DeepSeek-V4
DeepSeek-V4 is a next-generation open-source language model designed for high-performance reasoning, coding, and long-context intelligence. It introduces a powerful architecture with up to one million token context length, enabling seamless handling of large datasets and complex multi-step workflows. The model comes in two variants: DeepSeek-V4-Pro for maximum performance and DeepSeek-V4-Flash for efficiency and speed. DeepSeek-V4-Pro features 1.6 trillion total parameters with 49 billion activated, delivering near state-of-the-art performance comparable to leading closed-source models. It excels in agentic coding, mathematical reasoning, and world knowledge tasks. The model integrates advanced attention mechanisms, including token-wise compression and sparse attention, significantly reducing compute and memory costs. It is also optimized for AI agents, supporting tool use and multi-step workflows.
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GLM-OCR
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoderādecoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale imageātext data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.
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DeepSeek-V2
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.
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DeepSeek-VL
DeepSeek-VL is an open source Vision-Language (VL) model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios, including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead.
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