All AI news

AIGoogle DeepMind released EmbeddingGemma 2, an open 740M-parameter embedding model that maps text, code, images, video, and audio into one 768-dimensional space. Text-only use needs a 270M-parameter footprint, about 191MB active RAM when quantized on a Pixel 11 Pro, while loading all modalities takes about 567MB. The reported MTEB Code NDCG@10 score is 78.68, about 14% above the first generation, and MTEB Multilingual v2 is 61.36, roughly flat.

AIMistral Large 4 solved 18 of 19 challenges in a CTF speedrun, with tool calls and solve times drawn from actual runs. The post frames the model as efficient at reasoning over diverse complex challenges compared with other models.

AIMistral AI says its Mistral Large 4 is one of the world's strongest AI models for cybersecurity. The post gives no benchmark scores, comparisons, or availability details.

AIGoogle announces Nano Banana 2.1, an upgraded image model that outperforms its previous versions across the board. The company says it brings notable improvements in visual design, mask-based editing, subject consistency, and more natural-looking imagery. It is available now in the Gemini app and Google AI Studio.

AIPerplexity says its open-weights multimodal decision model pplx-decider-v1.1-27b is now available and scores highest on Hugging Face's new Decision Index 0.3 benchmark. The model costs $0.02 per million input tokens, half the price of v1.

AIOn ARC-AGI-2, Grok 4.7's best score is 61.4% at $2.01 per task, down from Grok 4.6's 67.1% at $0.76 per task. The post notes cost per task rose 166% despite identical input and output token pricing.

AISpaceXAI's Grok 4.7 scored 90.2% on ARC-AGI-1 at $0.64 per task, higher than Grok 4.6, according to ARC Prize. It reached 61.4% on ARC-AGI-2 at $2.01 per task and 1.8% on ARC-AGI-3 under the standard harness ($2.7k), or 10.0% with the provider adapter harness ($4.8k), both lower than Grok 4.6 on those two benchmarks.

AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.

AIGoogle released EmbeddingGemma 2, a 740M-parameter open embedding model under Apache 2.0 that combines a 270M text model with vision (170M) and audio (300M) encoders. The 270M text model can run locally with 0.5GB of RAM, and the full multimodal model with 1GB, and Unsloth provides GGUF files and fine-tuning support.

AIGoogle's EmbeddingGemma 2 is a 740M-parameter embedding model that outperforms some models more than twice its size while using about 191MB to 567MB of active RAM. It offers an 8K context window, 4x larger than the first generation, and can process up to 5.5 minutes of audio, 29 images, or 58 video frames in one pass.

AIGoogle has released Nano Banana 2.1, its latest image generation and editing model, which it says outperforms previous versions with gains in visual design, mask-based editing, and subject consistency. Paige Bailey shared a pug smile example made with the model and invited users to try it in Google AI Studio.

AIGoogle Gemma announces EmbeddingGemma 2, a lightweight embedding model that maps text, code, images, video, and audio into a single unified embedding space. The model has a 740M parameter form factor with modular encoders, Matryoshka Representation Learning dimensions from 768 down to 128, and an 8K context window that is 4x larger than the text-only EmbeddingGemma. It is released under the commercially permissive Apache 2.0 license.

AIGoogle Gemma says EmbeddingGemma 2 delivers best-in-size-class performance on MTEB (Code) and MIEB (Lite). The post highlights local codebase indexing, semantic code search, and coding agent retrieval, with a 9.92-point code performance gain over EmbeddingGemma.

AIGoogle has released EmbeddingGemma 2, a compact 740M-parameter model built for on-device and edge applications. It natively embeds text, images, audio, and video into one shared space, enabling cross-format search without manual organization, translation, or labeling.

AIGoogle DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model, under an Apache 2.0 license. The post says it is competitive across benchmarks and outperforms some specialist models more than twice its size, and that developers can use it for multimodal search or pair it with Gemma 4 for on-device RAG. Weights are available on Hugging Face and Kaggle.

AIGoogle DeepMind introduced EmbeddingGemma 2, its first natively multimodal open model for on-device embeddings. The model expands beyond text to unify code, images, audio, and video in a shared embedding space.

AIGoogle introduces EmbeddingGemma 2, its first open, natively multimodal embedding model, covering text, code, image, video, and audio tasks. It has a 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and the post claims it outperforms some specialist models more than twice its size. Weights are available now on Hugging Face.

AIGoogle AI Studio introduced Nano Banana 2.1, an image generation model that Google says outperforms its previous models across the board. The update highlights better visual design, mask-based editing, subject consistency, and more natural-looking images. It is available to try today in AI Studio.

AIMistral announced Mistral Large 4, which it describes as a natively multimodal model with 1T parameters and 49B active. Mistral says it is available via API now, with open weights to follow at the end of October, and a Hugging Face page is listed for the release.

Why it matters: The quoted Mistral announcement gives specific size, activation, and API details, and the open-weights timing matters for teams weighing open model options.

AIKling AI is showcasing videos creators have made with its Kling 4.0 and Kling 4.0 Flash models. The post is promotional and includes no specifications, benchmarks, pricing, or feature details.

AISimon Willison reports that Mistral can now generate his pelican SVG test, shared via a Markdown SVG renderer. The post links to a rendered result but gives no benchmark or scoring details. Background from Mistral's own announcement describes Mistral Large 4 as a 1T-parameter, natively multimodal model with 49B active parameters, available via API today and with open weights planned for end of October.

AIMistral announced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active, now available via API. Open weights are scheduled for release at the end of October, with a countdown page on Hugging Face showing October 31, 2026.

AIGeneralist's GEN-1.5 robot demonstrated putting an O-ring onto a hydraulic rod gland, then repeatedly removing and replacing it. The post is a short demonstration video that points readers to a blog post in the comments for more about GEN-1.5.

AIClément Delangue of Hugging Face shared a link to a Hugging Face model page for mistralai/Mistral-Large-4.0-1T05-A52B. The post itself gives no further details about the model's capabilities, release terms, or benchmarks.