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Gemma 3 27B IT

unsloth/gemma-3-27b-it
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Gemma 3 27B IT

unsloth/gemma-3-27b-it
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Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions.

Context Window

128.0K

Max Output

96.0K

Avg output tokens (7d)

215 tokens

15%

Input Price (Auto)

$0.30/1M

Output Price (Auto)

$0.30/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

4.9

Better than 3% of models compared

Coding Index

10.1

Better than 10% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

0.2%

Better than 1% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

0 Elo

Better than 3% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

-424 Elo

Better than 1% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

0.2%

Better than 1% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

7.3%

Better than 14% of models compared

Reasoning

HLE

Humanity's Last Exam

4.4%

Better than 20% of models compared

IFBench

Instruction-following benchmark

31.8%

Better than 18% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

23.3%

Better than 4% of models compared

LiveCodeBench

Contamination-free coding benchmark

13.7%

Better than 13% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

20.7%

Better than 22% of models compared

AIME

American Invitational Mathematics Examination

25.3%

Better than 52% of models compared

Math-500

Diverse mathematical problem solving benchmark

88.3%

Better than 59% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

66.9%

Better than 29% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

42.8%

Better than 18% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

3.8%

Better than 26% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

10.5%

Better than 7% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

7.3%

Better than 14% of models compared

Last updated Oct 1, 2026

Artificial Analysis

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