Pricing per million tokens, context window, capabilities — pulled from each provider's public docs. All 2 are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Multi-Functionality, Multi-Linguality, and Multi-Granularity embeddings model.
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Bge-M3
client.chat.completions.create(
model="baai/bge-m3",
messages=[{"role":"user","content":"hello"}],
)
# Bge-Reranker-Base
client.chat.completions.create(
model="baai/bge-reranker-base",
messages=[{"role":"user","content":"hello"}],
)