openai
OpenAI: Text Embedding 3 Small (batch)
openai/text-embedding-3-small:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter’s catalog lists openai/text-embedding-3-small:batch as an OpenAI text-to-embeddings endpoint that accepts text and returns embeddings. The catalog describes it as an improved, more performant version of OpenAI’s ada embedding model and gives it an 8,192-token context length. Those are source-reported specifications, not measured results: this source pack contains no independent model card or benchmark evidence. Its batch designation could make it worth evaluating for asynchronous embedding jobs, but workload testing is still needed.[1]
Strengths
Limitations
- Only an OpenRouter catalog entry is supplied; an independent model card was not available in this source pack.[1]
- No benchmark results or measured performance are included, so the catalog's claims need validation on the intended workload.[1]
- The entry does not define the batch variant's behavior; supported parameters are listed as none, while knowledge cutoff, licensing, hardware needs, and safety information are not provided.[1]
Where to use it
- Evaluate for semantic search or retrieval where relatedness between text items matters.[1]
- Consider for clustering, classification, or duplicate-detection prototypes after validating embedding quality on representative data.[1]
- Test in asynchronous or bulk embedding pipelines only after confirming the batch variant's actual behavior and operational limits.[1]
Sources
- [1] OpenRouter catalog · OpenAI: Text Embedding 3 Small (batch)Sources 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:batch
- Canonical 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 $0.010 / 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) |
|---|---|---|---|---|---|---|
| OpenAI | 8,192 | $0.010 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.