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Google · Open weights

Gemma 4 26B A4B Thinking

Added Apr 2, 2026Open weights

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Use in the app

Google's Gemma 4 26B A4B instruction-tuned model with structured reasoning for more deliberate coding, multimodal analysis, and long-context problem solving. 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)
1.1K tokens
Longer than 72% of 465 models

Side by side

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TryOpenAI o4-mini highStep 3.5 FlashDeepSeek V3.2 Exp ThinkingAll 3

Public evaluations

Benchmarks

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

Intelligence Index

16.7

Better than 44% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    39.3

    Composite of the coding evaluations

    Better than 32% of 168 scored models

Agentic work

  • AutomationBench-AA

    1.9%

    Workflow automation, penalized for guardrail violations

    Better than 12% of 173 scored models

  • AutomationBench-AA tasks completed

    0.2%

    Workflows finished end to end without a guardrail violation

    Better than 13% of 173 scored models

  • AA-Briefcasev1.1

    315 Elo

    Agentic knowledge work, rated head to head

    Better than 10% of 166 scored models

  • GDPval-AAv2.1

    565 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 14% of 187 scored models

Document reasoning

  • AA-LCRv1.1

    65.7%

    Reasoning across long documents

    Better than 42% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    19.3%

    Expert-level questions across academic fields

    Better than 46% of 323 scored models

  • GPQA Diamond

    79.2%

    Graduate-level science questions

    Better than 46% of 291 scored models

  • IFBench

    72.4%

    Following precise output instructions

    Better than 79% of 215 scored models

Coding

  • SciCode

    40.0%

    Scientific computing in Python

    Better than 21% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    13.6%

    Earlier terminal task suite

    Better than 30% of 212 scored models

  • τ²-Bench Telecom

    43.6%

    Tool use in a customer-support simulation

    Better than 35% of 214 scored models

  • AA-LCR (unversioned)

    65.7%

    Long-context reasoning, earlier grading

    Better than 42% of 309 scored models

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
58.6 tok/s
Faster than 41% of 250 models
Time to first token
3.1 s
Quicker than 56% of 250 models
Prompt cache hits
0.5%
About $0.0898 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 96.1%
  • 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:thinking",
    "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:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.