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BAAI: bge-m3

baai/bge-m3

8KContext Window

Supported Protocols:max_tokenstemperaturetop_pstopfrequency_penaltypresence_penaltyrepetition_penaltytop_kseedmin_presponse_format

Online

The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.

Capabilities

Text GenerationCode GenerationAnalysis & Reasoningmodels.reasoning

Technical Specs

Input Modality

Text

Output Modality

Text

Arch

—

Pricing

Pay per use, no monthly fees

Input Token< ¥0.001/1K Token

Output Token< ¥0.001/1K Token

Quick Start

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uniontoken.ai/v1",
    api_key="YOUR_UNIONTOKEN_API_KEY",
)

response = client.chat.completions.create(
    model="baai/bge-m3",
    messages=[
        {"role": "user", "content": "Hello!"}
    ],
)

print(response.choices[0].message.content)

FAQ

BAAI: bge-m3

baai/bge-m3

In< ¥0.001/1K

Out< ¥0.001/1K

Context Window8K

Start Using →View Integration Docs

Related Models

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BAAI: bge-base-en-v1.5

baai/bge-base-en-v1.5

< ¥0.001/1K

BAAI: bge-large-en-v1.5

baai/bge-large-en-v1.5

< ¥0.001/1K

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