Nemotron 3 Nano 30B A3B
NVIDIA · released Dec 14, 2025
NVIDIA Nemotron 3 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems. The model is fully...
Specification
- Context window
- 262K
- Max output
- 262K
- Knowledge cutoff
- Not stated
- Parameters
- 31.6B
- Licence
- other
- Serving providers
- 3
- Moderated
- No
- Uptime
- 100.0%
Intelligence
7.2
24th percentile
Coding
—
Coding Index
Agentic
2.0
Agentic Index
Output speed
138 t/s
Median across providers
Latency
353ms
Time to first token
Cost per task
$0.02
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro57.9%
- GPQA Diamond39.9%
- IFBench37.5%
- LiveCodeBench36.0%
- τ²-bench (Telecom)25.4%
- SciCode23.0%
- AIME 202513.3%
- Terminal-Bench Hard12.1%
- AA-LCR (long context)8.7%
- Humanity's Last Exam4.6%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 57.9% |
| GPQA Diamond | 39.9% |
| IFBench | 37.5% |
| LiveCodeBench | 36.0% |
| τ²-bench (Telecom) | 25.4% |
| SciCode | 23.0% |
| AIME 2025 | 13.3% |
| Terminal-Bench Hard | 12.1% |
| AA-LCR (long context) | 8.7% |
| Humanity's Last Exam | 4.6% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Command ACohere7.5
- Nemotron 3 Nano 30B A3BNVIDIA7.2
- Mistral Large 2407Mistral AI7.0
- 6.9
- GLM 4.5VZ.ai6.8
- GPT-4OpenAI6.8
- Gemini 2.5 Flash LiteGoogle6.7
- GPT-4o-miniOpenAI6.7
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Command A | 7.5 |
| Nemotron 3 Nano 30B A3B | 7.2 |
| Mistral Large 2407 | 7.0 |
| Qwen2.5 Coder 32B Instruct | 6.9 |
| GLM 4.5V | 6.8 |
| GPT-4 | 6.8 |
| Gemini 2.5 Flash Lite | 6.7 |
| GPT-4o-mini | 6.7 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.05
- Output / 1M tokens
- $0.2
- Cached input / 1M
- $0.03
- Blended 3:1
- $0.088
One task, estimated
$0.02
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).