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-Base-EN-V1.5 baai/bge-base-en-v1.5 | Bge-Large-EN-V1.5 baai/bge-large-en-v1.5 | |
|---|---|---|
| Provider | BAAI | BAAI |
| Family | BGE | BGE |
| Modality | embedding | embedding |
| Context window | 153,600 tok | — |
| Max output | — | — |
| Released | 2023-09-25 | 2023-11-07 |
| Input price | $0.067 /1M | $0.204 /1M |
| Output price | — | — |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | — |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector
BAAI general embedding (Large) model that transforms any given text into a 1024-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-Base-EN-V1.5
client.chat.completions.create(
model="baai/bge-base-en-v1.5",
messages=[{"role":"user","content":"hello"}],
)
# Try Bge-Large-EN-V1.5 — same client, same key
client.chat.completions.create(
model="baai/bge-large-en-v1.5",
messages=[{"role":"user","content":"hello"}],
)