Google: Gemini Embedding 001
google/gemini-embedding-001
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 is a Google text embedding model listed in the OpenRouter catalog, which reports a text-to-embeddings modality and a 20,000-token context window. The catalog description portrays it as a unified embedding model covering domains such as science, legal, finance, and coding, and claims a leading position on the Massive Text Embedding Benchmark; these are source-reported claims, not independently measured results. No independent model card was included in this source pack, so real-world behavior should be validated on your own data.[1]
Strengths
- The OpenRouter catalog lists a 20,000-token context window, ample for embedding long documents.[1]
- Its catalog description advertises a unified embedding experience across science, legal, finance, and coding domains; this is a vendor claim, not a measured result.[1]
- Catalog pricing fields indicate charges only for input tokens, with embedding outputs at no cost.[1]
Limitations
- Evidence is limited to the OpenRouter catalog; no independent model card was available in this source pack, so capabilities require validation on your workload.[1]
- The catalog's benchmark fields are empty, so the claimed benchmark standing cannot be verified from this evidence.[1]
- Supported parameters, embedding dimensions, and knowledge cutoff are not documented in the supplied source.[1]
Where to use it
- Candidate for semantic search or retrieval-augmented generation over long texts, per the catalog's stated context window; evaluate on your corpus first.[1]
- Worth testing for cross-domain text similarity tasks in legal, finance, scientific, or code corpora, matching the coverage advertised in the catalog.[1]
- May suit text clustering and classification pipelines that consume embeddings only.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini Embedding 001Sources 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
- 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 $0.150 / 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 | $0.150 | $0 | — | — | 100% | |
| Google AI Studio | 20,000 | $0.150 | $0 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.