NVIDIA: Llama 3.3 Nemotron Super 49B v1 (free)

nvidia/llama-3.3-nemotron-super-49b-v1:free

Created Apr 8, 2025131,072 context
$0/M input tokens$0/M output tokens

Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) optimized for advanced reasoning, conversational interactions, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta's Llama-3.3-70B-Instruct, it employs a Neural Architecture Search (NAS) approach, significantly enhancing efficiency and reducing memory requirements. This allows the model to support a context length of up to 128K tokens and fit efficiently on single high-performance GPUs, such as NVIDIA H200.

Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

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OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

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Apr 8Apr 9Apr 10Apr 11Apr 12Apr 13Apr 14Apr 15Apr 16Apr 17015M30M45M60M

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Sample code and API for Llama 3.3 Nemotron Super 49B v1 (free)

OpenRouter normalizes requests and responses across providers for you.

OpenRouter provides an OpenAI-compatible completion API to 300+ models & providers that you can call directly, or using the OpenAI SDK. Additionally, some third-party SDKs are available.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

completion = client.chat.completions.create(
  extra_headers={
    "HTTP-Referer": "<YOUR_SITE_URL>", # Optional. Site URL for rankings on openrouter.ai.
    "X-Title": "<YOUR_SITE_NAME>", # Optional. Site title for rankings on openrouter.ai.
  },
  extra_body={},
  model="nvidia/llama-3.3-nemotron-super-49b-v1:free",
  messages=[
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
)
print(completion.choices[0].message.content)

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.

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Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

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Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

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Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

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Usage of this model is subject to Meta's Acceptable Use Policy.

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The base model was pre-trained on 9 trillion tokens from diverse English texts, 50+ natural languages, and 40+ coding languages. The instruct model underwent additional alignment steps:

The alignment process used approximately 20K human-annotated samples, while 98% of the data for fine-tuning was synthetically generated. Detailed information about the synthetic data generation pipeline is available in the technical report.

    Llama 3.3 Nemotron Super 49B v1 (free) - API, Providers, Stats