NVIDIA’s model card and API documentation describe Llama 3.3 Nemotron Super 49B V1.5 as a commercially usable reasoning and chat model derived from Meta’s Llama 3.3 70B Instruct, while Price Per Token lists the model at $0.400 per million input tokens and $0.400 per million output tokens.
NVIDIA has documented Llama 3.3 Nemotron Super 49B V1.5 as a preview API model for reasoning and chat workloads, while Price Per Token has published a separate pricing catalog entry for the model.
NVIDIA’s model card for Llama-3.3-Nemotron-Super-49B-v1.5 describes it as a reasoning and chat model derived from Meta Llama-3.3-70B-Instruct. The same NVIDIA model card lists a 128K context length and gives a release date of July 25, 2025.
NVIDIA’s API documentation also identifies the model as commercially usable and points developers to a preview API link. The API reference covers the model overview, licensing, release date, and context length, according to NVIDIA’s own documentation.
The model name indicates a 49B-parameter class system, but the provided NVIDIA excerpts do not describe training data, evaluation methodology, deployment limits, or the exact architectural changes from the Meta base model. Those details would need to be checked in the full NVIDIA documentation before making stronger technical claims.
Price Per Token’s catalog entry, updated June 21, 2026, lists NVIDIA Llama 3.3 Nemotron Super 49B V1.5 at $0.400 per million input tokens and $0.400 per million output tokens.
The same Price Per Token entry reports a 131K-token context window and includes standard-versus-thinking benchmark scores. NVIDIA’s own model card excerpt, however, lists 128K context. That difference may reflect rounding, catalog formatting, or a source mismatch, so developers should verify the context length against NVIDIA’s current API documentation before relying on the larger figure.
For teams evaluating hosted large language models, the combination of model documentation and third-party pricing data provides a starting point for cost and capability comparisons. A flat listed price of $0.400 per million input tokens and $0.400 per million output tokens, as reported by Price Per Token, would make the model’s published catalog pricing simple to calculate for workloads with balanced input and output usage.
NVIDIA’s documentation positions the model for reasoning and chat use cases, which suggests it is aimed at applications such as assistants, tool-using systems, analysis workflows, and long-context question answering. The 128K context length listed by NVIDIA would also place the model in the long-context category, although actual usable context can depend on provider limits, latency, memory requirements, and API configuration.
Because NVIDIA’s API reference describes the model as ready for commercial use, organizations considering production deployment should still review the linked license terms, availability status, preview API conditions, and service-level details. Preview availability can carry different expectations than a fully general-availability service, and the provided excerpts do not specify uptime commitments, regional availability, rate limits, or data-handling terms.
The primary technical details come from NVIDIA’s model card and NVIDIA API documentation for llama-3.3-nemotron-super-49b-v1.5. Pricing and benchmark-catalog details come from Price Per Token’s model page. Where the sources differ, such as 128K versus 131K context length, NVIDIA’s model card should be treated as the authoritative source for NVIDIA’s own model specification unless NVIDIA’s live API documentation states otherwise.
NVIDIA has documented Llama 3.3 Nemotron Super 49B V1.5 as a preview API model for reasoning and chat workloads, while Price Per Token has published a separate pricing catalog entry for the model.
What NVIDIA says about the model NVIDIA’s model card for Llama 3.3 Nemotron Super 49B v1.5 describes it as a reasoning and chat model derived from Meta Llama 3.3 70B Instruct .
The same NVIDIA model card lists a 128K context length and gives a release date of July 25, 2025 .
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