OpenLuxBGE-M3 vs Jina Reranker V3
BGE-M3 and Jina Reranker V3 both run on OpenLux behind one OpenAI-compatible endpoint. BGE-M3 is metered at $0.0035 per 1M tokens; Jina Reranker V3 at $0.0042 per 1M tokens. BGE-M3 is the lower-cost option per unit — about 17% cheaper on the same OpenLux endpoint. This page compares their rates, strengths and best-fit workloads so you can route each request to the right model.
The short version
In practice the two models are complements, not substitutes: BGE-M3 suits workloads that need embeddings for search, RAG and clustering, retrieval and knowledge pipelines. Jina Reranker V3 is the better fit when you need reranking retrieval results before the answer model runs, retrieval and knowledge pipelines. Both are called with the same OpenLux key, so switching between them is a one-field change to the model name.
Choose BGE-M3 when
- You need embeddings for search, RAG and clustering.
- You need retrieval and knowledge pipelines.
- Unit cost is the deciding factor for this workload.
Choose Jina Reranker V3 when
- You need reranking retrieval results before the answer model runs.
- You need retrieval and knowledge pipelines.
Rates on OpenLux
| Model | Provider | Rate | Discount |
|---|---|---|---|
| BGE-M3 | AIGC | $0.0035 per 1M tokens | — |
| Jina Reranker V3 | AIGC | $0.0042 per 1M tokens | — |
Rates follow official provider pricing and are billed in USD. See the full catalogue and pricing plans for credit allowances.
One snippet, two models
BGE-M3 and Jina Reranker V3 share the same OpenAI-compatible request shape. Run the same code against both and compare the answers on your own workload.
from openai import OpenAI
client = OpenAI(
api_key="sk-openlux-...",
base_url="https://openlux.lol/v1",
)
for model in ["bge-m3", "jina-reranker-v3"]:
resp = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "Summarise this release note for me."}],
)
print(model, "->", resp.choices[0].message.content)Any OpenAI SDK works the same way — see the docs for streaming, tools and error codes.
BGE-M3 vs Jina Reranker V3 questions
How much do BGE-M3 and Jina Reranker V3 cost on OpenLux?
BGE-M3 is metered pay as you go at $0.0035 per 1M tokens. Jina Reranker V3 is metered at $0.0042 per 1M tokens. There is no subscription requirement: usage is billed in USD and every request is visible in the console.
Can I call BGE-M3 and Jina Reranker V3 with the same API key?
Yes. Both models sit behind the same OpenAI-compatible endpoint at https://openlux.lol/v1. Keep the same key and change only the "model" field to switch between bge-m3 and jina-reranker-v3 — request and response shapes stay identical.
Which is cheaper, BGE-M3 or Jina Reranker V3?
BGE-M3 costs less per unit: $0.0035 per 1M tokens versus $0.0042 per 1M tokens, about 17% lower for the same volume. Over a month of production traffic the difference compounds, and the console shows cost per request in real time.
When should I pick BGE-M3 over Jina Reranker V3?
In practice the two models are complements, not substitutes: BGE-M3 suits workloads that need embeddings for search, RAG and clustering, retrieval and knowledge pipelines. Jina Reranker V3 is the better fit when you need reranking retrieval results before the answer model runs, retrieval and knowledge pipelines. Both are called with the same OpenLux key, so switching between them is a one-field change to the model name.
Route every request to the right model
OpenLux keeps BGE-M3, Jina Reranker V3 and every other model behind one key, one endpoint and one bill. Start with free trial credit and switch models without touching your code.