DeepSeek: DeepSeek V3 0324

DeepSeek V3 0324 is a text model from DeepSeek, released by the provider on 24 March 2025. It is the latest iteration of DeepSeek's flagship chat model family and succeeds the earlier DeepSeek V3 model. Under the hood it is a 685B-parameter mixture-of-experts model, which means only part of the network is active for any given token, a design that lets a large model serve chat traffic without activating every parameter on every request. The model id on api14 is deepseek/deepseek-chat-v3-0324.

The model is text in, text out. It accepts text prompts and returns text completions, and it does not take images or audio as input. The context window is 163,840 tokens, which is large enough to hold long documents, multi-file source trees or extended conversation histories in a single request. The maximum output is 115,200 tokens, so responses can be long as well. Its knowledge cutoff is 31 July 2024, meaning events and information after that date are outside what the model learned during training.

Those capabilities point to a fairly wide set of practical uses. The long context suits document analysis, summarisation and question answering over lengthy source material, as well as code review or refactoring across several files at once. Because the model supports tool calling and structured outputs, it also fits agent-style workflows and pipelines where the response must conform to a schema rather than free text. General chat, drafting and editing are natural fits too, given that this is a chat model family. The large output ceiling helps for tasks that produce long artifacts, such as generating a full report or a sizeable code file in one pass.

You call it through api14's OpenAI-compatible API using the model id deepseek/deepseek-chat-v3-0324, so existing OpenAI SDKs and clients work with only the base URL and key changed. The request parameters it supports are frequency_penalty, logprobs, max_tokens, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs and top_p. That covers sampling controls, deterministic runs via seed, token-level probability inspection through logprobs and top_logprobs, and function calling or schema-constrained output through tools, tool_choice, response_format and structured_outputs.

One thing to weigh is modality. If your task depends on images, audio or other non-text inputs, this model cannot handle it, since it is text only. The knowledge cutoff of 31 July 2024 is also worth noting for questions about recent events, where retrieval or a model with a later cutoff may serve you better. Within those bounds, it is a straightforward choice for long-context text and tool-driven work.