Google's Gemma 4 31B instruction-tuned model for heavier reasoning, coding, agentic workflows, and long-context multimodal understanding. This route keeps tokenizer thinking disabled for faster direct answers.
Added Apr 2, 2026
Model weightsContext Window
262.1K
Max Output
131.1K
Input Price (Auto)
$0.11/1M
Output Price (Auto)
$0.37/1M
Cache Read (Auto)
$0.053/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
29.4
Coding Index
43.4
Agentic Index
14.4
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
85.7%
Better than 88% of models compared
HLE
Humanity's Last Exam
22.7%
Better than 86% of models compared
IFBench
Instruction-following benchmark
75.6%
Better than 93% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
59.9%
Better than 58% of models compared
AA-LCR
Long context reasoning evaluation
62.0%
Better than 78% of models compared
GDPval-AA
Economically valuable tasks
15.5%
CritPt
Research-level physics reasoning
1.4%
Coding
SciCode
Python programming for scientific computing
43.4%
Better than 87% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
36.4%
Better than 85% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
20.0%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
85.0%
Last updated Jun 28, 2026
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