Google: Gemini Embedding 001 (batch)
google/gemini-embedding-001:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
google/gemini-embedding-001:batch is a Google text-to-embeddings model listed in the OpenRouter catalog as a batch variant of gemini-embedding-001. It accepts text input and returns embedding vectors, with a stated context window of about 20,000 tokens. The catalog description presents it as a unified embedding model across domains including science, legal, finance, and coding, and claims a leading position on the Massive Text Embedding Benchmark; these are vendor-supplied descriptions, not measurements verified in this source pack. An independent model card was not available.[1]
Strengths
- Text-in, embeddings-out modality with a catalog-listed context window of roughly 20,000 tokens, which may accommodate long documents and passages.[1]
- OpenRouter's catalog describes the underlying model as unified across domains such as science, legal, finance, and coding, suggesting broad intended coverage rather than a single specialty.[1]
- The catalog description reports a consistently high placement on the Massive Text Embedding Benchmark; this is a source-reported claim, not a result measured within this pack.[1]
Limitations
- Evidence is catalog-only: this pack included no independent model card, technical paper, or embedding specifications such as dimensionality or supported languages.[1]
- The benchmark claim and domain coverage come from the catalog description and were not benchmarked here; supported parameters are listed as empty and batch-specific behavior is undocumented, so validate quality and throughput on your own workloads.[1]
- The catalog description text is truncated, so some stated capabilities may be incompletely captured.[1]
Where to use it
- Semantic search and retrieval-augmented generation over long text, where the large context window could reduce the need for chunking; evaluate recall on your own data before adoption.[1]
- Clustering, classification, or deduplication of documents in the domains the catalog names, such as legal or scientific text, as a candidate worth piloting rather than a proven fit.[1]
- Batch embedding pipelines for large corpora, consistent with the batch variant listing, provided throughput and indexing behavior are verified in testing.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini Embedding 001 (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
gemini-embedding-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text Embedding Benchmark...
Specifications
- Model ID
- google/gemini-embedding-001:batch
- Canonical ID
- google/gemini-embedding-001
- Creator
- Added to OpenRouter
- 31.10.2025
- Context length
- 20,000
- Modality
- text->embeddings
- Input modalities
- text
- Output modalities
- embeddings
- Token pricing
- USD per 1M tokens: in $0.075 / 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) |
|---|---|---|---|---|---|---|
| 20,000 | $0.075 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.