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Nvidia Nemotron 3 Ultra 550B Thinking

Nvidia's Nemotron 3 Ultra 550B A55B model from the Nemotron 3 family. It uses a hybrid Mamba-Transformer MoE architecture. Provider-specific context limits vary, with the longest current route supporting up to 1M context. Thinking enabled.

  • Reasoning
  • Tool Calling
  • Parallel Tool Calls

Added Jun 4, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.50
Output
$2.50
Cache read
$0.15

Specifications

Context window
1M
Max output
65.5K
Parameters
550B / 55B
Total / active
Avg output (7d)
752 tokens
Longer than 63% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

22.9

Better than 74% of models compared

Coding Index

49.3

Better than 57% of models compared

Agentic Index

20.1

Better than 55% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

3.0%

Better than 22% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.3%

Better than 24% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

876 Elo

Better than 37% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

1011 Elo

Better than 45% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

5.0%

Better than 25% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

79.3%

Better than 83% of models compared

MLCR-AA

Medical long-context reasoning

5.6%

Better than 17% of models compared

Reasoning

HLE

Humanity's Last Exam

28.4%

Better than 75% of models compared

IFBench

Instruction-following benchmark

81.4%

Better than 99% of models compared

CritPt

Research-level physics reasoning

3.1%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.5%

Better than 35% of models compared

SciCode

Python programming for scientific computing

40.3%

Better than 26% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

22.6%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

29.7%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

86.7%

Better than 82% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

36.4%

Better than 83% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

83.3%

Better than 73% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

79.3%

Better than 83% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

25.5%

Last updated Oct 22, 2026

Artificial Analysis

Providers

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