Cohere Embed v3vsCohere Embed v4
Cohere vs Cohere — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Cohere Embed v3 | Cohere Embed v4 |
|---|---|---|
| Provider | ||
| Arena Rank | — | — |
| Context Window | 512 tokens | 128K |
| Input Pricing | $0.10/1M tokens/1M tokens | $0.12/1M tokens |
| Output Pricing | N/A (embeddings)/1M tokens | $0.12/1M tokens |
| Parameters | Undisclosed | Undisclosed |
| Open Source | No | No |
| Best For | Search, RAG, semantic similarity, clustering | Semantic search, RAG embeddings, document retrieval |
| Release Date | Nov 2, 2023 | Mar 1, 2025 |
Cohere Embed v3
Cohere Embed v3 is one of the leading text embedding models, supporting over 100 languages with state-of-the-art retrieval performance. It produces dense vector representations of text that power semantic search, RAG pipelines, and classification systems. The model offers specialized embedding types for search queries vs documents, optimizing retrieval accuracy for enterprise applications.
View Cohere profile →Cohere Embed v4
Cohere Embed v4 is Cohere's latest embedding model supporting multimodal inputs for the first time — processing both text and images into unified vector representations. It generates high-quality embeddings for semantic search, RAG pipelines, and clustering applications. The model supports 100+ languages and produces compact, efficient embeddings that work well with vector databases. It represents a significant upgrade over text-only embedding models for building modern search and retrieval systems.
View Cohere profile →Key Differences: Cohere Embed v3 vs Cohere Embed v4
Cohere Embed v4 supports a larger context window (128K), allowing it to process longer documents in a single request.
When to use Cohere Embed v3
- +Budget is a concern and you need cost efficiency
- +Your use case involves search, rag, semantic similarity, clustering
When to use Cohere Embed v4
- +Quality matters more than cost
- +You need to process long documents (128K context)
- +Your use case involves semantic search, rag embeddings, document retrieval
The Verdict
Cohere Embed v4 wins our head-to-head comparison with 2 out of 5 category wins. It's the stronger choice for semantic search, rag embeddings, document retrieval, though Cohere Embed v3 holds an edge in search, rag, semantic similarity, clustering. If cost is your primary concern, Cohere Embed v3 offers better value.
Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages