Cohere Launches Embed 4: New Multimodal Search Model Can Process 200-Page Documents
Updated: April 16, 2025 10:23:23 Source: AIbase
As the AI agent boom continues to heat up, enterprise Retrieval-Augmented Generation (RAG) continues to play a crucial role. Recently, capitalizing on the growing market interest in agents, Cohere, which focuses on enterprise-level AI applications, released its latest embedding model, Embed 4. This model significantly enhances the multimodal capabilities of Embed 3, particularly excelling in processing unstructured data, and features an ultra-long context window of up to 128,000 tokens, theoretically capable of generating embeddings for documents of approximately 200 pages.
Performance Leap: Longer Context and Multimodal Enhancement
Cohere pointed out in its official blog that existing embedding models have inherent deficiencies in understanding complex multimodal enterprise data, forcing companies to perform tedious data preprocessing for limited accuracy improvements. Embed 4 aims to solve this pain point, helping enterprises and their employees efficiently mine critical insights hidden in massive, difficult-to-search information.
Enterprise Applications: Secure, Efficient, Suitable for Multiple Scenarios
According to the introduction, enterprises can deploy Embed 4 on virtual private clouds or within their internal technology stack to enhance data security. By generating embeddings, businesses can transform various documents or other data into numerical representations needed for RAG use cases, which AI agents can reference when responding to user prompts, thereby improving answer accuracy and avoiding “hallucination” phenomena.
Embed 4 claims excellent performance in strictly regulated industries such as finance, healthcare, and manufacturing. Cohere emphasizes that the model fully considers the security requirements of regulated industries and has a profound understanding of enterprise-level applications. Additionally, Embed 4 has been trained on “noisy real-world data,” maintaining high accuracy even when faced with spelling errors and formatting issues common in enterprise data. More notably, the model performs exceptionally well in searching scanned documents and handwritten files without complex preprocessing workflows, significantly saving enterprises time and operational costs. Embed 4 has wide-ranging application scenarios, covering investor presentations, due diligence documents, clinical trial reports, maintenance guides, and product documentation. Like previous versions, the model supports over 100 languages.
Cohere’s customer Agora has already adopted Embed 4 in its AI search engine and found that the model effectively showcases relevant products. Agora founder Param Jaggi stated that e-commerce data is complex, including images and multi-faceted text descriptions, while Embed 4 can present products in a unified embedding form, thereby accelerating search speed and improving the efficiency of internal tools.
Empowering Agents: Improving Accuracy and Efficiency
Cohere believes that models like Embed 4 will greatly improve agent application scenarios and is expected to become the “best search engine” for enterprise-level agents and AI assistants. The company emphasizes that Embed 4 not only demonstrates powerful accuracy across data types but also possesses enterprise-level efficiency, able to scale to meet the needs of large organizations and create compressed data embeddings to reduce storage costs.
Notably, Qodo’s Qodo-Embed-1-1.5B and Voyage AI’s model, recently acquired by MongoDB, are also competitors to Embed 4.
