V4 PRO

Built for high-throughput, long-context, and multimodal agent workloads: it handles text and images in a 1M-token context and supports thinking mode, JSON output, and tool calls. Its emphasis is lower latency and cost efficiency; raise reasoning effort when a task calls for more complex planning.

An experimental multimodal variant of DeepSeek-V4-Flash for image understanding alongside text. A practical choice for visual Q&A, screenshot and document analysis, while its experimental status makes it better suited to evaluation and flexible workflows than strict production-critical paths.

Built for reasoning, coding, and agentic tool use, with stronger long-context, thinking-mode, and complex-task execution than DeepSeek V3.2. Compared with Chinese peers such as Qwen, GLM, and Kimi, it is especially competitive for math, programming, and logical reasoning tasks.

A multimodal embedding model for RAG and search infrastructure, not a chat model. It supports 32K-token text inputs and maps text, images, and visual documents into a shared vector space for knowledge-base retrieval, cross-modal/document search, code retrieval, and semantic matching; dense embeddings default to 2048 dimensions and can be truncated to reduce storage cost.