Ant Group inclusionAI · Hugging Face new modelsinclusionAI has released the Duo 1.2 causal-base model, a full-duplex multimodal world model for real-time audio-video interaction, featuring improved generation quality, and model weights can be downloaded from Hugging Face.
Ant Group inclusionAI · Hugging Face new modelsinclusionAI releases Duo-1.0-distill, a full-duplex multimodal world model for real-time audio-video interaction, including Realization weights with four-step distillation, which needs to be used in conjunction with the causal-base suite.
MiniMax's open-source image model H3-Image supports native 4K (4096×4096 pixels) generation and can take up to 16 reference images. The author tested that it can deduce orthogonal three-view drawings and mechanical exploded views based on product appearance images, and reduce the plasticity on details such as pores and freckles on human faces. The API price is 50% to 70% lower than that of mainstream competitors, with a 50% discount on the first week's 16MP requests.
Google SWE uses 48 AI agents to analyze NASA radar data, discovering unreported Mayan pyramids in Mexico within 72 hours; the agents process 1,200 km² of radar images, identifying suspected pyramids, plazas, and reservoir structures with right angles and sharp corners, which have been confirmed by the National Institute of Anthropology and History of Mexico as not being in the registered records.
Ant Group inclusionAI · Hugging Face new modelsinclusionAI/ZoomGain-4B-GGUF is a 175B parameter multimodal model supporting image input, using FP16, Q8_0, and Q4_K_M precision quantization. The model includes a language model and mmproj visual encoder/projector, converted to GGUF format via llama.cpp. It provides llama-mtmd-cli for command-line inference and llama-server for network chat functionality.
Ant Group inclusionAI · Hugging Face new modelsThe GGUF format weights for inclusionAI/ZoomGain-9B have been released, converted using llama.cpp, with the language model in FP16, Q8_0, Q4_K_M. The visual encoder and projector (mmproj) also adopt this quantization scheme. Both the language model and mmproj files need to be provided for image input, supporting CLI inference (llama-mtmd-cli) and Web Chat (llama-server) deployment.
Ant Group inclusionAI · Hugging Face new modelsSing VL S1 is a multimodal decision-making model based on Qwen3.5, capable of answering named entity questions with text or JSON and up to four images, scoring options for each question using a single Transformers forward pass, performing excellently on 43 text datasets and 11 visual datasets, supporting Python 3.12 and PyTorch inference.
MiniMax NewsFeaturedSame story · 2 outlets8080MiniMax has released an open-source weight image generation model based on H3, H3-Image, which supports text-to-image, reference image generation, and image editing, capable of generating 4096 × 4096 4K images within 10 seconds, and supports up to 16 reference images.
Why it matters: The official provides 4K generation speed, reference image quantity, and pricing range, which can be used to judge the gap between open-source image models and closed-source solutions.
Seedream 5.5 outperformed GPT-Image 2.6 in high-pressure tasks of continuous image editing for 100 rounds, showing greater stability. The model enhances realism, supports small text and multi-language rendering, transparent image generation and editing, as well as layer splitting and text secondary editing functions. Additionally, Seedream 5.5's API price has dropped to 0.28 yuan per image, with improved speed, maintaining the 'input image + output image' two-stage billing structure, and no text token fees.
The DecoderFeatured7171Why it matters: The original text lists three capabilities: multi-round editing stability, native RGBA output, and text rendering, which can be used to determine its position in the design production process.
MiniMax · Hugging Face new modelsMiniMax-H3-Image-PE uses prompt expansion (PE) to convert short requests into detailed image descriptions and recommends aspect ratios. It supports two workflows: text-to-image (T2I) and image editing, using fine-tuned FP4 weights from Qwen3.8-27B. The online API can be accessed via platform.minimax.io or platform.minimax.cn, and the documentation provides support in both Chinese and English.
The Pangram Image 1.0 model locates AI-generated content using heatmaps, achieving an accuracy of 99.96% on a test dataset of 1200 photos. This model can resist the effects of compression, scaling, and artificial modifications, with an accuracy of 96.4% still maintained at 256×256 resolution. The simultaneously released Pangram Image Flash 1.0 video detection model achieves an accuracy of 98.7% on the DF26 dataset, leading the second place by 29 percentage points.
