DeepSeek: DeepSeek V4 Pro 0423

DeepSeek V4 Pro 0423 is a text model from DeepSeek, identified on api14 as deepseek/deepseek-v4-pro. It is a large-scale Mixture-of-Experts model with 1.6T total parameters and 49B activated parameters. The provider release date is 2026-04-24. It sits in the text category, so it is aimed at language work rather than image or audio processing.

The model takes text in and returns text out. Its context window is 1,048,576 tokens, and the maximum output is 384,000 tokens, so both very long inputs and very long generations are possible within those bounds. DeepSeek describes it as designed for advanced reasoning and coding. The supported parameters include reasoning controls (reasoning, reasoning_effort, include_reasoning), sampling controls (temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, seed, stop), and output shaping options (response_format, structured_outputs, logprobs, top_logprobs, logit_bias). Tool use is available through tools and tool_choice, and generation length is set with max_tokens or max_completion_tokens.

Those capabilities point to a few practical uses. The long context suits work over large codebases, long documents, or extended conversation histories where you would otherwise need to chunk and retrieve. The reasoning parameters fit tasks that benefit from a deliberate thinking phase, such as multi-step analysis, debugging, or planning. Coding support covers generation, refactoring, and review of source files. Structured outputs and tool calling make it usable as the decision layer in an agent or pipeline, where the model must return machine-readable results or invoke functions. Logprobs and logit_bias are there for developers who need to inspect or steer token-level behaviour.

You call it through api14's OpenAI-compatible API using the model id deepseek/deepseek-v4-pro. Because the interface follows the OpenAI chat completions shape, existing OpenAI SDKs and clients work with a change of base URL and key. Pass the model id in the request, put your instructions in the messages array, and set any of the supported parameters listed above. Reasoning is controlled with reasoning and reasoning_effort, with include_reasoning deciding whether reasoning content comes back. Use response_format or structured_outputs when you need a defined output shape, and tools with tool_choice when the model should call functions.

One thing to keep in mind is that this is a text-only model: it does not accept images or audio, so a multimodal model is a better fit if your input includes those. The context window is large but finite, and the output cap of 384,000 tokens applies per request. If your task is simple classification or short replies, a smaller model may be cheaper and faster, but the facts here do not include pricing or latency figures, so check the live price table on this page before deciding.