AI model by Cohere
Cohere Embed v4 for business
Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Reviewed on September 24, 2026
- Cohere
Capability tiersRelative, not benchmarks
Key facts about Cohere Embed v4
- Model ID at review
- embed-v4.0
- Provider
- Cohere
- Main category
- Embeddings
- Open weights
- No, available as a hosted service
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Enterprise search across text and images.
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Embedding mixed documents such as slides and PDFs with images.
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Long inputs, up to 128K tokens.
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Flexible vector sizes from 256 to 1,536.
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Pairing with Cohere Rerank.
Not the right choice for
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Generating answers on its own.
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Self-hosting, since it is not open weight.
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Projects that cannot re-embed if they switch later.
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Audio or video.
Use cases
Business use cases we would use it for
Document search
Slide and PDF search
Our notes
When we would choose it
Embed v4 is Cohere's current embedding model. It accepts text, images and mixed text-and-image inputs, handles inputs of up to 128K tokens and produces vectors from 256 to 1,536 dimensions. Cohere positions it for multimodal enterprise search and retrieval.
The long input limit and mixed inputs are its standout features. Many business documents are not plain text: slides, scanned forms and PDFs with charts. Embedding them directly, rather than extracting text first, can improve search on exactly the documents that are hardest to handle.
In a knowledge assistant, we would pair Embed v4 with Cohere Rerank and a language model such as Command A+ or Claude Sonnet. Keyword search alongside embeddings helps with product codes and names.
For comparison, Gemini Embedding 2 also embeds images and adds audio and video, while Voyage 4 and OpenAI embeddings focus on text. Test two on your own questions before choosing.
We did not verify pricing on Cohere's own site at review time, so the price level is an estimate. Confirm it before estimating the cost of embedding a large collection. A small pilot on a few thousand real documents gives a reliable cost and quality estimate before you commit to the full archive. See embedding models.
Before you commit
Things to check before you commit
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Retrieval test
Measure on real questions against your current search.
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Vector size
Choose a dimension that balances quality and storage.
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Pricing
We saw pricing only on third-party sites. Check Cohere directly.
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Switching cost
Changing models later means re-embedding everything.
Alternatives
Models to compare it with
Cohere Rerank 4
Cohere's multilingual rerank models, which sort search results by relevance, in a best-quality Pro version and a low-latency Fast version.
Gemini Embedding 2
Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
Voyage 4
Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
Keep exploring
Solutions, services and guides
Related services
View all related services- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
Solutions
View all solutions- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
Industries
View all industries- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
Case studies
View all case studiesGuides and articles
View all guides and articles- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
MCP servers
View all mcp servers- Chroma Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
- Confluence Atlassian's official Rovo MCP server lets AI search and read Confluence spaces and pages, create and edit content and add comments and labels.
- Memory The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
- Obsidian A community MCP server that lets AI list, read, search, edit, append to and delete notes in an Obsidian vault through the Local REST API plugin.
- Pinecone Pinecone's official developer MCP server lets AI search Pinecone docs, manage indexes, upsert records, search and rerank.
- Qdrant Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
Glossary terms
View all glossary terms- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Chatbot A chatbot is software that holds a conversation with people through text or voice, answering questions or completing simple tasks.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Yes. It accepts text, images and mixed inputs.
Up to 128K tokens, according to Cohere.
From 256 to 1,536 dimensions.