Model comparison
Compare Cohere Embed 4 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 |
| 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.
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 | Cohere Embed 4 | Mistral Embed |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | Hosted unit price not published | $0.10 / 1M tokens |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 128K tokens | 8K tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 256, 512, 1,024, or 1,536 | 1,024 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text, images, and mixed-content PDFs | Text |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Search query/document, classification, and clustering input types | Single or batched inputs; application handles chunking |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Cohere API, Model Vault, Microsoft Foundry, SageMaker | 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.
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.
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
Cohere Embed 4: Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window. Mistral Embed: Mistral's straightforward hosted text embedding model for semantic search, clustering, classification, and RAG.
Consider Cohere Embed 4 when your priority is Enterprise search over visually rich documents. 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.
