per 1M tokens
thenlper/gte-large
deepinfra/thenlper/gte-large
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
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.
$0.01 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0.01/1M | Not published— | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | token | $0.01 | Not published— | Not published— |
| 2026-10-09 | token | $0.01 | Not published— | Not published— |
| 2026-10-10 | token | $0.01 | Not published— | Not published— |
| 2026-10-11 | token | $0.01 | Not published— | Not published— |
| 2026-10-12 | token | $0.01 | Not published— | Not published— |
| 2026-10-13 | token | $0.01 | Not published— | Not published— |
| 2026-10-14 | token | $0.01 | Not published— | Not published— |
| 2026-10-15 | token | $0.01 | Not published— | Not published— |
| 2026-10-16 | token | $0.01 | Not published— | Not published— |
| 2026-10-17 | token | $0.01 | Not published— | Not published— |
| 2026-10-18 | token | $0.01 | Not published— | Not published— |
| 2026-10-19 | token | $0.01 | Not published— | Not published— |
| 2026-10-20 | token | $0.01 | Not published— | Not published— |
| 2026-10-21 | token | $0.01 | Not published— | Not published— |
| 2026-10-22 | token | $0.01 | Not published— | Not published— |
| 2026-10-23 | token | $0.01 | Not published— | Not published— |
| 2026-10-24 | token | $0.01 | Not published— | Not published— |
| 2026-10-25 | token | $0.01 | Not published— | Not published— |
| 2026-10-26 | token | $0.01 | Not published— | Not published— |
| 2026-10-27 | token | $0.01 | Not published— | Not published— |
| 2026-10-28 | token | $0.01 | Not published— | Not published— |
| 2026-10-29 | token | $0.01 | Not published— | Not published— |
| 2026-10-30 | token | $0.01 | Not published— | Not published— |
Alternatives
- BAAI/bge-en-icl$0.01/1M · Compare
- BAAI/bge-large-en-v1.5$0.01/1M · Compare
- BAAI/bge-m3$0.01/1M · Compare
- BAAI/bge-m3-multi$0.01/1M · Compare
- BAAI/bge-m3-multi-8k$0.01/1M · Compare
- BAAI: bge-large-en-v1.5$0.01/1M · Compare
Parameters (1)
embed
Call this model
OpenRouter id deepinfra/thenlper/gte-large. 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/thenlper/gte-large","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/thenlper/gte-large",
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/thenlper/gte-large", "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())