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New models and versions, open weights, and changes in capability and pricing, with their evaluations.
Google DeepMind released EmbeddingGemma 2, a 740M-parameter open model based on the Gemma 4 architecture and licensed under Apache 2.0. It maps text, images, audio, and video into a shared embedding space.
EmbeddingGemma 2 maps text, images, audio, and video into one embedding space and reports concrete on-device memory and vector truncation figures, helping assess local multimodal retrieval.
Mistral released a public preview of Mistral Large 4, a natively multimodal model with 1 trillion parameters and 49 billion active parameters. Its weights will be opened at the end of the month.
Mistral outlined the open-weight release schedule for its 1-trillion-parameter multimodal model and its investment in European compute, allowing comparison with mainstream open models.
Zhipu AI (Z.ai)'s GLM 5.3 is now available on Amazon Bedrock. The 753B-parameter mixture-of-experts model targets coding and long-horizon agent tasks and is offered through a fully managed API with cross-region inference, prompt caching, and service tiers. It is currently available to eligible enterprise customers.
The source describes how to access GLM 5.3 on Bedrock and use prompt caching, helping readers assess the cost impact of managed inference for coding and long-horizon agent workflows.
Ai2 open-sourced AstaBrief 8B, a model that turns research questions and retrieved literature excerpts into reports with citations. It was trained from Qwen3-8B using SFT and DPO and is now available as Asta's Fast mode.
The source describes AstaBrief's training-data construction and citation-filtering details, which can be adapted to deploy other long-form generation models.
Black Forest Labs released Flux 3 Image, an image model in the Flux 3 family. The company says it supports multi-step editing without changing other parts of an image and covers text-to-image, image-to-image, text rendering, and photorealistic generation.
Flux 3 Image combines multi-step editing with 4K output and a reference-image limit, allowing comparison with Ideogram 4.5 on the pace of competition in image-editing models.
Google released its next-generation flagship model, Gemini 4 Argon. It scored 77.9% on DeepSWE v1.1, ahead of Claude Opus 5.5 at 74.2% and GPT-6 Astra at 74.1%, and ranked first on Text Arena with 1,525 points.
Google's new flagship tops several leaderboards and publishes cost and maximum output-token figures, helping readers assess its position against GPT and Opus.
AI2 released Olmo-core 3, a major upgrade to its large language model development framework. It redesigns the open MoE training system to scale MoE training to trillions of parameters while maintaining compute efficiency.
The source provides throughput and memory comparisons for the MoE training stack, helping readers assess changes in the cost of large-scale MoE training.
Google DeepMind released frontier model Gemini 4 Argon, initially making it available through the Fairwind Program to trusted cybersecurity defenders before gradually expanding access. Pricing starts at $2 per million input tokens and $10 per million output tokens.
Gemini 4 Argon raises the maximum output to 1M tokens and reports multiple benchmark results and internal deployment data, offering a view of its limits on long-running workflows.
Google DeepMind launched SynthID Bio, bringing watermarking to synthetic biology. It embeds imperceptible signatures directly into biological code so that generated content can be verified both in digital models and in synthesized physical proteins, while preserving their biological function.
SynthID watermarking has expanded from images to synthetic biology, showing how AI-generated proteins can be marked and verified.
OpenAI released its next-generation flagship model GPT-6 Astra at 1 a.m. Beijing time on September 4. Leiphone compiled first-hand tests from users who received access early.
Early user tests show GPT-6 Astra can check its own work on long tasks but still needs human oversight, revealing its capability limits.
OpenAI has released GPT-6.1 Sol, saying it approaches the flagship GPT-6.1 Astra—which was delayed over safety concerns—in agentic coding, computer use, and office tasks, at about one-fifth the cost.
With flagship Astra delayed over safety concerns, readers can compare Sol and Astra with Opus 5.5 on price and benchmark gaps.
NVIDIA has released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass from labeled table rows, supporting classification and regression without training, tuning, or feature engineering. The model comes in three sizes from 28M to 215M parameters, was pretrained entirely on synthetic tables, is commercially usable under the OpenMDW-1.1 license, and ranks first on four benchmarks: TabArena, BeyondArena, TALENT, and ScoringBench.
The source reports model sizes, training-data provenance, and rankings on four benchmarks, helping readers assess the accuracy and efficiency tradeoffs of tabular foundation models.
Microsoft Research has released Quine, an AI research system designed for biological complexity. It combines a biological world model with an interactive harness connecting models, scientific tools, literature, and experimentalists. In work with the Broad Institute on pancreatic ductal adenocarcinoma, it predicted and prioritized thousands of compounds; the top-ranked compound produced the largest classical-to-ground-state transition in wet-lab experiments. The process from narrowing the search space to selecting a candidate for validation took one weekend.
Microsoft Research describes Quine's multimodal biological world model and experimental loop, including wet-lab validation in pancreatic cancer compound screening.
OpenAI has released GPT-6.1 Sol, positioning it as near-Astra intelligence for coding, computer use, and professional work. Standard API input and output tokens cost one-fifth as much as Astra.
OpenAI describes GPT-6.1 Sol as close to Astra in capability at one-fifth the API price, giving readers a basis for weighing cost against performance.
Latent Space's AINews roundup of developments from September 24–25 says community feedback was positive after Opus 5.5 launched that week, especially for generating explainer videos with code.
The weekly roundup offers a quick look at model and agent-infrastructure developments, including community feedback on Opus 5.5's code-generated videos and how vendors position their new models.
AWS announced that Claude Sonnet 5.5 is available on Amazon Bedrock and Claude Platform on AWS. It is positioned as a more efficient Sonnet model for coding and knowledge work, with lower per-task costs and faster speed for most tasks.
The source describes Sonnet 5.5 improvements for coding and document tasks, its division of work with Opus 5.5, and access through Bedrock.
H Company has released the Holo4 agent-model family, with a 27B dense model and a 35B-A3B MoE model. Both are available through the H Models API, and weights are open on Hugging Face in BF16, FP8, NVFP4, and 4-bit GGUF formats.
Holo4 opens both its weights and complete evaluation traces, giving readers a basis to assess the cost and capability limits of general-purpose computer-use agents.
Google DeepMind has released Gemini 3.8 Live with Live Avatar, combining real-time video generation and voice to give Gemini's conversational model a near-real-time visual avatar, with accurate lip-sync, natural expressions, and smooth turn-taking.
Gemini 3.8 Live adds real-time video avatars, giving readers a view of how multimodal conversation is being applied in enterprise settings and where the feature is available.
Logan Kilpatrick@OfficialLoganKPicks71Google's Gemini 3.8 Flash and Flash-Lite TTS release lists more than 2,000 voices, support for 100 languages, voice cloning, and other capabilities.
Google DeepMind has released two speech-generation models: Gemini 3.8 Flash TTS for character design and fine-grained creative control, and Gemini 3.8 Flash-Lite TTS for high-volume voiceovers and voice agents.
The article outlines the models' voice cloning, line-by-line direction, and benchmark results, helping readers assess their suitability for dubbing and podcast production.
