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NVIDIA: Llama 3.1 Nemotron Ultra 253B v1

nvidia/llama-3.1-nemotron-ultra-253b-v1

Created Apr 8, 2025131,072 context
$0.60/M input tokens$1.80/M output tokens

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) optimized for advanced reasoning, human-interactive chat, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta’s Llama-3.1-405B-Instruct, it has been significantly customized using Neural Architecture Search (NAS), resulting in enhanced efficiency, reduced memory usage, and improved inference latency. The model supports a context length of up to 128K tokens and can operate efficiently on an 8x NVIDIA H100 node.

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

Providers for Llama 3.1 Nemotron Ultra 253B v1

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

Performance for Llama 3.1 Nemotron Ultra 253B v1

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Apps using Llama 3.1 Nemotron Ultra 253B v1

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Recent activity on Llama 3.1 Nemotron Ultra 253B v1

Total usage per day on OpenRouter

Prompt
344M
Completion
2.73M
Reasoning
0

Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.

Uptime stats for Llama 3.1 Nemotron Ultra 253B v1

Uptime stats for Llama 3.1 Nemotron Ultra 253B v1 across all providers

Sample code and API for Llama 3.1 Nemotron Ultra 253B v1

OpenRouter normalizes requests and responses across providers for you.

OpenRouter provides an OpenAI-compatible completion API to 400+ 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.

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.