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
Avg output tokens (7d)
357 tokens
Input Price (Auto)
$0.13/1M
Output Price (Auto)
$0.40/1M
Cache Read (Auto)
$0.065/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
22.2
Coding Index
43.4
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
85.7%
Better than 79% of models compared
HLE
Humanity's Last Exam
23.6%
Better than 76% 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 56% of models compared
AA-LCR
Long context reasoning evaluation
69.7%
Better than 66% of models compared
GDPval-AA
Economically valuable tasks
9.7%
CritPt
Research-level physics reasoning
0.0%
Coding
SciCode
Python programming for scientific computing
45.5%
Better than 37% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
36.4%
Better than 83% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.6%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
81.9%
Last updated Sep 7, 2026
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