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 4 Argon google/gemini-4-argon | Kimi K3 moonshot/kimi-k3 | |
|---|---|---|
| Provider | Moonshot | |
| Family | ||
| Modality | text | text |
| Context window | 1,048,576 tok | 1,048,576 tok |
| Max output | 65,536 tok | 32,768 tok |
| Released | 2026-10-14 | 2026-07-16 |
| Input price | $2.00 /1M | $3.00 /1M |
| Output price | $10.00 /1M | $15.00 /1M |
| Cache read | — | — |
| Tools | — | yes |
| Streaming | yes | yes |
| Vision | — | yes |
| JSON mode | — | yes |
| Reasoning | — | yes |
| Prompt caching | — | — |
Google's frontier model for deep work on complex, real-world tasks across coding, finance, legal, cybersecurity and science. The first model in the Gemini 4 family.
Kimi K3 is Moonshot's flagship 2.8 trillion-parameter model, built on Kimi Delta Attention (a hybrid linear attention mechanism) with Attention Residuals. It offers native visual understanding, always-on reasoning, and a 1M-token context window for long-horizon coding, knowledge work, and deep reasoning tasks.
# 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 4 Argon
client.chat.completions.create(
model="google/gemini-4-argon",
messages=[{"role":"user","content":"hello"}],
)
# Try Kimi K3 — same client, same key
client.chat.completions.create(
model="moonshot/kimi-k3",
messages=[{"role":"user","content":"hello"}],
)