Nemotron Nano 9B V2

Nemotron Nano 9B V2 by NVIDIA excels in the Coding category, where it ranks #256 with a composite score of 40/100. NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Nemotron Nano 9B V2 is priced at $0.04 per million input tokens and $0.16 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Nemotron Nano 9B V2 holds rank #256 out of 341 models tracked. Its quality rank is #256 and adoption rank is #256. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Nemotron Nano 9B V2 has been evaluated across 5 different signals. Its strongest areas include Capabilities (67/100), Pricing (100/100), Context Window (81/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 40/100 reflects a weighted combination of all tracked signals.

Nemotron Nano 9B V2 is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check NVIDIA's website for current free tier availability and promotional offers.

Nemotron Nano 9B V2 supports a 131K token context window (131,072 tokens total). That translates to roughly 98,304 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Nemotron Nano 9B V2 can generate up to 16K output tokens (16,384 tokens) per response. That is roughly 12,288 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Nemotron Nano 9B V2 supports function/tool calling, structured JSON output, extended reasoning/chain-of-thought, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Nemotron Nano 9B V2 is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Nemotron Nano 9B V2 was developed by NVIDIA. It was released on September 5, 2025. You can access it through NVIDIA's API or download the model weights directly. Check our provider page for all models from NVIDIA and how they compare against each other.

Pick Nemotron Nano 9B V2 when you need a budget-friendly option for high-volume, simpler tasks where you prioritize cost over peak performance. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Nemotron Nano 9B V2 through NVIDIA's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.