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Gemma 3 4B IT

Added Jan 1, 2024Open weights

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Use in the app

Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions.

  • PDF input
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
8.2K
Average output (7 days)
115 tokens
Longer than 11% of 468 models

Side by side

Gemma 3 4B IT compared

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

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Gemma 3 4B ITGoogleThis modelGemma 4 26B A4BGoogleRemove
ProviderGoogleGoogle
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 2, 2026
Limits
Context window128K262.14KBest
Max output8.19K131.07KBest
Average output115 tokens131 tokens
Pricing, USD per 1M tokens
Input$0.20$0.09Best
Output$0.20Best$0.30
Cache read—$0.05
Performance
Throughput—62.5 tok/s
Time to first token—2.2 s
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index4.816.7Best
Coding Index2.739.3Best
Math Index12.7—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputYesNo
Video inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Gemma 3 4B IT
  • Gemma 4 26B A4B

Intelligence Index

  • Gemma 3 4B IT4.8
  • Gemma 4 26B A4B16.7

Gemma 3 4B IT: Better than 0% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemma 3 4B IT: 2.7
    • Gemma 4 26B A4B: 39.3Top

  • Math Index

    Composite of the math evaluations

    • Gemma 3 4B IT: 12.7
    • Gemma 4 26B A4B: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemma 3 4B IT: —
    • Gemma 4 26B A4B: 1.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemma 3 4B IT: —
    • Gemma 4 26B A4B: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemma 3 4B IT: —
    • Gemma 4 26B A4B: 315 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemma 3 4B IT: —
    • Gemma 4 26B A4B: 565 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemma 3 4B IT: 6.7%
    • Gemma 4 26B A4B: 65.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemma 3 4B IT: 5.3%
    • Gemma 4 26B A4B: 19.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemma 3 4B IT: 29.1%
    • Gemma 4 26B A4B: 79.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemma 3 4B IT: 41.7%
    • Gemma 4 26B A4B: —

  • IFBench

    Following precise output instructions

    • Gemma 3 4B IT: 28.3%
    • Gemma 4 26B A4B: 72.4%Top

Coding

  • SciCode

    Scientific computing in Python

    • Gemma 3 4B IT: —
    • Gemma 4 26B A4B: 40.0%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemma 3 4B IT: 11.2%
    • Gemma 4 26B A4B: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemma 3 4B IT: 12.7%
    • Gemma 4 26B A4B: —

  • AIME

    Competition mathematics

    • Gemma 3 4B IT: 6.3%
    • Gemma 4 26B A4B: —

  • MATH-500

    Competition math problems

    • Gemma 3 4B IT: 76.6%
    • Gemma 4 26B A4B: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemma 3 4B IT: 0.8%
    • Gemma 4 26B A4B: 13.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemma 3 4B IT: 5.0%
    • Gemma 4 26B A4B: 43.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemma 3 4B IT: 6.7%
    • Gemma 4 26B A4B: 65.7%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.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 66.6%
  • 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": "unsloth/gemma-3-4b-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="unsloth/gemma-3-4b-it",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.