Cohere Embed v4 vs embed-english-v3.0

Cohere Embed v4 and embed-english-v3.0 are both embedding models tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Cohere Embed v4

Cohere Embed v4 transforms text, images, and mixed content into unified vector representations for semantic search, RAG, document clustering, and similarity matching. The model supports 1,536-dimensional embeddings with flexible compression via Matryoshka embeddings (256, 512, 1024, 1536 dimensions). Priced at $0.12/1M text tokens and $0.47/1M image tokens, it delivers multimodal capabilities competitive with text-only alternatives. The API supports batch processing up to 128,000 tokens per request with asymmetric search optimization. Limitation: incompatible with v3 embeddings; corpus re-embedding required for upgrades.

embed-english-v3.0

embed-english-v3.0 generates semantic embeddings from English text, producing 1,024-dimensional vectors suitable for retrieval-augmented generation, classification, clustering, and semantic search tasks. It achieves state-of-the-art performance on MTEB and BEIR benchmarks and was trained on approximately 1 billion English training pairs. The model supports batches of up to 96 inputs with 512 tokens maximum per input, and supports both text and image embedding. Pricing is $0.10 per million tokens. A notable limitation is that it requires explicit input_type specification to differentiate between search documents, queries, classification, and clustering tasks.

Attribute	Cohere Embed v4	embed-english-v3.0
Pricing	Paid	Paid
Price	$0.12 per 1M text tokens; $0.47 per 1M image tokens	$0.10 per million tokens
Free trial	0 days	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	Cohere Platform, AWS Bedrock, Azure AI Foundry, Amazon SageMaker, GitHub Models	Cohere API, AWS SageMaker, Azure AI Foundry, OCI Generative AI
Languages	English and 100+ languages for text input; English for image input	English (primary); text-image multimodal support
Released	2025-04-15	2024
Pros	Unified multimodal model reduces infrastructure complexity Competitive pricing at $0.12/1M tokens for text embeddings Flexible output dimensions (256-1536) via Matryoshka embeddings reduce storage and latency Strong MTEB performance (65.2) with 35% cross-lingual retrieval improvement Supports asymmetric search for optimized query-document retrieval	State-of-the-art performance on MTEB and BEIR benchmarks Highly cost-efficient at $0.10 per million tokens Supports multimodal input (text and images) with unified embeddings Batch processing up to 96 inputs per request Multiple embedding output formats (float, int8, uint8, binary, base64)
Cons	Embed v4 vectors incompatible with v3; requires full corpus re-embedding for migrations Image pricing ($0.47/1M tokens) is higher than text and limits image-heavy workloads Trial keys rate-limited and unusable for production, requiring immediate production key conversion	English-optimized only; use embed-multilingual-v3.0 for multilingual needs 512-token limit per input may truncate long documents Requires explicit input_type specification for optimal results

Bottom line

Cohere Embed v4 and embed-english-v3.0 are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.