Google · Open weights

Gemma 4 26B A4B Cybersecurity

Added Sep 19, 2026Open weights

Gemma 4 26B A4B Cybersecurity is a cybersecurity-focused variant based on the uncensored model, with provider moderation for illegal activities. It supports optional reasoning, image understanding, tool calling, and a 262,144-token context window.

Reasoning
Tools
Vision

Input: Text, Images
Output: Text
Discovery tier: Open weights

Pricing

USD per 1M tokens

Input: $0.11
Output: $0.33
Cache read: $0.0528

Reference prices as published by the model provider. API example

Specifications

Context window: 262.1K
Max output: 32.8K
Average output (7 days): 600 tokens; Longer than 56% of 473 models

Side by side

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Public evaluations

Benchmarks

There are no public benchmark results for Gemma 4 26B A4B Cybersecurity yet.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits: 52.5%; About $0.0779 per 1M input tokens with caching
Request success (24h): 67.3%; Failing

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 12.5%

Healthy (98% or more)
Degraded (90–98%)
Down (under 90%)
No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart

curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-4-26b-a4b-it-cybersecurity",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="google/gemma-4-26b-a4b-it-cybersecurity",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.