Model comparison
Compare Voyage 4 Large 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 4 LargeVoyage AI | Voyage AI | $0.0007 |
| Voyage Context 4Voyage AI | Voyage AI | $0.0007 |
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 · 2.1.3 · eng-Latn · test/default
dimensions: 1024 · similarity: cosine · modelMetadata: https://github.com/embeddings-benchmark/results/blob/main/results/voyageai__voyage-4-large/1/model_meta.json
Checked: September 5, 2026
MTEB contributors · FinanceBenchRetrieval0.9288 nDCG@10
Quick take
Voyage AI's quality-first general embedding model for text and code retrieval, with adjustable dimensions and a shared family vector space.
The large free-token allowance is an account-level commercial detail, not a permanent zero-cost guarantee. Confirm eligibility, retention settings, and the rate that applies after the allowance.
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 | Voyage 4 Large | Voyage Context 4 |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | $0.12 / 1M tokens after 200M free | $0.12 / 1M tokens after 200M free |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 32K tokens | 32K per internal pass; longer documents split automatically |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 1,024 default; 256, 512, or 2,048 optional | 256, 512, 1,024, or 2,048 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text and code | Whole text documents |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Query/document input types, truncation, output dimension | Automatic contextual chunking, overlap, and long-document splitting |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Voyage API, MongoDB Atlas, and selected clouds | 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.
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
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
Voyage 4 Large: Voyage AI's quality-first general embedding model for text and code retrieval, with adjustable dimensions and a shared family vector space. Voyage Context 4: Voyage AI's document-aware embedding model that automatically creates vectors for chunks while preserving information from the surrounding document.
Consider Voyage 4 Large when your priority is Quality-sensitive text retrieval. 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.
