Gemma 4 26B A4B

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
  • Vision
  • Video Input
  • Structured Output

Added Apr 2, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.068
Output
$0.23
Cache read
$0.037
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Specifications

Context window
262.1K
Max output
131.1K
Parameters
26B / 4B
Total / active
Avg output (7d)
125 tokens
Longer than 8% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

16.7

Better than 59% of models compared

Coding Index

39.3

Better than 46% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.9%

Better than 20% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

311 Elo

Better than 17% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

569 Elo

Better than 29% of models compared

Document reasoning

AA-LCR v1.1

Long context reasoning with updated grading

65.7%

Better than 57% of models compared

Reasoning

HLE

Humanity's Last Exam

19.3%

Better than 67% of models compared

IFBench

Instruction-following benchmark

72.4%

Better than 89% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench Hard (legacy)

Legacy fallback · Agentic coding and terminal use

13.6%

Better than 51% of models compared

SciCode

Python programming for scientific computing

40.0%

Better than 26% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

15.5%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

92.1%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

79.2%

Better than 66% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

43.6%

Better than 48% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

65.7%

Better than 57% of models compared

Last updated Oct 3, 2026

Artificial Analysis

Providers

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