Thenlper
GTE-Base
thenlper/gte-base
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
RoleTurns text into vectors for search.
$0.0050
In / M
Free
Out / M
512
Context
- AA intelligence index
- Not on listing
- Inputs
- text
- Outputs
- embeddings
- Tools
- Not listed
- Structured output
- Not listed
- Reasoning
- Not listed
- Weights
- Open · thenlper/gte-base
- Listed
- Nov 18, 2025
01 · What it's good at
Use it for this.
Three jobs this model is built for, and the ones to give to something else.
01
Semantic search and RAG indexes
Skip it for
- Answering the question. Pair it with a reranker and a chat model.
02 · Example tasks
What you'd actually send it.
Real inputs, not a benchmark. Paste one into the snippet below to try it.
You send
Embed each help article chunk.
Why this model
The first step of RAG.
03 · Your stack
Where it sits: Retrieve.
An embedding model maps text to a vector. You store the vectors, search the nearest ones, and only then ask a chat model to write.
Layer 01 · this model
Retrieve
Layer 02
Plan & reason
Layer 03
Do the volume
Layer 04
Gate & route
Layer 05
Draw, speak, listen
04 · Wire it up
One request. Paste and run.
Uses OpenRouter, so one key works for every model on this site. Set OPENROUTER_API_KEY and swap the prompt for one of the tasks above.
const res = await fetch("https://openrouter.ai/api/v1/embeddings", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "thenlper/gte-base",
"input": [
"How do I reset my password?",
"Billing runs on the first of each month."
]
}),
});
const { data } = await res.json();
// One vector per input, in order. Store each with its source text.
const vectors = data.map((item: { embedding: number[] }) => item.embedding);
console.log(vectors.length, vectors[0].length);
05 · Watch out for
Gotchas.
This lesson is generated from the model type and the live listing. It has not been hand-reviewed yet.
Check the listing for supported parameters before you rely on tools or structured output.
Sources
Lessons are written from each model's listing and the linked docs. We don't publish our own benchmark numbers; the index above is Artificial Analysis, as shown on OpenRouter.