Nvidia Nemotron 3 Ultra 550B
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.
- Reasoning
- Tool Calling
- Parallel Tool Calls
Added Jun 4, 2026
Model weightsPricing
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)
- 904 tokens
- Longer than 66% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
22.9
Coding Index
49.3
Agentic Index
20.1
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
3.0%
Better than 23% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
0.3%
Better than 3% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
81.7%
Better than 24% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
876 Elo
Better than 39% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1016 Elo
Better than 47% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
5.0%
Better than 24% 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
11.1%
Better than 33% of models compared
Reasoning
HLE
Humanity's Last Exam
28.4%
Better than 76% 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 28% 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
25.0%
Last updated Oct 3, 2026
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