NVIDIA Nemotron Nano 9B V2
NVIDIA Nemotron Nano 9B V2 is a compact open chat model from NVIDIA's Nemotron family, positioned at the small end of the lineup for latency- and cost-sensitive deployments.
API Pricing
Cheapest on Runcrate — 20% below avg| Provider | Input / 1M | Output / 1M |
|---|---|---|
| $0.040 | $0.160 | |
| $0.060 | $0.250 |
Prices updated daily. Last check: Oct 12, 2026
Compare API pricing for every Nemotron model →NVIDIA Nemotron Nano 9B V2 pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Reasoning & Knowledge
- MMLU-Pro73.9%
- GPQA Diamond55.7%
- Humanity's Last Exam4.6%
Coding
- LiveCodeBench70.1%
Math
- AIME 202562.3%
Agentic & Tool Use
- Terminal-Bench Hard0.8%
- τ²-bench23.4%
Instruction & Long Context
- IFBench27.1%
- Long-Context Reasoning24.0%
Benchmarks measured Oct 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- NVIDIA
- Modalities
- Text
Capabilities
- Open Source
- Yes
Strengths & Limitations
Strengths
- Small 9B-class parameter scale, which generally allows serving on a single GPU and lowers per-token inference cost
- Measured output throughput of roughly 137 tokens per second in Artificial Analysis benchmarking, suitable for streaming interfaces
- Part of NVIDIA's Nemotron family, so it shares a naming and release cadence with larger siblings that can be used as an escalation tier
- Second-generation (V2) revision of the 9B Nano line rather than an initial release
- Small-tier positioning makes it a practical candidate for high-volume, repetitive text workloads where a frontier model would be overprovisioned
- Distributed through multiple inference providers, so throughput and pricing can be compared across hosts on this page
Limitations
- Time to first token measured at roughly 1,159 ms, which is slower to start than the quickest small models even though sustained generation is fast
- A ~9B-parameter model will generally trail much larger chat models on complex multi-step reasoning, long-form coding, and specialist knowledge
- We do not track a confirmed context window length for this entry, so long-document workloads require checking the provider's stated limit
- Tool calling, structured output, and vision support are not confirmed in our metadata and should be verified per provider
- Benchmark accuracy scores (MMLU, coding, math) are not recorded for this model in our database, making direct quality comparisons harder
Key Features
About NVIDIA Nemotron Nano 9B V2
Common Use Cases
Nemotron Nano 9B V2 fits workloads where volume and unit cost dominate the decision: customer-support drafting, summarization of tickets or documents, content classification and tagging, data extraction into structured text, and lightweight in-product assistants. Its measured generation speed of around 137 tokens per second makes it reasonable for streaming chat UIs where users see text appear incrementally, though the roughly 1.16-second first-token latency means it is less ideal for very short, snappy request/response patterns such as autocomplete. As a small-tier model it also works well as the first stage in a cascade — handling routine requests directly and routing harder ones to a larger Nemotron or a frontier model — and its 9B scale makes it a candidate for self-hosting when data residency or steady-state cost favors running your own GPUs over per-token API billing.
Frequently Asked Questions
How much does NVIDIA Nemotron Nano 9B V2 cost?
Pricing depends on which provider hosts the model and how it is billed — per input and output token for serverless APIs, or per GPU-hour if you self-host. Rates change frequently and differ between hosts, so check the live pricing table on this page for current per-provider numbers.
What is Nemotron Nano 9B V2 best used for?
It suits high-volume, cost-sensitive text work: summarization, classification, extraction, drafting, and embedded product assistants. Its small parameter class keeps serving cheap and fast, which matters more for these tasks than the extra reasoning depth of a much larger model.
How fast is it?
Artificial Analysis benchmarking records approximately 137 output tokens per second with a time to first token of about 1,159 ms. Sustained generation is quick; the initial response delay is moderate. Actual figures vary by provider, region, prompt length, and load.
How does it compare to larger Nemotron models?
The Nano tier is the small end of NVIDIA's Nemotron lineup. Expect lower cost and faster, cheaper serving than the larger family members, with correspondingly less capability on complex reasoning and long, intricate generations. A common pattern is to run Nano for routine traffic and escalate difficult requests to a bigger model.
Does it support images or tool calling?
Our database does not record confirmed multimodal input or function-calling support for this entry, so we cannot state either way. Check the documentation of the specific provider you plan to use, since feature support for open models often varies by host.
What does the "V2" mean?
It marks this as a second-generation revision of NVIDIA's Nemotron Nano 9B model rather than the original release. Providers may list both generations, so confirm the exact version string when comparing endpoints.