Nemotron Nano 9B v2 Pricing

    OSS

    As of August 25, 2026, Nemotron Nano 9B v2 API pricing is $0.00 per million input tokens and $0.00 per million output tokens. A typical 100K-in / 20K-out request costs $0.00. Source: https://openrouter.ai/api/v1/models

    Source: OpenRouter pricing (https://openrouter.ai/api/v1/models)

    Updated August 25, 2026128K context$0.00 blended / 1M

    Input $/M

    $0.00

    Output $/M

    $0.00

    Blended / 1M

    $0.00

    100K / 20K request

    $0.00

    Where it ranks

    Percentile against every model with a published score on that eval. Click a row for the full board.

    Quality vs price

    GPQA against blended input+output $/M. Log price. Nemotron Nano 9B v2 is the green point.

    Nemotron Nano 9B v2 specs

    Context Window128K
    Max OutputN/A
    Release Date2025-08-18
    Knowledge Cutoff2024-09-01
    LicenseOpen Source
    ThroughputN/A

    Nemotron Nano 9B v2 benchmarks

    Compare Nemotron Nano 9B v2

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    Nemotron Nano 9B v2 FAQ

    How much does Nemotron Nano 9B v2 cost per million tokens?

    As of August 25, 2026, Nemotron Nano 9B v2 OpenRouter API pricing is $0.00 per million input tokens and $0.00 per million output tokens via OpenRouter. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://openrouter.ai/api/v1/models

    What is the blended API cost for Nemotron Nano 9B v2?

    On this page the blended Nemotron Nano 9B v2 API cost is $0.00 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $0.00.

    How much does a typical Nemotron Nano 9B v2 request cost?

    A typical Nemotron Nano 9B v2 API request with 100K input tokens and 20K output tokens costs about $0.00 at current rates as of August 25, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from OpenRouter can bring the real invoice lower than the public list rate at https://openrouter.ai/api/v1/models.

    What is the context window for Nemotron Nano 9B v2?

    Nemotron Nano 9B v2 supports a 128K token context window. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.

    Is Nemotron Nano 9B v2 open source?

    Yes. Nemotron Nano 9B v2 is an open-weight model by NVIDIA. You can download the weights and self-host, or pay OpenRouter's hosted API and skip GPU ops. Hosted API pricing on this page is the public per-million-token rate, not the electricity cost of running the weights yourself.

    What benchmarks does Nemotron Nano 9B v2 perform well on?

    Nemotron Nano 9B v2 currently reports GPQA 64%, LiveCodeBench 71.1%, IFEval 90.3%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.

    Is Nemotron Nano 9B v2 good for coding?

    Nemotron Nano 9B v2 scores 71.1% on LiveCodeBench. Pair that score with OpenRouter API pricing on this page: a slightly worse SWE-bench model can still win if it is much cheaper per million tokens. Open the SWE-bench or coding leaderboard from the sidebar for the full ranking, then jump into an LLM comparison against a coding specialist.

    What is the knowledge cutoff for Nemotron Nano 9B v2?

    Nemotron Nano 9B v2 has a published knowledge cutoff of 2024-09-01. Facts after that date are not guaranteed to be in the weights. For current events you still need retrieval or tools, which adds input tokens and therefore API cost per million tokens.

    How do I compare Nemotron Nano 9B v2 with another LLM?

    Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated Nemotron Nano 9B v2 vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.

    What are the cheapest alternatives to Nemotron Nano 9B v2?

    The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut Nemotron Nano 9B v2. Open-weight models can be cheaper still if you self-host, but that is not the same as OpenRouter API pricing on this page.

    How do I use Nemotron Nano 9B v2 in my application?

    Nemotron Nano 9B v2 is called through OpenRouter's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for OpenRouter and 5+ other providers with auth, payments, and deployment already configured.

    How accurate is this Nemotron Nano 9B v2 pricing data?

    This Nemotron Nano 9B v2 page tracks the latest published OpenRouter API pricing from OpenRouter, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://openrouter.ai/api/v1/models. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.

    About Nemotron Nano 9B v2 API pricing

    Nemotron Nano 9B v2 is a open-source model from NVIDIA. Current API pricing is $0.00 per million input tokens and $0.00 per million output tokens, with a 128K token context window. A blended 1M-in / 1M-out workload costs $0.00.

    Rank it on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.

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