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Google · Open weights

Gemma 4 26B A4B

Added Apr 2, 2026Open weights

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Use in the app

Google's Gemma 4 26B A4B instruction-tuned model built for scalable reasoning, coding, long-context, and multimodal workflows. This route is tuned for faster direct answers while preserving multimodal and structured output support. Requests containing video cost $0.10 per million input tokens and $0.40 per million output tokens.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.09
Output
$0.30
Cache read
$0.05

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
131.1K
Average output (7 days)
131 tokens
Longer than 13% of 465 models

Side by side

Gemma 4 26B A4B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Gemma 4 26B A4BGoogleThis modelGemma 4 31B ThinkingGoogleRemove
ProviderGoogleGoogle
Discovery tierOpen weightsOpen weights
AddedApr 2, 2026Apr 2, 2026
Limits
Context window262.14K262.14K
Max output131.07K131.07K
Average output131 tokens1.1K tokens
Pricing, USD per 1M tokens
Input$0.09Best$0.10
Output$0.30Best$0.34
Cache read$0.05Best$0.10
Performance
Throughput72.7 tok/sBest26.4 tok/s
Time to first token2.9 sBest9.0 s
Request success (24h)100.0%Best99.7%
Benchmark indexes
Intelligence Index16.7Best14.7
Coding Index39.343.4Best
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
Video inputYesYes
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Gemma 4 26B A4B
  • Gemma 4 31B Thinking

Intelligence Index

  • Gemma 4 26B A4B16.7
  • Gemma 4 31B Thinking14.7

Gemma 4 26B A4B: Better than 44% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemma 4 26B A4B: 39.3
    • Gemma 4 31B Thinking: 43.4Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemma 4 26B A4B: 1.9%
    • Gemma 4 31B Thinking: 4.9%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemma 4 26B A4B: 0.2%
    • Gemma 4 31B Thinking: 0.5%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemma 4 26B A4B: 315 Elo
    • Gemma 4 31B Thinking: 368 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemma 4 26B A4B: 565 Elo
    • Gemma 4 31B Thinking: 619 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemma 4 26B A4B: —
    • Gemma 4 31B Thinking: 6.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemma 4 26B A4B: 65.7%
    • Gemma 4 31B Thinking: 69.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemma 4 26B A4B: 19.3%
    • Gemma 4 31B Thinking: 23.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemma 4 26B A4B: 79.2%
    • Gemma 4 31B Thinking: 85.7%Top

  • IFBench

    Following precise output instructions

    • Gemma 4 26B A4B: 72.4%
    • Gemma 4 31B Thinking: 75.6%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemma 4 26B A4B: —
    • Gemma 4 31B Thinking: 0.0%

  • SciCode

    Scientific computing in Python

    • Gemma 4 26B A4B: 40.0%
    • Gemma 4 31B Thinking: 45.5%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemma 4 26B A4B: 13.6%
    • Gemma 4 31B Thinking: 36.4%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemma 4 26B A4B: 43.6%
    • Gemma 4 31B Thinking: 59.9%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemma 4 26B A4B: 65.7%
    • Gemma 4 31B Thinking: 69.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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.

Throughput
72.7 tok/s
Faster than 50% of 251 models
Time to first token
2.9 s
Quicker than 56% of 251 models
Prompt cache hits
16.9%
About $0.0832 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

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 98.6%
  • 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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.