per 1M tokens
Qwen/Qwen3-Embedding-4B
deepinfra/Qwen/Qwen3-Embedding-4B
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B).
Price
Providers
Each row is one seller. Prices are the published rate from that source. A dash means that source did not publish the amount. Sorted by a 3:1 blend of input and output.
No provider prices yet.
Monthly bill
Token models use the token counts. Image, video, and audio models use units times the flat rate.
$0.02 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0.02/1M | Not published— | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | token | $0.02 | Not published— | Not published— |
| 2026-10-09 | token | $0.02 | Not published— | Not published— |
| 2026-10-10 | token | $0.02 | Not published— | Not published— |
| 2026-10-11 | token | $0.02 | Not published— | Not published— |
| 2026-10-12 | token | $0.02 | Not published— | Not published— |
| 2026-10-13 | token | $0.02 | Not published— | Not published— |
| 2026-10-14 | token | $0.02 | Not published— | Not published— |
| 2026-10-15 | token | $0.02 | Not published— | Not published— |
| 2026-10-16 | token | $0.02 | Not published— | Not published— |
| 2026-10-17 | token | $0.02 | Not published— | Not published— |
| 2026-10-18 | token | $0.02 | Not published— | Not published— |
| 2026-10-19 | token | $0.02 | Not published— | Not published— |
| 2026-10-20 | token | $0.02 | Not published— | Not published— |
| 2026-10-21 | token | $0.02 | Not published— | Not published— |
| 2026-10-22 | token | $0.02 | Not published— | Not published— |
| 2026-10-23 | token | $0.02 | Not published— | Not published— |
| 2026-10-24 | token | $0.02 | Not published— | Not published— |
| 2026-10-25 | token | $0.02 | Not published— | Not published— |
| 2026-10-26 | token | $0.02 | Not published— | Not published— |
| 2026-10-27 | token | $0.02 | Not published— | Not published— |
| 2026-10-28 | token | $0.02 | Not published— | Not published— |
| 2026-10-29 | token | $0.02 | Not published— | Not published— |
| 2026-10-30 | token | $0.02 | Not published— | Not published— |
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Parameters (1)
embed
Call this model
OpenRouter id deepinfra/Qwen/Qwen3-Embedding-4B. InferenceList does not send the request.
curl
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"deepinfra/Qwen/Qwen3-Embedding-4B","messages":[{"role":"user","content":"Hello"}]}'
JavaScript
await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.OPENROUTER_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "deepinfra/Qwen/Qwen3-Embedding-4B",
messages: [{ role: "user", content: "Hello" }],
}),
});
Python
import os, json, urllib.request
req = urllib.request.Request(
"https://openrouter.ai/api/v1/chat/completions",
data=json.dumps({"model": "deepinfra/Qwen/Qwen3-Embedding-4B", "messages": [{"role": "user", "content": "Hello"}]}).encode(),
headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"], "Content-Type": "application/json"},
)
print(urllib.request.urlopen(req).read().decode())