Nemotron 3 Ultra

Largest Nemotron 3 model for maximum open-weight reasoning and agent accuracy
- Context
- 262K
- Released
- 4mo ago
- Parameters
- 550B
- Input / 1M tokens
- $0.60
- Output / 1M tokens
- $2.40
- Cached input / 1M tokens
- $0.12
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
Other published benchmarks
| Evaluation | Reported score and source |
|---|---|
| GPQA diamond | |
| OTIS Mock AIME 2024-2025 |
Benchmark compilation: Epoch AI, Capabilities & benchmarking (CC BY attribution). Scores are converted to percentages and model names matched to the Vertical Tabs catalog. Individual evaluations are credited to their original sources. External results may carry separate licences and evaluation configurations; do not interpret scores from different harnesses as directly comparable.
Specifications
- Developer
- NVIDIA
- Model family
- Nemotron
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- June 4, 2026
- Model ID
nvidia/nemotron-3-ultra-550b-a55b- Openness
- Open weights
- License
- —
- Parameters
- 550B
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Native context
- 262K
- Tokenizer
- Other
- Max output tokens
- 183K
- Hugging Face
- nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- nvidia/nemotron-3-ultra-550b-a55b
- Source family
- nemotron
- Last updated
- 2026-06-04
- Model context
- 1M
- Model output limit
- 128K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature control
- Yes
Published benchmarks
| Benchmark | Score | Evaluation details | Evidence |
|---|---|---|---|
| SWE-Bench Verified | 70.7 · resolved | 2026-06-04 | Source |
| SWE-Bench Multilingual | 67.7 · resolve rate | 2026-06-04 | Source |
| Terminal-Bench | 56.4 · success rate | 2.1 · 2026-06-04 | Source |
| GPQA | 87 · accuracy | no tools · 2026-06-04 | Source |
| Humanity's Last Exam | 26.7 · accuracy | no tools · 2026-06-04 | Source |
| Humanity's Last Exam | 37.4 · accuracy | with tools · 2026-06-04 | Source |
| LiveCodeBench | 89 · pass@1 | v6 · 2026-06-04 | Source |
| MMLU-Pro | 86.8 · accuracy | 2026-06-04 | Source |
| BrowseComp | 44.4 · accuracy | 2026-06-04 | Source |
| IFBench | 81.7 · accuracy | prompt loose · 2026-06-04 | Source |
| GDPval | 46.7 · wins or ties | 2026-06-04 | Source |
Complete source record (JSON)
{
"id": "nvidia/nemotron-3-ultra-550b-a55b",
"name": "Nemotron 3 Ultra 550B A55B",
"description": "Largest Nemotron 3 model for maximum open-weight reasoning and agent accuracy",
"family": "nemotron",
"last_updated": "2026-06-04",
"attachment": false,
"reasoning": true,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 1000000,
"output": 128000
},
"benchmarks": [
{
"name": "SWE-Bench Verified",
"score": 70.7,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "resolved",
"date": "2026-06-04"
},
{
"name": "SWE-Bench Multilingual",
"score": 67.7,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "resolve rate",
"date": "2026-06-04"
},
{
"name": "Terminal-Bench",
"score": 56.4,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "success rate",
"version": "2.1",
"date": "2026-06-04"
},
{
"name": "GPQA",
"score": 87,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"variant": "no tools",
"date": "2026-06-04"
},
{
"name": "Humanity's Last Exam",
"score": 26.7,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"variant": "no tools",
"date": "2026-06-04"
},
{
"name": "Humanity's Last Exam",
"score": 37.4,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"variant": "with tools",
"date": "2026-06-04"
},
{
"name": "LiveCodeBench",
"score": 89,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "pass@1",
"version": "v6",
"date": "2026-06-04"
},
{
"name": "MMLU-Pro",
"score": 86.8,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"date": "2026-06-04"
},
{
"name": "BrowseComp",
"score": 44.4,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"date": "2026-06-04"
},
{
"name": "IFBench",
"score": 81.7,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "accuracy",
"variant": "prompt loose",
"date": "2026-06-04"
},
{
"name": "GDPval",
"score": 46.7,
"source": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
"metric": "wins or ties",
"date": "2026-06-04"
}
],
"release_date": "2026-06-04",
"open_weights": true
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| deepinfra/fp4 | 262K | $0.50 | $2.20 | $0.10 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| baseten/fp4 | 203K | $0.60 | $2.40 | $0.12 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| baseten/fp4/us | 203K | $0.60 | $2.40 | $0.12 | — | 99.92% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| venice/fp8 | 256K | $0.63 | $3.13 | $0.19 | — | 99.68% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.