DeepSeek: DeepSeek V3

DeepSeek V3 is a text generation model from DeepSeek, listed on api14 under the model id deepseek/deepseek-chat. It is the latest model from the DeepSeek team, building on the instruction following and coding abilities of earlier versions in the family. The provider released it on 2024-12-26, and it was pre-trained on nearly 15 trillion tokens. Its knowledge cutoff is 2024-07-31, so anything after that date is outside what the model learned during training.

The model works with text only, both as input and as output, so it does not accept images or audio. It offers a context window of 163,840 tokens, which is large enough to hold long documents, extended conversations or sizeable code files in a single request. The maximum output is 16,384 tokens per response. Because it is a general text model with strong instruction following and coding ability, it fits tasks that involve reading and producing language or code rather than multimodal work.

Practical uses follow directly from those traits. Long context plus text output suits document summarisation, question answering over lengthy source material, and multi turn chat where earlier turns need to stay in view. The coding background makes it a reasonable choice for code generation, explanation and refactoring, and for working through a repository sized chunk of source in one prompt. Instruction following supports extraction, rewriting, classification and other structured text tasks, and the tool calling parameters make it usable as the reasoning step in an agent loop that needs to call external functions.

You call it through api14's OpenAI-compatible API using the model id deepseek/deepseek-chat, so existing OpenAI client libraries work with only the base URL and key changed. The request parameters it supports are frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. In practice that covers sampling control (temperature, top_p, top_k, min_p), penalties for repetition, deterministic runs via seed, output shaping with response_format and structured_outputs, and function calling through tools and tool_choice.

Two limits are worth noting before you commit. Input and output are text only, so if your task needs image or audio understanding, a multimodal model is the better fit. The knowledge cutoff of 2024-07-31 means the model has no built in awareness of later events, so for time sensitive questions you should supply current information in the prompt or pair it with retrieval.