Home › Guides › Best Chinese LLM Stack for RAG & Semantic Search (2026)
RAG has two cost centers: the embedding model (cheap but multiplied by your corpus size) and the generation model (your only real lever). The Bridge stack below keeps both at the bottom of the market.
$0.02 / — per 1M tokens
The embedding engine — $0.02 per 1M tokens via the standard /v1/embeddings endpoint. Embed a 10M-token corpus for $0.20.
≈ $0.20/mo at 10M tokens · cache — · model page →
$0.42 / $1.27 $0.14 / $0.28 save 67% per 1M tokens
The generation companion — 67% off official for retrieval-grounded answers, classification and extraction at scale.
≈ $1.58/mo at 10M tokens · cache $0.014 · model page →
$0.25 / $0.75 per 1M tokens
The self-hosted alternative — open weights on Bridge capacity with the lowest p50 latency, for latency-sensitive extraction pipelines.
≈ $4.00/mo at 10M tokens · cache — · model page →
| Priority | Pick | Why |
|---|---|---|
| Lowest cost | DeepSeek V4 Flash $0.14 / $0.28 | DeepSeek V4 Flash — cheapest generation tier at $1.58/10M-token month. |
| Balanced | Qwen2.5 72B $0.25 / $0.75 | Qwen2.5 72B — lowest latency, open-weights stability for high-volume extraction. |
| Best quality | DeepSeek V4 Pro $0.28 / $0.82 | DeepSeek V4 Pro — when retrieval-grounded answers need real reasoning. |
If your RAG answers must reason across many retrieved chunks, upgrade generation to DeepSeek V4 Pro (still 78% off official). If your corpus is primarily Chinese and answers need deep Chinese nuance, GLM-5.2 is the generation pick.
Which embedding model should I use for RAG?
Qwen3 Embedding 8B on Bridge costs $0.02 per 1M tokens through the OpenAI-compatible /v1/embeddings endpoint — a 10M-token corpus costs $0.20 to embed. It is the standard choice for new RAG systems.
What is the cheapest LLM for RAG generation?
DeepSeek V4 Flash at $0.14/$0.28 per 1M tokens (67% below official) is the cheapest generation tier. A typical 10M input + 2M output month runs about $1.58.
Does Bridge support the OpenAI embeddings API format?
Yes — /v1/embeddings with model="qwen3-embedding-8b" works with the OpenAI SDK by changing base_url and api_key, exactly like chat completions.