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.
Added Apr 2, 2026
Model weightsContext Window
262.1K
Max Output
131.1K
Avg output tokens (7d)
398 tokens
Input Price (Auto)
$0.080/1M
Output Price (Auto)
$0.33/1M
Cache Read (Auto)
$0.040/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
26.1
Coding Index
39.3
Agentic Index
11.0
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
79.2%
Better than 69% of models compared
HLE
Humanity's Last Exam
19.3%
Better than 74% of models compared
IFBench
Instruction-following benchmark
72.4%
Better than 89% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
43.6%
Better than 48% of models compared
AA-LCR
Long context reasoning evaluation
61.7%
Better than 65% of models compared
GDPval-AA
Economically valuable tasks
13.5%
CritPt
Research-level physics reasoning
0.0%
Coding
SciCode
Python programming for scientific computing
40.0%
Better than 71% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
13.6%
Better than 51% 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%
Last updated Aug 18, 2026
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