Cohere

Cohere Embedding Cost Calculator

Calculate Cohere embed-v4 costs at scale. Cohere's enterprise-focused embeddings support multilingual and multimodal inputs - calculate your cost per million tokens.

Embedding Cost Calculator

Enter your workload details below

Total documents to embed

Typical document length in tokens (~750 words = 1000 tokens)

Search/retrieval queries per month

Typical query length in tokens

1536 dimensions · 8,191 max tokens · MTEB 62

Total Tokens to Embed

50.0M

50,000,000 tokens

Embedding Cost

$1.00

One-time cost to embed all documents

Monthly Query Cost

$0.0500

50,000 queries/mo

Total Monthly Cost

$1.05

Embedding + query costs

Annual Cost

$12.60

Cost Per Document

$0.0000

Cheapest Alternative

Switch to text-embedding-005

Google · 768 dims · MTEB 63

Save $1.05/month ($12.60/year)

Vector database providers

Once you generate embeddings, you need somewhere to store and query them. These vector databases handle similarity search at scale.

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Cohere Embedding Models

ModelPrice / 1M TokensDimensionsMax TokensMTEB ScoreNotes
embed-v4$0.1001,02451264Multimodal, 100+ languages
embed-v3 (multilingual)$0.1001,02451264100+ languages
embed-v3 (English)$0.1001,02451264English optimised

512 Token Limit Note

Cohere's 512 token limit is significantly shorter than OpenAI's 8,191 limit. A 512-token chunk is approximately 375 words. Long documents need more chunks - factor this into your storage and retrieval architecture.

Frequently Asked Questions

How much do Cohere embeddings cost?

Cohere embed-v4 is priced at $0.10 per 1 million tokens. This makes it more expensive than OpenAI text-embedding-3-small ($0.02/1M) but comparable to the legacy ada-002. Cohere's advantage is its enterprise-focused features including multilingual support, multimodal embeddings (text + images), and a dedicated enterprise API with SLAs.

What are the key features of Cohere embed-v4?

Cohere embed-v4 offers: 1,024-dimensional embeddings, support for 100+ languages, multimodal capabilities (embedding both text and images in the same vector space), MTEB score of 64, and a 512-token limit per input. It is optimised for enterprise search and RAG applications, with SOC2 Type II compliance and GDPR data residency options.

How does Cohere compare to OpenAI for embeddings?

Cohere embed-v4 costs 5x more than OpenAI text-embedding-3-small but offers multilingual and multimodal capabilities that OpenAI's embedding models lack. For English-only RAG pipelines, OpenAI text-embedding-3-small is typically the better value. For multilingual enterprise search or applications requiring image+text embeddings, Cohere is the stronger choice.

What is Cohere's token limit for embeddings?

Cohere embed-v4 has a maximum of 512 tokens per input - shorter than OpenAI's 8,191 token limit. This means more aggressive chunking is required for long documents. Each 512-token chunk represents approximately 375 words of text. For long document pipelines, this chunking overhead should be factored into your cost and latency calculations.