Structured capabilities
Natively supports JSON output and Function Calling
Code completion
Natively supports FIM, completing code in the middle based on surrounding context
Generation controls
The chat interface provides temperature（0—2）, top_p, and max_tokens
Response method
The dedicated interface supports stream; hosted sessions return answer and id

Native capabilities describe the model itself; use message organization, generation parameters, and session management according to the selected invocation interface.

Core Capabilities

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

Chinese long-form writing is better suited to iterative refinement

This version's Chinese improvements focus on the quality of medium- and 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 draft into a complete article, rather than merely generating short sentences.

Frontend 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 frontends. 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 deliverable is inspectable implementation text, not an already deployed website.

Connect structured tasks to business workflows

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

Applicable Scenarios

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

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 then continue asking it to preserve data, condense repetitive discussion, or convert it into a formal letter, ultimately obtaining a text draft that is convenient for human review. It is best to retain reference numbers for cited materials to facilitate item-by-item checking of facts against the original text.

Web prototypes and interaction iteration

Provide page requirements, component hierarchy, color schemes, and interaction instructions to generate implementation code for a web page or simple game frontend. Add browser errors, existing code, and expected behavior in follow-up conversations to continue locating issues and adjusting the layout. It is suitable for prototyping and code assistance; runtime testing and dependency checks are still required before release.

Document analysis and field extraction

Organize document text or obtained search results into text, add 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 textual conclusions; whether the information is up to date 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-form Chinese writing, frontend generation, or function calling, the 0324 version is worth trying first. In official comparisons, 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 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 a task requires specialized reasoning control, choose a model that matches that working style separately.

Getting Started

From a small-scale task to formal integration.

01

Prepare Tasks and Materials

Define the goal, required inputs, and output requirements, using real business examples as a starting point.

02

Try It in the API Playground

Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.

03

Integrate According to the API Documentation

Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.

Usage Boundaries

Before formal use, understand output quality and capability boundaries.

  • This model focuses on text understanding and generation. Do not assume that image or audio message fields mean it can directly view images, listen to audio, or generate speech. When processing documents, extract the main text before submitting it; scanned documents need to be converted to text first to avoid having the model guess based on incomplete content.
  • Function calling outputs the intended call and parameters; it does not mean that a dedicated dialogue request 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 provide the environment, constraints, and acceptance criteria, while long-form content 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 about using deepseek-v3-250324.

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

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

Where are its improvements in Chinese writing reflected?

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

How do I call it and continue the previous conversation?

When using /deepseek/chat/completions, submit model and messages, and include the necessary history when following up. When choosing /aichat/conversations or /aichat2/conversations, you can enable stateful and then use the returned id to continue the same conversation.

Can it generate JSON or call business functions?

It supports JSON output and function calling. You can set response_format at 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 result, and business data needs to be validated.

Can it directly search the web or read PDFs?

It can analyze web results and document content prepared as 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 retrieve the relevant content and then provide it to the model; when using conversation tools, distinguish between tool retrieval and model analysis as separate steps.

Model information · Updated: 2026-10-01. Please see the API and pricing sections for call parameters and billing rules.

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