Model comparison
Compare Voyage 4 Large and Mistral Embed 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) |
|---|---|---|
| Mistral EmbedMistral AI | Mistral AI | $0.0006 |
| Voyage 4 LargeVoyage 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.
Mistral's straightforward hosted text embedding model for semantic search, clustering, classification, and RAG.
The 8K input window and fixed 1,024 dimensions offer fewer controls than newer embedding families. Verify regional endpoint support and measure retrieval quality on your language and domain.
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 | Mistral Embed |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | $0.12 / 1M tokens after 200M free | $0.10 / 1M tokens |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 32K tokens | 8K tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 1,024 default; 256, 512, or 2,048 optional | 1,024 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text and code | Text |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Query/document input types, truncation, output dimension | Single or batched inputs; application handles chunking |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Voyage API, MongoDB Atlas, and selected clouds | Mistral hosted API and Studio |
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
Mistral says API data is not used for training by default. Standard API inputs and outputs are generally retained for 30 rolling days for abuse monitoring unless approved zero-data-retention controls apply.
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. Mistral Embed: Mistral's straightforward hosted text embedding model for semantic search, clustering, classification, and RAG.
Consider Voyage 4 Large when your priority is Quality-sensitive text retrieval. Consider Mistral Embed when your priority is Text-only semantic search. 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.
