openai

OpenAI: Text Embedding 3 Small

openai/text-embedding-3-small

Our take

AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.

According to the OpenRouter catalog, openai/text-embedding-3-small is an OpenAI text-to-embeddings model that converts text into numerical vectors intended to capture how related pieces of text are. The catalog describes it as an improved, more performant successor to OpenAI's ada embedding line and lists a context length of 8192 tokens, text-only input, and embedding outputs. No independent model card was available in this source pack, and the catalog reports no measured benchmark results, so capability claims rest on the provider's description alone.[1]

Strengths

Catalog-listed context length of 8192 tokens, suitable for embedding relatively long text inputs.[1]
Dedicated embedding modality (text in, vectors out) rather than a general-purpose chat model.[1]
The catalog lists no completion charge, so billing applies only to input text.[1]

Limitations

Evidence comes only from the OpenRouter catalog; no independent model card was available in this source pack.[1]
No measured benchmark results are reported, so retrieval quality should be validated on the intended workload.[1]
The supported-parameters list is empty, so tunable options are not documented here.[1]

Where to use it

A reasonable candidate for semantic search and relatedness scoring where text vectors are required.[1]
May suit clustering or deduplication pipelines on moderately long documents, given the listed context length.[1]

Sources

[1] OpenRouter catalog · OpenAI: Text Embedding 3 SmallSources checked: Nov 13, 2026

About this model

Description below is the original text supplied by OpenRouter, shown in the source language.

text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces...

Specifications

Model ID: openai/text-embedding-3-small
Creator: openai
Added to OpenRouter: 30.10.2025
Context length: 8,192
Modality: text->embeddings
Input modalities: text
Output modalities: embeddings
Token pricing: USD per 1M tokens: in $0.020 / out $0
Knowledge cutoff: —
Expiration date: —

Tasks and capabilities

A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.

Task	Grade	Source-declared capability
General quality	No measurement	—
Coding	No measurement	—
Agents	No measurement	—
Writing	No measurement	—
Reasoning	No measurement	—
Document analysis	No measurement	—
Vision	No measurement	—
Image generation	No measurement	—
Audio	No measurement	—
Video	No measurement	—
Embeddings	No measurement	output: embeddings

Evaluations

No evaluations are supplied for this model.

How grades work

Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.

S — S — top 10% of the cohort
A — A — next 20% of the cohort
B — B — next 30% of the cohort
C — C — below the B band

Providers

Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.

Provider	Context length	Price in /1M	Price out /1M	Latency p50 (30m, s)	Throughput p50 (30m, tok/s)	Uptime (30m)
Azure	8,192	$0.020	$0	—	—	100%
OpenAI	8,192	$0.020	$0	—	—	100%

Provider data retrieved: 13.11.2026

No published articles identify this model yet.