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
Model weightsContext Window
128.0K
Max Output
16.4K
Avg output tokens (7d)
2.0K tokens
Input Price (Auto)
$0.020/1M
Output Price (Auto)
$0.100/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
9.0
Coding Index
20.7
Agentic Index
1.4
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.2%
Better than 2% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
0 Elo
Better than 2% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
514 Elo
Better than 22% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
2.0%
Better than 19% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
34.7%
Better than 35% of models compared
Reasoning
HLE
Humanity's Last Exam
11.0%
Better than 56% of models compared
IFBench
Instruction-following benchmark
65.1%
Better than 77% of models compared
CritPt
Research-level physics reasoning
1.4%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 18% of models compared
SciCode
Python programming for scientific computing
38.9%
Better than 25% of models compared
LiveCodeBench
Contamination-free coding benchmark
77.7%
Better than 89% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
89.3%
Better than 88% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
74.8%
Better than 47% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.0%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
94.1%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
68.8%
Better than 46% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
10.6%
Better than 45% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
60.2%
Better than 57% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
34.7%
Better than 35% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.7%
Last updated Sep 13, 2026
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