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Nvidia Nemotron 3 Super 120B

nvidia/nemotron-3-super-120b-a12b
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Nvidia Nemotron 3 Super 120B

nvidia/nemotron-3-super-120b-a12b

Nvidia's Nemotron 3 Super 120B A12B model from the March 2026 Nemotron 3 release. It uses a hybrid Mamba-Transformer MoE architecture and targets agentic and coding workloads with a 262K context window here.

Added Mar 1, 2026

Model weights

Context Window

262.1K

Max Output

16.4K

Avg output tokens (7d)

659 tokens

54%

Input Price (Auto)

$0.050/1M

Output Price (Auto)

$0.25/1M

Cache Read (Auto)

$0.025/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

13.6

Better than 55% of models compared

Coding Index

37.7

Better than 44% of models compared

Agentic Index

4.1

Better than 31% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

3.8%

Better than 29% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

0 Elo

Better than 2% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

644 Elo

Better than 29% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.6%

Better than 21% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

65.7%

Better than 61% of models compared

Reasoning

HLE

Humanity's Last Exam

20.8%

Better than 72% of models compared

IFBench

Instruction-following benchmark

71.5%

Better than 88% of models compared

CritPt

Research-level physics reasoning

3.1%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 17% of models compared

SciCode

Python programming for scientific computing

36.2%

Better than 17% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

24.3%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

87.0%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

80.0%

Better than 67% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

28.8%

Better than 71% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

67.8%

Better than 61% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

65.7%

Better than 61% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

7.2%

Last updated Sep 13, 2026

Artificial Analysis

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