Best Embedding Models in July 2026
Embedding models compared on price and specs. No quality benchmark suite for embeddings is ingested yet, and this page makes no quality claim.
Embedding model choice is stickier than chat model choice: switching means re-embedding your corpus, so the pragmatic questions are price per million tokens, dimensionality, and whether the model will remain available. This page compares the catalog’s embedding models on exactly those grounds, ordered by price where one is published.
An honesty note: no independent embedding quality benchmark (such as MTEB) is currently ingested into the catalog, so this page deliberately makes no quality ranking claim. It is a specification and price comparison, which for embeddings is most of the decision anyway once a model family has acceptable retrieval quality for your domain.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
The ranking
This list compares the 49 embedding models in the catalog on specifications and price, ordered by the lowest available price where one is published. No public quality benchmark suite for embeddings is currently ingested, so this page makes no quality claim about ordering.
| # | Model | Context | $ in / out per 1M |
|---|---|---|---|
| 1 | openai | 8K | $0.02 in |
| 2 | cohere | 512 | $0.1 in |
| 3 | cohere | 512 | $0.1 in |
| 4 | cohere | 512 | $0.1 in |
| 5 | mistral | 8K | $0.1 in |
| 6 | openai | 8K | $0.1 in |
| 7 | cohere | 8K | $0.12 in |
| 8 | openai | 8K | $0.13 in |
| 9 | google | 2K | $0.15 in |
| 10 | google | 8K | $0.2 in |
marks models served through the Inferbase API. Missing values are shown as No data rather than estimated.
Also worth evaluating
The next ranks after the top ten, for teams that want a wider shortlist.
Or stop choosing manually
This page exists because model choice is a per-task decision, and the honest answer changes as benchmarks and prices move. Routing makes that decision per request instead.
Send model="auto" to one OpenAI-compatible endpoint and each request is served by the best fit from the same quality and price data behind this ranking, with the decision disclosed per request.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/api/v1/inference",
api_key="YOUR_INFERBASE_KEY",
)
# Let routing pick the best model per request,
# or pin any model id from GET /models.
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "..."}],
)Frequently asked questions
How this ranking works and how to act on it.
Because no independent embedding benchmark suite is ingested into the catalog yet, and this site does not publish rankings it cannot back with data. When embedding quality evidence is ingested, ordering will incorporate it; until then the comparison is specs and price.
Retrieval quality on your domain, price per million tokens (embedding costs scale with corpus size, not just queries), dimensionality (storage and search cost in your vector database), and operational stability, since a model change forces re-embedding.
Yes. Inferbase serves an OpenAI-compatible POST /embeddings endpoint for the served models on this page, with a pinned model id. Batch inputs preserve order, and usage is metered the same way as chat.
Embeddings from different models are not comparable: a vector index built with one model cannot be queried with another. Routing between embedding models would silently corrupt retrieval, so the endpoint requires an explicit model choice by design.
Embedding models are typically one to two orders of magnitude cheaper per token than chat models, but they process your entire corpus. For large document bases, the embedding line item is dominated by corpus size and re-embedding frequency, not query volume.
Related rankings
The same evidence-first method, applied to other tasks.
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