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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-05-11 · Source: nvidia_reference_catalog
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
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-3.1-40...
llama-3.1-nemotron-ultra-253b-v1 is a Text model from NVIDIA (US). HotON.ai tracks it at $0.60 per 1M input tokens and $1.80 per 1M output tokens, with a 131K-token context window. Its composite efficiency score is 89/100 at an estimated $0.002 per successful task.
llama-3.1-nemotron-ultra-253b-v1 is tracked at $0.60 per 1M input tokens and $1.80 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $1.50 per 1M tokens. Figures are illustrative demo data.
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
llama-3.1-nemotron-ultra-253b-v1 supports up to a 131K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, llama-3.1-nemotron-ultra-253b-v1 is cheaper than 40% of models on input price and ranks #257 of 537 by overall efficiency.
Yes — minimax-m2 is a lower-cost option at $1.00 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — llama-3.1-nemotron-ultra-253b-v1 (NVIDIA): $0.60/1M input, $1.80/1M output, as of 2026-05-11. https://hoton.ai/en/models/nvidia-llama-3-1-nemotron-ultra-253b-v1Pricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.