Private AI
An open-weight 21B parameter model released under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI's Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
Added Aug 5, 2025
Context Window
128.0K
Max Output
16.4K
Input Price (Auto)
$0.030/1M
Output Price (Auto)
$0.15/1M
Capabilities
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
24.5
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Coding Index
18.5
Agentic Index
27.6
GPQA Diamond
Graduate-level scientific reasoning
68.8%
Better than 53% of models compared
HLE
Humanity's Last Exam
9.8%
Better than 64% of models compared
IFBench
Instruction-following benchmark
65.1%
Better than 77% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
60.2%
Better than 57% of models compared
AA-LCR
Long context reasoning evaluation
30.7%
Better than 42% of models compared
GDPval-AA
Economically valuable tasks
7.5%
Better than 32% of models compared
CritPt
Research-level physics reasoning
1.4%
Better than 76% of models compared
SciCode
Python programming for scientific computing
34.4%
Better than 54% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
10.6%
AIME 2025
American Invitational Mathematics Examination 2025
89.3%
Better than 88% of models compared
MMLU-Pro
Professional and academic subject knowledge
74.8%
Better than 48% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
15.5%
Better than 23% of models compared
Last updated Jun 4, 2026
Artificial AnalysisBetter than 44% of models compared
LiveCodeBench
Contamination-free coding benchmark
77.7%
Better than 89% of models compared
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
94.1%
Better than 6% of models compared
