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 2.5 Pro google/gemini-2.5-pro | MiniMax M3 minimax/m3 | |
|---|---|---|
| Provider | MiniMax | |
| Family | MiniMax M | |
| Modality | text | text |
| Context window | 1,000,000 tok | 1,000,000 tok |
| Max output | 65,536 tok | 4,096 tok |
| Released | 2026-05-22 | 2026-06-01 |
| Input price | $1.25 /1M | $0.300 /1M |
| Output price | $10.00 /1M | $1.20 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | yes | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
Google's most capable Gemini 2.5 model with strong reasoning, thinking support, and a 1M token context window.
MiniMax's M3 language model with frontier coding and agentic capabilities, a 1M token context window, and multilingual support.
# 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 2.5 Pro
client.chat.completions.create(
model="google/gemini-2.5-pro",
messages=[{"role":"user","content":"hello"}],
)
# Try MiniMax M3 — same client, same key
client.chat.completions.create(
model="minimax/m3",
messages=[{"role":"user","content":"hello"}],
)