Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| BGE-Reranker-Base baai/bge-reranker-base | Bge-Small-EN-V1.5 baai/bge-small-en-v1.5 | |
|---|---|---|
| Provider | BAAI | BAAI |
| Family | BGE | BGE |
| Modality | reranking | embedding |
| Context window | — | — |
| Max output | — | — |
| Released | 2025-02-14 | 2023-11-07 |
| Input price | $0.0031 /1M | $0.020 /1M |
| Output price | — | — |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | — |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
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.
BAAI general embedding (Small) model that transforms any given text into a 384-dimensional vector
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try BGE-Reranker-Base
client.chat.completions.create(
model="baai/bge-reranker-base",
messages=[{"role":"user","content":"hello"}],
)
# Try Bge-Small-EN-V1.5 — same client, same key
client.chat.completions.create(
model="baai/bge-small-en-v1.5",
messages=[{"role":"user","content":"hello"}],
)