DeepSeek-V3 is DeepSeek's general-purpose language model, suitable for turning natural-language requirements into answers, drafts, 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. On this platform, you can choose Chat Completions to organize messages yourself, or use managed sessions to continue conversations, arranging history and outputs according to application needs.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
The inputs, outputs, and controls above are described according to this platform's invocation methods and do not represent the model's native capacity.
Learn what deepseek-v3 can bring to your work.
DeepSeek-V3 can continue explaining, supplementing, or rewriting based on previous questions and answers, making it suitable for text assistants that need to clarify requirements step by step. By continuing conversations through message history or managed sessions, combined with role definitions and formatting requirements, you can keep the tone, terminology, and delivery structure consistent within the same task.
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 cause analysis, modification plans, and testing suggestions to help developers move forward with subsequent validation.
For summaries, translation, content rewriting, and report drafts, DeepSeek-V3 can transform the expression of provided text. By specifying the audience, retaining terminology, and preserving chapter structure, you can obtain deliverables better suited to the intended use; for fact-sensitive materials, you can request a distinction between conclusions from the original text and model suggestions.
Start with specific tasks to identify where the model can be effective.
Provide 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; when the materials do not cover a question, it should prompt for additional information rather than fill in rules on its own.
Submit relevant functions, the runtime environment, and error logs, and ask the model to analyze possible causes and output candidate fixes 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 declaring an issue resolved without running the code.
Use meeting notes, article text, or report excerpts as text input, asking it to extract themes, action items, or section summaries, then rewrite them for the target audience. When processing large amounts of material, you can first extract key points section by section and then compile them into a document, making it easier to retain critical details and trace each conclusion back to its source text.
Choose based on task complexity, input materials, and expected results.
If your goal is text Q&A, document processing, and code 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 only on the series name. Before switching versions in an existing application, use real tasks to compare format compliance, answer quality, and code usability.
When you need precise control over each message round, history trimming, or incremental responses, choose Chat Completions, with the application maintaining messages. To simplify ongoing exchanges, choose managed sessions and continue tasks through stateful and id. Both approaches support text conversations; the main trade-offs are control over history, client complexity, and response handling.
From a small-scale task to production integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using deepseek-v3.
To call DeepSeek-V3 here, use deepseek-v3. deepseek-chat and models with date suffixes should not be treated as the same version solely because their names are similar. If switching IDs, it is recommended to first run regression tests on typical questions, formatting requirements, and coding tasks, to avoid changes in an application's default behavior.
When using Chat Completions, include relevant user and assistant history in messages. When using managed sessions, enable stateful and return the id in subsequent requests. The former is suitable for fine-grained history management, while the latter is suitable for reducing the client-side work of maintaining messages.
Set stream=true in the request body for /deepseek/chat/completions, and have the client read incremental content from the stream and concatenate it. Receiving [DONE] indicates that the current round 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 to specify whether you want a patch or an explanation. You can request that the response include testing ideas and edge cases, but you still need to run and verify it yourself; generated code is not automatically executed because of a single question and answer.
For this model, prioritize providing the main text or recognized text as input, then request summarization, classification, or rewriting. Attachment fields do not imply native vision or file-understanding capabilities; if the task depends on layout, charts, or image details, choose a processing method suited to that input and retain necessary textual descriptions.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with a clear goal and evaluate whether it fits your work based on real results.
Input
$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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