M2.1 builds on M2 with enhanced context understanding and improved complex tool use. 230B parameter MoE model (10B active) optimized for agentic workflows and long-horizon tasks. Running inside a TEE (Trusted Execution Environment), with provider attestation support.
Context Window
200.0K
Max Output
131.1K
Input Price (Auto)
$0.30/1M
Output Price (Auto)
$1.20/1M
Capabilities
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
39.4
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Coding Index
32.8
GPQA Diamond
Graduate-level scientific reasoning
83.0%
Better than 87% of models compared
HLE
Humanity's Last Exam
22.2%
Better than 90% of models compared
IFBench
Instruction-following benchmark
69.9%
Better than 88% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
85.4%
Better than 81% of models compared
AA-LCR
Long context reasoning evaluation
59.0%
Better than 79% of models compared
SciCode
Python programming for scientific computing
40.7%
Better than 86% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
28.8%
Better than 78% of models compared
AIME 2025
American Invitational Mathematics Examination 2025
82.7%
Better than 79% of models compared
MMLU-Pro
Professional and academic subject knowledge
87.5%
Better than 98% of models compared
Last updated May 15, 2026
Artificial AnalysisLiveCodeBench
Contamination-free coding benchmark
81.0%
Better than 93% of models compared
