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)
162 tokens
Input Price (Auto)
$0.100/1M
Output Price (Auto)
$0.40/1M
Cache Read (Auto)
$0.050/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
19.2
Coding Index
39.3
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
79.2%
Better than 66% of models compared
HLE
Humanity's Last Exam
19.3%
Better than 71% 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
65.7%
Better than 61% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
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
15.5%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
92.1%
Last updated Sep 7, 2026
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