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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
$0.076
Output
$0.26
Cache read
$0.042

Specifications

Context window
262.1K
Max output
131.1K
Parameters
26B / 4B
Total / active
Avg output (7d)
174 tokens
Longer than 18% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

16.7

Better than 62% of models compared

Coding Index

39.3

Better than 45% of models compared

Agentic Index

2.9

Better than 30% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.9%

Better than 19% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.2%

Better than 19% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

315 Elo

Better than 15% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

566 Elo

Better than 27% of models compared

Document reasoning

AA-LCR v1.1

Long context reasoning with updated grading

65.7%

Better than 56% of models compared

Reasoning

HLE

Humanity's Last Exam

19.3%

Better than 65% 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 25% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

19.1%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

86.4%

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 56% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

3.3%

Last updated Oct 22, 2026

Artificial Analysis

Providers

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