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.
| Gemini 3.1 Pro google/gemini-3.1-pro | Bge-M3 baai/bge-m3 | |
|---|---|---|
| Provider | BAAI | |
| Family | Gemini 3 | BGE |
| Modality | text | embedding |
| Context window | 1,000,000 tok | 60,000 tok |
| Max output | 65,536 tok | — |
| Released | 2026-04-13 | 2024-05-22 |
| Input price | $2.00 /1M | $0.012 /1M |
| Output price | $12.00 /1M | — |
| Cache read | $0.200 /1M | — |
| Tools | yes | — |
| Streaming | yes | — |
| Vision | yes | — |
| JSON mode | yes | — |
| Reasoning | yes | — |
| Prompt caching | yes | — |
Google's most intelligent Gemini model with improved reasoning, a medium thinking level, and a 1M token context window.
Multi-Functionality, Multi-Linguality, and Multi-Granularity embeddings model.
# 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 Gemini 3.1 Pro
client.chat.completions.create(
model="google/gemini-3.1-pro",
messages=[{"role":"user","content":"hello"}],
)
# Try Bge-M3 — same client, same key
client.chat.completions.create(
model="baai/bge-m3",
messages=[{"role":"user","content":"hello"}],
)