6Hugging Face Blog·
NVIDIA Nemotron 3 Embed ranks #1 on RTEB benchmark for agentic retrieval
NVIDIA's Nemotron 3 Embed model has achieved the top overall ranking on the RTEB (Retrieval Text Embedding Benchmark), a benchmark focused on agentic retrieval tasks. The announcement was published on the Hugging Face blog, positioning the model as a leading embedding solution for retrieval-augmented and agentic workflows. Strong embedding performance is increasingly relevant as retrieval quality becomes a bottleneck in production agent systems.
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Introducing RTEB: A New Standard for Retrieval Evaluation
Hugging Face introduces RTEB (Retrieval Text Embedding Benchmark), a new benchmark designed to standardize evaluation of retrieval systems and text embeddings. The benchmark aims to address gaps in existing evaluation frameworks by providing more comprehensive and realistic retrieval tasks. This represents an effort to improve how the community measures progress in retrieval-augmented generation and semantic search systems.
Nvidia Nemotron 3 Ultra: hybrid Mamba-transformer open-weights model targeting agentic workloads
Nvidia released Nemotron 3 Ultra, a 550B parameter (55B active) hybrid Mamba-transformer mixture-of-experts model with a 1M token context window, publishing weights, training data, and RL environments under an open license. The model ranks as the highest-scoring U.S. open-weights model on the Artificial Analysis Intelligence Index (47.7-48.2) and is approximately three times faster than comparable open-weights rivals, though it trails leading Chinese models like Kimi K2.6 and DeepSeek V4 Pro on intelligence benchmarks. Nvidia used a novel Multi-Teacher On-Policy Distillation approach with 10+ specialized teacher models and trained using NVFP4 quantization. The release is strategically motivated by Nvidia's interest in a healthy open-weights ecosystem that drives AI semiconductor adoption.
Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents
NVIDIA has released Nemotron 3 Nano Omni, a multimodal model targeting long-context understanding across documents, audio, and video modalities. The model is positioned for agentic use cases requiring cross-modal reasoning. It is published via the Hugging Face blog as part of NVIDIA's Nemotron model family. No detailed technical specifications or benchmark results are provided in the available body text.
Measuring Open-Source Llama Nemotron Models on DeepResearch Bench
NVIDIA evaluates its open-source Llama Nemotron models on the DeepResearch Bench, a benchmark designed to assess deep research agent capabilities. The post appears to report competitive performance of the Nemotron models in agentic research tasks. This is relevant to the ongoing development of open-weights models capable of multi-step research and reasoning workflows.
Qwen3 Embedding: State-of-the-Art Text Embedding and Reranking Models Released
Alibaba's Qwen team has released the Qwen3 Embedding series, a set of open-weights text embedding and reranking models built on the Qwen3 foundation model. The models are designed for retrieval and reranking tasks and claim state-of-the-art performance across multiple benchmarks. They are released under the Apache 2.0 license and are available on Hugging Face and ModelScope.
The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator
NVIDIA and Hugging Face present an evaluation methodology for the Nemotron 3 Nano model using the NeMo Evaluator framework. The post describes benchmark results and an open evaluation recipe intended to standardize how small/nano-scale models are assessed. It positions NeMo Evaluator as a reproducible, open evaluation stack for the community.
Nvidia releases Nemotron 3 Super 120B-A12B open-weights model with hybrid Mamba-2/MoE architecture
Nvidia released Nemotron 3 Super 120B-A12B, an open-weights LLM with a hybrid Mamba-2/transformer/MoE architecture that activates only 12B parameters per token and supports up to 1 million token context. The model claims the fastest inference speed in its size class at 442 tokens/second and leads open-weights models on PinchBench agentic task evaluation, outperforming larger models including Kimi K2.5 (1T parameters). Nvidia is releasing weights, training data, and recipes under a permissive commercial license, and plans a $26B five-year investment in open-weights models — framed partly as a strategic response to Chinese labs building capable open-weights models on non-Nvidia hardware.
Mistral AI Releases Codestral Embed: First Code-Specialized Embedding Model
Mistral AI has launched Codestral Embed (codestral-embed-2505), its first embedding model specialized for code retrieval and semantic understanding. The model claims to outperform leading competitors including Voyage Code 3, Cohere Embed v4.0, and OpenAI's large embedding model across benchmarks including SWE-Bench, CodeSearchNet, and Text2SQL tasks. It supports variable output dimensions and precisions (including int8), enabling storage/quality trade-offs, and is priced at $0.15 per million tokens via Mistral's API with batch discounts available.