per 1M tokens
LiquidAI: LFM2.5-Embedding-350M (free)
liquid/lfm-2.5-embedding-350m:free
LFM2.5-Embedding-350M is a text embedding model from Liquid AI. It produces 1,024-dimensional embeddings for retrieval and semantic search. Successful OpenRouter requests and embeddings may be retained and used to train...
Price
per 1M tokens
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.
$0.00 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0/1M | $0/1M | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-09-15 | token | $0 | $0 | Not published— |
| 2026-09-16 | token | $0 | $0 | Not published— |
| 2026-09-17 | token | $0 | $0 | Not published— |
| 2026-09-18 | token | $0 | $0 | Not published— |
| 2026-09-19 | token | $0 | $0 | Not published— |
| 2026-09-20 | token | $0 | $0 | Not published— |
| 2026-09-21 | token | $0 | $0 | Not published— |
| 2026-09-22 | token | $0 | $0 | Not published— |
| 2026-09-23 | token | $0 | $0 | Not published— |
| 2026-09-24 | token | $0 | $0 | Not published— |
| 2026-09-25 | token | $0 | $0 | Not published— |
| 2026-09-26 | token | $0 | $0 | Not published— |
| 2026-09-27 | token | $0 | $0 | Not published— |
| 2026-09-28 | token | $0 | $0 | Not published— |
| 2026-09-29 | token | $0 | $0 | Not published— |
| 2026-09-30 | token | $0 | $0 | Not published— |
| 2026-10-01 | token | $0 | $0 | Not published— |
| 2026-10-02 | token | $0 | $0 | Not published— |
| 2026-10-03 | token | $0 | $0 | Not published— |
| 2026-10-04 | token | $0 | $0 | Not published— |
| 2026-10-05 | token | $0 | $0 | Not published— |
| 2026-10-06 | token | $0 | $0 | Not published— |
| 2026-10-07 | token | $0 | $0 | Not published— |
| 2026-10-08 | token | $0 | $0 | Not published— |
| 2026-10-09 | token | $0 | $0 | Not published— |
| 2026-10-10 | token | $0 | $0 | Not published— |
| 2026-10-11 | token | $0 | $0 | Not published— |
| 2026-10-12 | token | $0 | $0 | Not published— |
| 2026-10-13 | token | $0 | $0 | Not published— |
| 2026-10-14 | token | $0 | $0 | Not published— |
| 2026-10-15 | token | $0 | $0 | Not published— |
| 2026-10-16 | token | $0 | $0 | Not published— |
| 2026-10-17 | token | $0 | $0 | Not published— |
| 2026-10-18 | token | $0 | $0 | Not published— |
| 2026-10-19 | token | $0 | $0 | Not published— |
| 2026-10-20 | token | $0 | $0 | Not published— |
| 2026-10-21 | token | $0 | $0 | Not published— |
| 2026-10-22 | token | $0 | $0 | Not published— |
| 2026-10-23 | token | $0 | $0 | Not published— |
| 2026-10-24 | token | $0 | $0 | Not published— |
| 2026-10-25 | token | $0 | $0 | Not published— |
| 2026-10-26 | token | $0 | $0 | Not published— |
| 2026-10-27 | token | $0 | $0 | Not published— |
| 2026-10-28 | token | $0 | $0 | Not published— |
| 2026-10-29 | token | $0 | $0 | Not published— |
| 2026-10-30 | token | $0 | $0 | Not published— |
Alternatives
Call this model
OpenRouter id liquid/lfm-2.5-embedding-350m:free. 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":"liquid/lfm-2.5-embedding-350m:free","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: "liquid/lfm-2.5-embedding-350m:free",
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": "liquid/lfm-2.5-embedding-350m:free", "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())