NVIDIA: Llama 3.1 Nemotron Ultra 253B v1
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Model Card
Llama 3.1 Nemotron Ultra 253B is a 253B parameter reasoning model derived from Llama 3.1 405B using Neural Architecture Search for improved efficiency, supporting 128K context and toggle ON/OFF reasoning modes. It excels at complex math, scientific reasoning, coding, RAG, and tool calling tasks while fitting on a single 8xH100 node.
Context Window 131K
tokens
Max Output N/A
tokens
Input Cost $0.6
per million tokens
Output Cost $1.8
per million tokens
Release Date Apr 7, 2025
Code Example
Add AI to your app with the Puter.js AI API, no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From NVIDIA
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Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts model from NVIDIA with 120B total parameters and 12B active per token. It takes text, images, documents, and video, and adds reasoning and tool use for long-running agents. NVIDIA reports results on SWE-bench Verified, SWE-bench Pro, CharXiv reasoning, and OSWorld-Verified, and says it matches Nemotron 3 Ultra on agent tasks with about one-fifth the parameters. It trails newer open models such as Qwen3.8-Flash-Next and DeepSeek-V4-Flash on most coding and agent benchmarks. It has a 1M-token context window. It suits agent workflows that mix tool calls with document, chart, screenshot, or video inputs.
ChatNemotron 3.5 Lightning
Nemotron 3.5 Lightning is a 30B-parameter Mixture-of-Experts model from NVIDIA with 3B active parameters per token, using a hybrid Mamba-2, MoE, and attention layer architecture. It is designed for high-volume, low-latency execution inside multi-agent systems, where a larger reasoning model like Nemotron 3 Ultra plans and delegates, and Lightning handles tool calls, code review, and other repetitive subagent tasks. NVIDIA reports up to 4x higher output throughput than similarly sized open models and, on its PinchBench agent benchmark, 86% accuracy while completing 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy. The model ships with built-in multi-token prediction and draft models for speculative decoding, and is available in NVFP4 and BF16 checkpoints under the permissive OpenMDW-1.1 license with open weights and training recipes. Choose it for agent harnesses handling frequent, narrow calls, tool validation, or subagent delegation where response speed matters more than broad reasoning depth.
ChatNemotron 3 Ultra 550B A55B
Nemotron 3 Ultra 550B A55B is NVIDIA's open-weight frontier reasoning model with 550B total and 55B active parameters, built on a hybrid Mamba-Transformer Mixture-of-Experts architecture. It supports a 1M token context window and is designed for long-running agentic workflows, complex multi-step reasoning, and high-accuracy tasks across code, math, and science. NVIDIA reports up to 5.9x higher inference throughput than comparable open MoE models. On the Artificial Analysis Intelligence Index it scores 48, leading US open-weight models and delivering the highest non-hallucination score in its comparison set (78.7 on AA-Omniscience). Choose it for production agentic pipelines, deep document analysis, or reasoning-heavy API workloads where both accuracy and throughput matter.
Frequently Asked Questions
Llama 3.1 Nemotron Ultra 253B v1 is no longer available through Puter.js. Explore other AI models for alternatives.
| Price per 1M tokens | |
|---|---|
| Input | $0.6 |
| Output | $1.8 |
Llama 3.1 Nemotron Ultra 253B v1 was created by NVIDIA and released on Apr 7, 2025.
Llama 3.1 Nemotron Ultra 253B v1 supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Yes — the Llama 3.1 Nemotron Ultra 253B v1 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
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