open-source

OpenAI is releasing a set of open-source prompts developers can use to make their applications safer for teens. The policies, designed to work with OpenAI's gpt-oss-safeguard model, address graphic violence, sexual content, harmful body ideals, dangerous activities, and age-restricted goods.

Stability AI has open-sourced Stable Audio Open Small in partnership with Arm, a smaller and faster variant of its text-to-audio model designed for on-device deployment. The model maintains output quality and prompt adherence while reducing computational requirements for real-world edge deployment on devices powered by Arm's technology, which runs on 99% of smartphones globally.

Rakuten Group has released RakutenAI-3.0, a 671 billion parameter mixture-of-experts (MoE) model designed specifically for Japanese language tasks. The model activates 37 billion parameters per token and supports a 128K context window. It is available under the Apache License 2.0 on Hugging Face.

Nvidia has released Nemotron 3 Super, a 120-billion parameter hybrid Mamba-Transformer Mixture-of-Experts model that activates only 12 billion parameters during inference. The open-weight model features a 1-million token context window, multi-token prediction capabilities, and pricing at $0.10 per million input tokens and $0.50 per million output tokens.

NVIDIA released Nemotron 3 Content Safety 4B, an open-source multimodal safety model designed to moderate content across text, images, and multiple languages. Built on Gemma-3 4B-IT with a 128K context window, the model achieved 84% average accuracy on multimodal safety benchmarks and supports over 140 languages through culturally-aware training data.

Cursor acknowledged that its newly launched Composer 2 coding model was built on Moonshot AI's open-source Kimi 2.5 model, not developed independently. The admission came after user scrutiny revealed code references to Kimi. Cursor claims only 25% of compute came from the base model, with 75% from its own training and reinforcement learning.

AI2 has released MolmoSpaces and MolmoBot, robotics models trained exclusively in simulation that transfer directly to real robots without manual real-world data collection or fine-tuning. The approach eliminates months of teleoperated demonstrations typically required for simulation-trained robots. Both systems are open-source.

Hume AI has open-sourced TADA, a speech generation model that maps exactly one audio signal to each text token, achieving 5x faster processing than comparable systems. The model produced zero transcription hallucinations across 1,000+ test samples and runs on smartphones, available in 1B and 3B parameter versions under MIT license.

Hume AI has released TADA-1B, a 1 billion parameter text-to-speech model available on Hugging Face under an MIT license. The model, which combines speech and language capabilities, has already accumulated over 3,100 downloads since its January 12 release.

Nvidia is preparing to launch an open-source AI agent platform, according to reports ahead of the company's annual developer conference. The move mirrors approaches by competitors like OpenAI in building agent-based AI systems.

StepFun has released Step-3.5-Flash-Base, a text generation model available on Hugging Face under Apache 2.0 license. The model is part of the Step 3.5 series and focuses on efficient inference.

Alibaba has released Qwen3.5-2B, a 2-billion-parameter multimodal model capable of processing both images and text. The model is available on Hugging Face under the Apache 2.0 license and supports image-text-to-text tasks.

Alibaba's Qwen team has released Qwen3.5-4B, a 4 billion parameter multimodal model capable of processing both images and text. The model is available on Hugging Face under an Apache 2.0 license, making it freely available for commercial and research use.

Alibaba has released Qwen3.5-9B, a 9-billion parameter multimodal language model capable of processing both images and text. The model is available under Apache 2.0 license on Hugging Face with transformer-compatible architecture.

Perplexity has open-sourced two text embedding models designed to match or exceed the performance of Google's and Alibaba's embeddings while requiring significantly less memory. The move brings competitive embedding technology into the open-source ecosystem.

DeepSeek has released DeepSeek-R1, a text generation model featuring reasoning capabilities through chain-of-thought processing. The model was published January 20, 2025 and has accumulated over 830,000 downloads on Hugging Face.

Alibaba's Qwen team has released Qwen3.5-35B-A3B-Base, a 35-billion parameter multimodal model supporting image-text-to-text tasks. The model is available under the Apache 2.0 license and compatible with major inference endpoints including Azure deployment.

Alibaba Qwen has released Qwen3.5-27B, a 27-billion parameter model capable of processing both images and text. The model is available under an Apache 2.0 open license and is compatible with standard transformer endpoints.

LocoreMind has released LocoOperator-4B, a 4 billion parameter text generation model fine-tuned from Qwen/Qwen3-4B-Instruct-2507. The model is optimized for agent workflows and tool-calling capabilities and is available under an MIT license.

Guide Labs has open-sourced Steerling-8B, an 8 billion parameter language model built with a new architecture specifically designed to make the model's reasoning and actions easily interpretable. The release addresses a persistent challenge in AI development: understanding how large language models arrive at their outputs.

Cohere Labs has released tiny-aya-global, a lightweight text generation model trained to support conversational tasks across 100+ languages. The model is available on Hugging Face under a CC-BY-NC-4.0 license and builds on the tiny-aya-base architecture.

Zyphra has released ZUNA, an open-source model available on Hugging Face under the Apache 2.0 license. The model has been downloaded 529 times since its February 4, 2026 release.

Segmind has released SegMoE, a mixture-of-experts (MoE) diffusion model designed to accelerate image generation while reducing computational overhead. The model applies MoE techniques traditionally used in large language models to the diffusion model architecture, enabling selective expert activation during inference.