Releases

Meta released Muse Spark 1.1 and made it available through Meta Model API.

Meta announced Muse Video, a generative video model with native audio support, as coming soon.

Meta released Muse Image for instruction-following image generation, precise editing and multi-reference composition.

Meta released training code for Brain2Qwerty v1 and v2; v2 performs real-time non-invasive sentence decoding from brain recordings.

Meta Superintelligence Labs introduced Muse Spark, a multimodal model designed for efficient broad intelligence.

Meta released an efficiency update adding object multiplexing and up to 32-frame-per-second multi-object video tracking on one H100.

Meta released a tri-modal foundation model that predicts high-resolution human brain activity from video, audio and language.

Meta and World Resources Institute released a DINOv3-based global forest-canopy model with improved accuracy and detail.

Meta released a unified multimodal model for separating sounds from complex mixtures using natural prompts.

Meta released a model for estimating human body pose and shape from a single image.

Meta released a model for reconstructing 3D objects and scenes from a single image.

Meta released SAM 3 for text-, exemplar- and visual-prompted detection, segmentation and tracking in images and video.

Meta released speech-recognition models covering more than 1,600 languages plus an open corpus for 350 underserved languages.

Meta released DINOv3, a scaled self-supervised vision foundation model for high-quality visual features.

Meta released V-JEPA 2 for video understanding, physical-world prediction and zero-shot robot planning.

Meta released vision encoders for images and video designed to support recognition, localization and multimodal systems.

Meta released a multimodal safety classifier for Llama 4 inputs and outputs.

Meta previewed Behemoth as a teacher model for the Llama 4 family; it was not made generally available.

Meta released Llama 4 Maverick, a natively multimodal mixture-of-experts model for general AI workloads.

Meta released Llama 4 Scout, a natively multimodal mixture-of-experts model with a 10-million-token context window.

Meta released a behavioral foundation model for controlling virtual humanoid agents.

Meta released a multilingual 70B instruction model with performance comparable to larger Llama 3.1 models.

Meta published improved SAM 2.1 checkpoints and developer tooling.

Meta released a 90B multimodal model for image understanding.

Meta released an 11B multimodal model for image understanding.

Meta released a compact 1B text model for on-device use.

Meta released a compact 3B text model for on-device use.

Meta released SAM 2 for promptable object segmentation in both images and video.

Meta released a classifier for detecting prompt injection and jailbreak attempts.

Meta released a multilingual Llama 3.1-based safeguard model.

Meta released its 405B-parameter open-weights frontier model.

Meta released Llama 3.1 70B with a 128K context window and multilingual support.

Meta released Llama 3.1 8B with a 128K context window and multilingual support.

EvolutionaryScale, founded by former Meta researchers, released ESM-3, a generative protein model originating from Meta's ESM program.

Meta and World Resources Institute released global high-resolution canopy-height maps built with DINOv2.

Meta released the Llama 3-based update to its open safeguard model.

Meta released the 8B Llama 3 pretrained and instruction-tuned models.

Meta released the 70B Llama 3 pretrained and instruction-tuned models.

Meta released V-JEPA, a video model trained by predicting missing representations rather than pixels.

Meta released a 70-billion-parameter Code Llama model for code generation and instruction following.

Meta released Llama Guard, a safeguard model for classifying conversational AI inputs and outputs.

Meta released a low-latency streaming speech translation research model.

Meta released a research model that preserves vocal style and prosody in translated speech.

Meta released a more capable second version of SeamlessM4T.

Meta introduced Emu as the image-generation technology powering new generative AI experiences.

Meta released code-specialized Llama models for generation, completion and instruction following.

Meta released SeamlessM4T for multilingual speech-to-speech, speech-to-text, text-to-speech and text translation.

Meta released Llama 2 models in 7B, 13B and 70B sizes for research and commercial use.

Meta released MMS speech-recognition and synthesis models covering more than 1,100 languages.

Meta released ImageBind, a joint embedding model spanning images, text, audio, depth, thermal and IMU data.