openai
OpenAI: Text Embedding 3 Large (batch)
openai/text-embedding-3-large:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
openai/text-embedding-3-large:batch is an OpenAI text-to-embeddings model listed in the OpenRouter catalog, where it is described as OpenAI's most capable embedding model for English and non-English tasks, with an 8192-token context window. That capability claim comes from the catalog description, not from independent testing: the source pack contains no benchmark results or measured evaluations, and an independent model card was not available. Editorial suitability for retrieval or similarity workloads should therefore be validated on your own data before adoption.[1]
Strengths
- Converts text into embedding vectors, the representation the catalog describes as useful for measuring relatedness between texts.[1]
- 8192-token context window according to the catalog listing.[1]
- The catalog describes it as capable on both English and non-English tasks; treat this as a vendor-reported claim, not a measured result.[1]
Limitations
- Evidence is catalog-only: no independent model card was included in this source pack, so the stated capabilities are unverified.[1]
- The supplied source contains no benchmark results or measured performance data; validate on your own workload.[1]
- The listing documents no supported parameters and does not describe batch-specific behavior beyond the canonical model.[1]
Where to use it
- Candidate for semantic search and retrieval pipelines that need text similarity scoring; an editorial suggestion to validate, not a proven result.[1]
- Candidate for clustering, deduplication, or relatedness-based ranking of documents, subject to testing on your workload.[1]
- Possible fit for multilingual embedding needs given the catalog's non-English capability claim, pending your own evaluation.[1]
Sources
- [1] OpenRouter catalog · OpenAI: Text Embedding 3 Large (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-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two...
Specifications
- Model ID
- openai/text-embedding-3-large:batch
- Canonical ID
- openai/text-embedding-3-large
- 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 $0.065 / 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 | $0.065 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.