shibing624/text2vec-base-chinese

deepinfra/shibing624/text2vec-base-chinese

textContext 512

A sentence similarity model that can be used for various NLP tasks such as text classification, sentiment analysis, named entity recognition, question answering, and more. It utilizes the CoSENT architecture, which consists of a transformer encoder and a pooling module, to encode input texts into vectors that capture their semantic meaning. The model was trained on the nli_zh dataset and achieved high performance on various benchmark datasets.

Price

Input$0.005

per 1M tokens

Compare this model

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.

Provider	Tag	Quant	Input	Output	Context	Uptime	Status	Source
DeepInfra	—	—	$0.005/1M	Not published—	512	—	Listed	deepinfra

Monthly bill

Token models use the token counts. Image, video, and audio models use units times the flat rate.

Input tokens Output tokens Units

$0.01 at list price for this workload

Every rate

Unit	Input	Output	Flat	Confidence
token	$0.005/1M	Not published—	Not published—	official

Price history

shibing624/text2vec-base-chinese

input or flat output

Daily snapshots

UTC date	Unit	Input	Output	Flat
2026-10-08	token	$0.005	Not published—	Not published—
2026-10-09	token	$0.005	Not published—	Not published—
2026-10-10	token	$0.005	Not published—	Not published—
2026-10-11	token	$0.005	Not published—	Not published—
2026-10-12	token	$0.005	Not published—	Not published—
2026-10-13	token	$0.005	Not published—	Not published—
2026-10-14	token	$0.005	Not published—	Not published—
2026-10-15	token	$0.005	Not published—	Not published—
2026-10-16	token	$0.005	Not published—	Not published—
2026-10-17	token	$0.005	Not published—	Not published—
2026-10-18	token	$0.005	Not published—	Not published—
2026-10-19	token	$0.005	Not published—	Not published—
2026-10-20	token	$0.005	Not published—	Not published—
2026-10-21	token	$0.005	Not published—	Not published—
2026-10-22	token	$0.005	Not published—	Not published—
2026-10-23	token	$0.005	Not published—	Not published—
2026-10-24	token	$0.005	Not published—	Not published—
2026-10-25	token	$0.005	Not published—	Not published—
2026-10-26	token	$0.005	Not published—	Not published—
2026-10-27	token	$0.005	Not published—	Not published—
2026-10-28	token	$0.005	Not published—	Not published—
2026-10-29	token	$0.005	Not published—	Not published—
2026-10-30	token	$0.005	Not published—	Not published—

Alternatives

BAAI/bge-base-en-v1.5$0.005/1M · Compare
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intfloat/e5-base-v2$0.005/1M · Compare
Intfloat: E5-Base-v2$0.005/1M · Compare
Sentence Transformers: all-MiniLM-L12-v2$0.005/1M · Compare
Sentence Transformers: all-MiniLM-L6-v2$0.005/1M · Compare

Parameters (1)

embed

Call this model

OpenRouter id deepinfra/shibing624/text2vec-base-chinese. 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/shibing624/text2vec-base-chinese","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/shibing624/text2vec-base-chinese",
    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/shibing624/text2vec-base-chinese", "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())

JSON · History JSON