Model comparison
Compare Cohere Embed 4 and Voyage Context 4 using the same provider-sourced embeddings & vector search rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
Set your usage. Your estimate updates as you type.
Assumes 600 tokens per page, processed separately. Actual token counts vary. This covers embedding only, not storage, search, or generated answers.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| Voyage Context 4Voyage AI | Voyage AI | $0.0007 |
| Cohere Embed 4Cohere | Cohere | No reviewed rate |
Results describe a specific test, language and configuration—not overall intelligence. Missing results do not imply worse quality.
Community-submitted result
Retrieval relevance (0–1; higher is better)
MTEB · 1.38.43 · eng-Latn · test/default
dimensions: 1536 · similarity: cosine · modelMetadata: https://github.com/embeddings-benchmark/results/blob/main/results/Cohere__Cohere-embed-v4.0/1/model_meta.json
Checked: September 5, 2026
MTEB contributors · FinanceBenchRetrieval0.8833 nDCG@10
Quick take
Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window.
Cohere does not publish one simple hosted token price for Embed 4 on the reviewed pricing page. Ask for the exact SaaS or private-deployment rate before comparing total cost.
Voyage AI's document-aware embedding model that automatically creates vectors for chunks while preserving information from the surrounding document.
Automatic chunking is convenient but less application-controlled than a custom parser. Test headings, tables, citations, metadata, and update behavior before adopting it across a document estate.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Embeddings & vector search | Cohere Embed 4 | Voyage Context 4 |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | Hosted unit price not published | $0.12 / 1M tokens after 200M free |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 128K tokens | 32K per internal pass; longer documents split automatically |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 256, 512, 1,024, or 1,536 | 256, 512, 1,024, or 2,048 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text, images, and mixed-content PDFs | Whole text documents |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Search query/document, classification, and clustering input types | Automatic contextual chunking, overlap, and long-document splitting |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Cohere API, Model Vault, Microsoft Foundry, SageMaker | Voyage hosted API and MongoDB-integrated routes |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
Cohere enterprise customers can opt out of training; SaaS prompts and generations are generally deleted after 30 days. Approved zero-data-retention accounts and private deployments offer stronger controls.
Voyage's public terms allow customer content to improve services unless the customer opts out. Eligible paid organizations can configure an opt-out and zero-day retention; separately negotiated enterprise terms may differ.
Provider and API links
Frequently asked questions
Cohere Embed 4: Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window. Voyage Context 4: Voyage AI's document-aware embedding model that automatically creates vectors for chunks while preserving information from the surrounding document.
Consider Cohere Embed 4 when your priority is Enterprise search over visually rich documents. Consider Voyage Context 4 when your priority is Long-document RAG. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Embeddings & vector search category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
