nemo-megatron-t5-3B
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探索 AI 模型 1050
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查看详情 ↗The NeMo NanoCodec is a neural audio codec that leverages finite scalar quantization and adversarial training with large speech language models to achieve state-of-the-art audio compression across different bitrate and frame rate ranges. Model variant details: Model | Sample Rate | Frame Rate | Bit Rate | # Codebooks | Codebook Size | Embed Dim | FSQ Levels | :-----------:|:-----------:|:----------:|:----------:|:-----------:|:-------------:|:-----------:|:------------:| 1.78kbps-12.5fps| 22050 | 12.5 | 1.78kpbs | 13 | 2016 | 52 | [8, 7, 6, 6] | 0.6kbps-12.5fps | 22050 | 12.5 | 0.6kpbs | 4 | 4032 | 16 | [9, 8, 8, 7] | 1.89kbps-21.5fps | 22050 | 21.5 | 1.89kpbs | 8 | 2016 | 32 | [8, 7, 6, 6] | ⚠️ **Note on 0.6kbps-12.5fps** This variant is designed for **fine-tuning with a limited set of speakers**, as shown in our S2S Duplex paper. It is **not recommended** for general-purpose audio encoding or decoding.
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查看详情 ↗h1, h2, h3, h4, h5, h6 { color: #76b900; /* NVIDIA green */ font-weight: 700; } /* Improve list spacing */ ul, ol { margin-top: 0.5rem; margin-bottom: 0.5rem; } /* Badge alignment consistency */ img { display: inline; vertical-align: middle; }
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查看详情 ↗NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...
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$0.2 / M 输入 TokenOpenRouter · nvidia/nemotron-3.5-content-safety
查看详情 ↗NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...
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$0 / M 输入 TokenOpenRouter · nvidia/nemotron-3.5-content-safety:free
查看详情 ↗**Nemotron-3.5-Embed-1B-BF16** is a versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image, text, or combined image–text input. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. For text retrieval, the model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, and Vietnamese. For image retrieval, it was evaluated across 18 languages: Arabic, Bengali, Chinese, English, Persian, Finnish, French, German, Hindi, Indonesian, Japanese, Korean, Russian, Spanish, Swahili, Telugu, Thai, and Yoruba. The model may support additional languages beyond these evaluation sets. Among models of comparable size, Nemotron-3.5-Embed-1B-BF16 achieves state-of-the-art results across multiple multilingual and multimodal retrieval benchmarks. Read more details in our Blog Post. The **Nemotron-3.5-Embed-1B-BF16** is suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technology. The input of the model is a text or a document image, and the output is a fixed-size embedding vector. The embedding model is a bi-encoder that supports context in textual format (e.g. the query or the OCR text of a page or a section of a document) or the image of a document page. Typically, the embedding model is used first to embed (vectorize) the whole corpus (document images or text chunks), and embeddings are stored in a vector database associated to its raw content (image or text). Then at inference time, the embedding model is used to embed the query. The model generates dense vector embeddings from text or image inputs, enabling multimodal retrieval, semantic search, and (agentic) RAG workflows.
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查看详情 ↗**Nemotron-3.5-Embed-8B-BF16** is a state-of-the-art, versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image, text, or combined image–text input. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. For text retrieval, the model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, and Vietnamese. For image retrieval, it was evaluated across 18 languages: Arabic, Bengali, Chinese, English, Persian, Finnish, French, German, Hindi, Indonesian, Japanese, Korean, Russian, Spanish, Swahili, Telugu, Thai, and Yoruba. The model may support additional languages beyond these evaluation sets. Nemotron-3.5-Embed-8B-BF16 achieves state-of-the-art results across multiple multilingual and multimodal retrieval benchmarks. Read more details in our Blog Post. The **Nemotron-3.5-Embed-8B-BF16** is suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technology. The input of the model is a text or document image and the output is a fixed-size embedding vector. The embedding model is a bi-encoder that supports context in textual format (e.g. the query or the OCR text of a page or a section of a document) or the image of a document page. Typically, the embedding model is used first to embed (vectorize) the whole corpus (document images or text chunks), and embeddings are stored in a vector database associated to its raw content (image or text). Then at inference time, the embedding model is used to embed the query. The model generates dense vector embeddings from text or image inputs, enabling multimodal retrieval, semantic search, and (agentic) RAG workflows.
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查看详情 ↗Nemotron 3.5 Lightning
NVIDIANVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...
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$0.07 / M 输入 TokenOpenRouter · nvidia/nemotron-3.5-lightning
查看详情 ↗NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...
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$0 / M 输入 TokenOpenRouter · nvidia/nemotron-3.5-lightning:free
查看详情 ↗Nemotron-3.5-Parse
NVIDIANemotron 3.5 Parse is a document image parser that produces structured, machine-readable text with semantic layout classes, bounding boxes, and reading order. It combines a C-RADIOv4-SO400M vision encoder with a 10-layer mBART decoder that consumes the visual sequence as a causal prefix. The release uses dynamic rectangular NaViT preprocessing, content-normalized coordinates, grouped-query self-attention (16 query heads and 8 key/value heads), a 72,505-token multilingual tokenizer, and chart-aware output through the ` ` token. It is intended for document understanding, information retrieval, data extraction, and multimodal data-curation workflows. ParseBench results use triple pass with region union on 2,078 unique pages. See pipeline details, reproduction instructions, and evaluation results. MOSCAR uses simple synthetic renderings of text in each language to test basic OCR capability. These results do not guarantee accuracy on complex scanned documents, handwriting, degraded images, or other real-world variations. Character bag F1 measures character overlap without considering character order.
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查看详情 ↗NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...
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$0 / M 输入 TokenOpenRouter · nvidia/nemotron-3.5-super-vl:free
查看详情 ↗nemotron-3-8b-base-4k
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。