# Nvidia Nemotron Nano 9B V2

A dense 9B reasoning model with 131.1K tokens context. Where the sparse Nemotron 3 Nano trades active parameters for scale, Nvidia Nemotron Nano 9B V2 takes the opposite approach with a focused dense architecture.

- **Model ID:** `nvidia/nemotron-nano-9b-v2`
- **Type:** chat
- **Providers:** bedrock
- **Context window:** 131,072
- **Maximum output tokens:** 131,072
- **Pricing:** $0.06/1M input tokens, $0.23/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/nemotron-nano-9b-v2

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'nvidia/nemotron-nano-9b-v2',
  prompt: 'Why is the sky blue?'
})
```

## About

NVIDIA released Nvidia Nemotron Nano 9B V2 on August 18, 2025 as the compressed reasoning variant of the Nemotron Nano 2 family. It is a 9B-parameter model with a context window of 131.1K tokens.

Nvidia Nemotron Nano 9B V2 matches or exceeds Qwen3-8B on complex reasoning tasks at up to 6x the throughput. The hybrid Mamba-Transformer architecture contributes to this efficiency. Mamba layers handle sequence processing with sub-quadratic memory scaling, while Transformer attention layers maintain precision on retrieval-heavy tasks within the context window.

Nvidia Nemotron Nano 9B V2 also supports thinking budget control. You can prompt it to reason briefly for simple tasks (faster, cheaper) or thoroughly for hard problems (slower, more accurate). Adjust the latency-accuracy tradeoff at inference time without switching models. Technical report and assets: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html.

## What to consider

Nvidia Nemotron Nano 9B V2 is a compact dense reasoning model. Evaluate whether its capability tier fits your workload before committing at production scale. Compare $0.06 and $0.23.

## When to use

### Best For

- High-throughput reasoning workloads where 6x speed over comparable models matters
- Applications that benefit from thinking budget control (variable reasoning depth per request)
- Cost-sensitive production environments where a compact reasoning model reduces infrastructure spend

### Consider Alternatives When

- You need a 1M-token context window. Nemotron 3 Nano (30B/3B active) supports that scale
- You need vision or multimodal capabilities. Nemotron Nano 12B v2 VL is the right choice
- Your task involves multi-agent orchestration better served by the sparse MoE design of Nemotron 3 Nano

## Best for

- **High-throughput reasoning:** Workloads where 6x speed over comparable models matters
- **Thinking budget control:** Applications that vary reasoning depth per request
- **Cost-sensitive production:** Compact reasoning models that reduce infrastructure spend

## Consider alternatives

- **1M-token context:** Nemotron 3 Nano (30B/3B active) supports that scale
- **Vision or multimodal:** Nemotron Nano 12B v2 VL is the right choice
- **Multi-agent orchestration:** The sparse MoE design of Nemotron 3 Nano is better suited to that pattern

## Frequently asked questions

### How is this model different from Nemotron 3 Nano (30B/A3B)?

They use different architectures. Nvidia Nemotron Nano 9B V2 is a dense 9B model with a context window of 131.1K tokens. Nemotron 3 Nano is a sparse MoE (30B total, 3B active) with a 1M-token context for multi-agent throughput. Choose based on whether your constraint is footprint (9B v2) or context scale (Nemotron 3 Nano).

### What does thinking budget control mean in practice?

You can instruct the model to reason briefly or in depth on a per-request basis. Brief reasoning produces faster, cheaper responses for straightforward tasks. Deep reasoning takes longer but improves accuracy on complex problems.

### Where are input and output prices listed?

Pricing appears on this page and updates as providers adjust their rates. AI Gateway routes traffic through the configured provider.

## Links

- [Model page](https://vercel.com/ai-gateway/models/nemotron-nano-9b-v2)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
- [Provider pricing](https://aws.amazon.com/bedrock/pricing/)