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BAAI: bge-m3

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. Priced at $0.01 per M tokens. 8,194 token context window, maximum output of 8,194 tokens. Higher uptime with 2 providers.

Vedi la fonte su OpenRouter Hugging Face
Panoramica

Specifiche del modello

Contesto
8194 tokens
Output massimo
7374 tokens
Architettura
text->embeddings
Tokenizer
Other
Limite di conoscenza
Non indicato
Moderato
No
OPENROUTER

Prezzi completi

Tariffe sincronizzate da OpenRouter, per milione di token.

Input
$0.01
/M tokens
API

Avvio rapido

Chiama questo modello tramite l’API compatibile OpenAI di OpenRouter.

curl https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baai/bge-m3","input":"Your text here"}'
Capacità e modalità

Input → Output

Input
text
Output
embeddings
API

Parametri API supportati

frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Provider disponibili

2 provider

Provider live su OpenRouter

Disponibilità, latenza, throughput e routing cambiano continuamente. Consulta la fonte per i dati correnti.

OpenRouter

Parasail

unknown
Contesto
8K
Output massimo
7K
Input
$0.010
Output
Gratuito
Lettura cache
Non indicato
Scrittura cache
Non indicato

DeepInfra

fp32
Contesto
8K
Output massimo
7K
Input
$0.010
Output
Gratuito
Lettura cache
Non indicato
Scrittura cache
Non indicato
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BAAI: bge-base-en-v1.5

The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features...

Contesto
1K
Input
text
Output
embeddings
Input: $0.0050Output: Gratuitoper milione di token
Vedi i dettagli del modello →
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BAAI: bge-large-en-v1.5

The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks...

Contesto
1K
Input
text
Output
embeddings
Input: $0.010Output: Gratuitoper milione di token
Vedi i dettagli del modello →