MarkTechPostFeaturedSame story · 2 outlets7070OpenAI launches Images 2.6 on ChatGPT, Codex, and API, offering GPT-Image-2.6 Flare and Sunburst models, with the official claim that generation speed is 50% faster than the previous version
OpenAI NewsFeaturedSame story · 2 outlets6262OpenAI has released Images 2.6, bringing faster image generation, GIF creation, more precise editing, and new API controls. This version covers three access points: ChatGPT, Codex, and API.
Why it matters: The original text lists four changes: image generation speed improvement, GIF creation, more precise editing, and API control, which can be used to judge the boundary of image capabilities for ChatGPT, Codex, and API.
China Merchants Lion Mountain Laboratory releases the 'Embodied Reasoning Visual Language Model Lion-ER-35B-A3B', adopting the MoE mixed expert model architecture, with approximately 35B total parameters, and each token only activates about 3B parameters.
The GroundingPI model jointly released by Xpeng, Peking University, Hong Kong University, etc., scored 73.68 in a comprehensive evaluation of 34 perception benchmarks, surpassing GPT-6 Astra's 71.54. The model, with a parameter size of about 4 billion, adopts a 'perception-first' technical approach, demonstrating higher data processing efficiency and stronger scene generalization capabilities in robotic simulation tasks, significantly reducing teaching costs while maintaining stability.
MarkTechPostTencent WeChat Vision team releases open-source visual language model WeVisOne-VL, including 2B and 4B parameter versions, supporting detection, OCR, segmentation, and Matting, using a 9.6M parameter decoder to output pixel results, surpassing same-scale baselines in over 30 benchmark tests, with the 4B version achieving a 71.3 F1 score on Dense200 dense detection.
MiniMax · Hugging Face new modelsMiniMax-H3-Image is an open-source image generation and editing model in the MiniMax H3 family, supporting 16MP image generation, image editing, and up to 16 reference images. It adopts the H3 architecture and uses the Qwen3-VL text encoder, with 68.65GB of BF16 precision model weights provided on Hugging Face.
Moonshot AI · Hugging Face new modelsKimi K3.1 Pro is an open-source multimodal AI model by Moonshot AI, with 2.8T parameters, 104B activation parameters, and a context window of 1M tokens, demonstrating excellent performance in areas such as coding, 3D creation, and financial research. Compared to Kimi K3, it adopts Gated DSA layers to improve long text processing efficiency and has outperformed competitors like Claude Fable 5.1 and GPT-6 Astra in benchmarks such as Terminal-Bench 4.0.
Ant Group inclusionAI · Hugging Face new models3838inclusionAI releases ZwZ2-9B GGUF format weights, belonging to the Zooming without Zooming v2 project, including language models (FP16, Q8_0, Q4_K_M) and visual encoders and projectors (mmproj, FP16, Q8_0, Q4_K_M), requiring pairing with llama.cpp Qwen3.5 multimodal support for use, supporting CLI inference and Web Chat deployment.
Ant Group inclusionAI · Hugging Face new models2222inclusionAI/ZwZ2-27B-GGUF model weights released, including language models and visual components with FP16, Q8_0, Q4_K_M precision, supporting llama.cpp multimodal inference.
Ant Group inclusionAI · Hugging Face new modelsThe inclusionAI/ZwZ2-4B-GGUF model weights have been released. This model is part of Zooming without Zooming v2 and is divided into a language model (FP16, Q8_0, Q4_K_M) and a visual encoder and projector (mmproj, FP16, Q8_0, Q4_K_M), offering multiple precision combinations. It needs to be used with llama.cpp (supporting Qwen3.5 multimodal).
XHEART NewCore Journey releases the X7 Ultra large model chip, achieving 700 TOPS of computing power, with mass-produced models achieving 99.99% alignment, a 80% reduction in memory bandwidth consumption, and a 100% improvement in energy efficiency. The chip incorporates a high-precision operator acceleration engine, natively supporting FP16/FP32 computing, and is the first in China to mass-produce Armv9 automotive-grade CPUs, offering a 20% performance boost and a 10% improvement in energy efficiency.
Liquid AI releases the LFM3 model family, with the first batch of multiple models based on LFM3-1.7B-Base, among which LFM-1.7B has a 256K context window, outperforming the previous generation LFM2.5-2.6B while reducing parameters by 35%
WuWen CoreVault ranks first among all participating teams in the NVIDIA Research GPU operator performance evaluation list SOL-ExecBench with 88 single-item first places, covering four major tracks: L1 basic operators, L2 fusion operators, Quantization operators, and FlashInfer-Bench inference operators. Through containerized isolation architecture, modular Skills, and shared memory mechanisms, combined with refined optimization strategies for different computing scenarios, it achieves the ability of AI to autonomously complete complex operator optimization.
