LLM Reference
Cloudflare Workers AI

Using BGE M3 on Cloudflare Workers AI

Implementation guide · BGE · Beijing Academy of Artificial Intelligence (BAAI)

ServerlessOpen Source

Cloudflare Workers AI exposes BGE M3 through model ID @cf/baai/bge-m3. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-04-28. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at Cloudflare Workers AI and generate an API key.
  2. 2
    Use the Cloudflare Workers AI SDK or REST API to call @cf/baai/bge-m3 — see the documentation for request format.

Code Examples

See Cloudflare Workers AI documentation for integration details.

Pricing on Cloudflare Workers AI

Capabilities

No model capability flags are currently sourced.

About BGE M3

BGE-M3 is BAAI's flagship multilingual embedding model that simultaneously performs dense retrieval, sparse (lexical) retrieval, and multi-vector (ColBERT-style) retrieval. It covers 100+ languages with an 8,192-token context window — far longer than most embedding models — making it effective for both short queries and long documents. Built on an extended XLM-RoBERTa architecture, it achieves state-of-the-art results on the MKQA and MLDR multilingual retrieval benchmarks and is available via NVIDIA NIM.

Model Specs

Released2024-01-27
Parameters568M
Context8k
ArchitectureEncoder Only

Provider

Cloudflare Workers AI
Cloudflare Workers AI

Cloudflare

San Francisco, California, United States