Model comparison
Compare text-embedding-3-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 |
| text-embedding-3-largeOpenAI | OpenAI | $0.0008 |
Quick take
OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines.
The model embeds text only and does not chunk long documents for you. Your ingestion pipeline still needs a deliberate chunking, metadata, evaluation, and re-indexing strategy.
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 | text-embedding-3-large | Mistral Embed |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | $0.13 / 1M tokens | $0.10 / 1M tokens |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 8,191 input tokens | 8K tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 3,072 default; shorter vectors via dimensions | 1,024 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text | Text |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Optional dimensions parameter; chunking handled by the application | Single or batched inputs; application handles chunking |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | OpenAI hosted Embeddings API | 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.
OpenAI says business and API data is not used to train its models by default. Abuse-monitoring retention and eligible zero-data-retention controls depend on the endpoint and organization approval.
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
text-embedding-3-large: OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines. Mistral Embed: Mistral's straightforward hosted text embedding model for semantic search, clustering, classification, and RAG.
Consider text-embedding-3-large when your priority is High-quality text search and RAG. 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.
