embeddingsopenAuto lesson

Qwen

Qwen3 Embedding 8B

qwen/qwen3-embedding-8b

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text...

RoleTurns text into vectors for search.

$0.010

In / M

Free

Out / M

41K

Context

AA intelligence index
Not on listing
Inputs
text
Outputs
embeddings
Tools
Not listed
Structured output
Not listed
Reasoning
Not listed
Weights
Open · Qwen/Qwen3-Embedding-8B
Listed
Oct 28, 2025
Live from the OpenRouter listing ↗

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.

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": "qwen/qwen3-embedding-8b",
    "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.