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Google DeepMind·Apache 2.0
Gemma 4 26B A4B IT
26B MoE · ~4B active · Instruction-tuned
Mixture-of-Experts Gemma 4 — 25.2B total parameters with ~3.8B active per token. Built for high-throughput voice and agent workloads with low TTFT.
Model details
| Developed by | Google DeepMind |
| Model family | Gemma 4 |
| Architecture | Mixture-of-Experts (MoE) |
| Total parameters | 25.2B |
| Active parameters | ~3.8B per token |
| Experts | 8 active / 128 total + 1 shared |
| Context length | 256K tokens |
| Modalities | Text, Image → Text |
| License | Apache 2.0 |
On Autoloops
| Hardware | H100 |
| API | OpenAI-compatible |
| Region | US West |
| Pricing | $0.13 / $0.45 / $0.02 per 1M tokens (input / output / cached) |
| Model ID | gemma-4-26b-a4b-it |
Capabilities
- MoE efficiency — dense-model quality at roughly 4B active compute per token
- Tuned on Autoloops for low time-to-first-token under concurrent voice calls
- Instruction-tuned with thinking mode and native function calling
- Image understanding with variable aspect ratios and resolutions
- OpenAI-compatible chat completions via Autoloops
OpenAI-compatible chat completion
from openai import OpenAI
import os
client = OpenAI(
api_key=os.environ["AUTOLOOPS_API_KEY"],
base_url="https://api.autoloops.ai/v1",
)
stream = client.chat.completions.create(
model="gemma-4-26b-a4b-it",
messages=[{"role": "user", "content": "Tell me a fun fact about cats."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")