Microsoft AI NewsFeaturedSame story · 2 outlets6767Microsoft AI released the image editing model MAI Image 2.7, launching as a preview version. It ranks 3rd in Arena Edit and 2nd in the Artificial Analysis image editing chart, making Microsoft AI the 2nd place lab.
Why it matters: Microsoft announced the rankings of MAI Image 2.7 on two image editing lists and provided the improvement magnitude on 10 use cases compared to the previous generation.
The OXR open-source model topped all three benchmark tests, OmniDocBench, olmOCR-Bench, and DocParsingBench, with complete parsing speed exceeding PaddleOCR-VL-1.6 and MinerU2.5-Pro
MarkTechPost5858Kyutai releases CADRE, an open-source causal diffusion representation autoencoder designed for interactive world models, reducing action latency below 230 ms. CADRE includes 7 autoencoders, each with approximately 476M parameters plus 86M frozen DINOv3 ViT-B/16, a world model of 1.2B parameters, video resolution of 288×512, frame rate of 20 fps, running on 1 H100.
Why it matters: The article provides specific evaluation numbers for model parameter scale, text rendering, and layout control, allowing for an assessment of open-source image generation models' position in design-related tasks.
The DecoderCanva has released its first self-developed image generation model, Canva Craft, claiming its performance can compete with similar models from Google, OpenAI, ByteDance, and Meta. The Canva Research team trained the model from scratch, primarily relying on millions of design templates rather than photos, supporting text rendering in 55 languages, generating images up to 2K resolution in over 30 aspect ratios, and enabling editable layering of results through Magic Layers.
Tencent Hunyuan · Hugging Face new modelsTencent Mixuan released the WeVisOne-VL-4B model, built on Qwen3-VL, integrating a 9.6M parameter pixel decoder, supporting object-level perception (open-vocabulary object detection, pointing localization, counting, pointing and OCR) and pixel-level perception (pointing segmentation and promptable alpha friction), suitable for research tasks such as visual localization, pointing, counting, OCR localization, pointing segmentation, and image friction.
vLLM Blog5858vLLM provides zero-day support for MiniMax-M3.1-Flash on NVIDIA Blackwell, Rubin, and AMD MI350 series GPUs, performing excellently on agent workloads.
Comfy BlogBlack Forest Labs releases open-source FLUX 3 Image Klein model with 5.6B parameters, outperforming Qwen-Image 2.1 on text-to-image and multi-reference generation tasks, supporting both Base and Distilled versions, with the latter offering 4.3-9.3 times speed improvement, suitable for consumer-grade GPUs.
Microsoft AI NewsMicrosoft AI has completed the pre-training of a new MoE large model MAI-Base-2, with 80 billion active parameters and 2.8 trillion total parameters, surpassing all open-source models in internal evaluations. The model is 4-12 times more efficient in pre-training FLOPs than the strongest open-source base model DeepSeek-V4-Pro-Base and is used to train larger models. Microsoft plans to license the model weights through Foundry using organic data pre-training and full-stack control.
Baseten BlogNVIDIA Nemotron 3.5 Super VL combines visual understanding, reasoning, and tool use, supporting reading documents, images, application interfaces, and videos, scoring 1190 Elo in the GDPval evaluation, higher than Nemotron 3 Super's 493. Developers can fine-tune it via Baseten Loops SDK and evaluate it using NVFP4 weights on Blackwell GPU.
NVIDIA BlogNVIDIA Omniverse, combined with cutting-edge AI models like GPT-6 Astra, helps developers achieve simulation applications by guiding AI agents to complete asset assembly, physical connections, and rendering checks through natural language instructions.
Liquid AI BlogLiquid AI has launched the d1 decision model, now supporting text and image input, surpassing GPT-6.1 Sol and Claude Opus 5.5 in six practical applications, with costs reduced by 19-200 times and significantly improved speed.
Claude.dev BlogClaude Sonnet 5.5 is the second model in the Claude 5.5 family after Opus 5.5, more intelligent, efficient, and 30% faster than Sonnet 5, but the price per token remains unchanged, as the cost is reduced due to a maximum of 30% reduction in token consumption for typical tasks.
