An open-weight, 117B-parameter Mixture-of-Experts (MoE) language model designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Added Feb 3, 2026
Context Window
128.0K
Max Output
16.4K
Input Price (Auto)
$0.041/1M
Output Price (Auto)
$0.19/1M
Capabilities
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
33.3
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Coding Index
28.6
Agentic Index
37.9
GPQA Diamond
Graduate-level scientific reasoning
78.2%
Better than 79% of models compared
HLE
Humanity's Last Exam
18.5%
Better than 87% of models compared
IFBench
Instruction-following benchmark
69.0%
Better than 87% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
65.8%
Better than 67% of models compared
AA-LCR
Long context reasoning evaluation
50.7%
Better than 67% of models compared
GDPval-AA
Economically valuable tasks
22.3%
Better than 79% of models compared
CritPt
Research-level physics reasoning
1.1%
Better than 85% of models compared
SciCode
Python programming for scientific computing
38.9%
Better than 79% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
23.5%
Better than 71% of models compared
AIME 2025
American Invitational Mathematics Examination 2025
93.4%
Better than 94% of models compared
MMLU-Pro
Professional and academic subject knowledge
80.8%
Better than 71% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
21.5%
Better than 71% of models compared
Last updated May 15, 2026
Artificial AnalysisLiveCodeBench
Contamination-free coding benchmark
87.8%
Better than 98% of models compared
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
91.2%
Better than 13% of models compared
