#Model release

AIAlibaba NLP has released core-emb-2b and core-emb-8b, multimodal embedding models built on Qwen3-VL that distill reranker judgments to better match attribute-object bindings in text and image retrieval. The Core-Embed-8B model posts the best total average (0.666) among evaluated embedding models on compositional benchmarks, 5.7 points above its VL-Emb-8B backbone. Companion Core-Reranker-2B and 8B models are also available, with the 8B reranker reaching 82.7% total average on the same benchmarks.

AIAlibaba NLP released core-reranker-2b, a 2B-parameter multimodal relevance-scoring model built on Qwen3-VL-Reranker to better distinguish attribute-object bindings in text and image pairs. The Core-Reranker family also includes an 8B variant, and Core-Reranker-8B reports an 82.7% total average on compositional reasoning benchmarks COLA, SugarCrepe++, and NegBench, 10.7 points above Jina-Reranker. Usage details are provided in the source, including loading through the GitHub repository wrapper classes.

AITencent Hunyuan has open-sourced Hy4 preview under Apache 2.0, a flagship mixture-of-experts model with 770B total parameters, 49B active per token, and a 1M context window. Blind evaluation by 163 internal experts across 203 engineering tasks gave it an average score of 2.99, narrowly ahead of GLM 5.3 at 2.92 and Kimi K3 at 2.94. The model includes a native MTP layer for speculative decoding and is trained on production workflows spanning software engineering, data analysis, game development, and scientific research.

AITinker announces that Z.ai's GLM-5.3 is now available on its platform with a 256k context window. Tinker says it is currently the strongest open-weights model on coding evals including Terminal-Bench 3.0 and DeepSWE 1.1, built on the same base as GLM-5.2 with scaled-up post-training.

AILM Studio says Zhipu's GLM-5.3 is now available in its Bionic agent, hosted in the US with zero data retention. The launch offer is 50% off until Monday. The post builds on Z.ai's announcement that GLM-5.3 is now open-weight for agentic coding and cyber defense.

AIReplicate has made Google's Gemini Omni 1.1 available to try through a hosted model page. The post provides only a link to the model on replicate.com, with no specifications, benchmarks, or pricing mentioned.

AIOpenBMB released MiniCPM5-2B-SFT, an SFT-only BF16 checkpoint taken before RL and OPD, within its MiniCPM5-2B series. The model is a 2B dense Transformer built for on-device and local deployment, with 131,072-token context and the same training recipe as the final release.

Why it matters: The source gives concrete benchmark averages against same-size and larger models, plus released training data and multiple deployment formats, useful for judging a compact on-device model.

AIOpenBMB released MiniCPM5-2B, a dense 2B Transformer for on-device and resource-constrained deployment, alongside its training datasets. The source reports a 53.9 average across its comparison set and strong results in coding, math, long-context, tool use, and agentic tasks. This page is the pre-training base checkpoint, with BF16 weights and GGUF, MLX, GPTQ, and LiteRT-LM variants listed separately.

AITencent's Hy Team released Hy4 preview, a Mixture-of-Experts model with 770B total parameters and 49B activated per token, with a 1M context length. Hugging Face hosts the Instruct model and an FP8 quantized version under the Apache License 2.0, with vLLM and SGLang deployment instructions provided.

Why it matters: The model card gives architecture, activated parameters, and vLLM and SGLang deployment recipes, useful for judging whether the release fits your serving setup.

AIQwen has published Qwen-Drive-1.0-4B on Hugging Face, a vision-language model for autonomous driving built on Qwen3.5-4B. The release includes a BEV perception head and two Planning Experts, planner-sft and planner-rl, with code and an inference example in the linked GitHub repository.

Why it matters: The source gives concrete benchmark results and a runnable setup, letting readers judge how a driving VLM with planning and perception heads compares with existing systems.

AINVIDIA's DLSS 4.5 Ray Reconstruction is now available, using a second-generation joint denoiser and super-resolution model. According to the post, it delivers much better image quality at the same compute cost, pushing the trade-off between image quality and rendering cost further.

AIZ.ai announced that the weights for GLM-5.3 will be released tomorrow. The post links to the GLM-5.3 model page on Hugging Face.

AITencent has published ContextPilot-E4B on Hugging Face, the Gemma4-E4B checkpoint of ContextPilot, a framework that teaches long-horizon language-model agents to plan, maintain long-term memory, and offload less useful context while reasoning and using tools. The checkpoint is intended for research on proactive context management, long-context QA, and deep search, and loading it alone does not execute the context-management tools, which are provided in the ContextPilot repository.

AITencent has released ContextPilot-14B on Hugging Face, a Qwen3-14B checkpoint for proactive context management in long-horizon language-model agents. The framework lets agents plan, maintain long-term memory, and offload less useful context while reasoning and using tools. The checkpoint is intended for research on long-context QA and deep search, and loading it alone does not execute the context-management tools, which are provided in the ContextPilot repository.

AILM Studio announced that Z.ai's GLM-5.3-Flash, previously previewed as Ox Alpha, is available in LM Studio Bionic. The source says the model outperforms GLM-5.2 at 9-10x lower cost, supports image input, and is served from US-based servers with ZDR enabled by default.

AILM Studio published a model page for GLM-5.3 Flash on its website. The post contains only a link to the model page and no further details about the model's specifications or capabilities.

AIThe post links to a Google blog page about Gemini 3.5 Transcribe, presented as a model developers can start building with. The post itself gives no benchmark scores, pricing, or capability details beyond the link.

AIZ.ai released GLM-5.3-Flash, a 320B-A18B model, with day-0 support in SGLang, after appearing earlier as ox-alpha. The post calls it the first native multimodal model in the GLM-5 series and says it outperforms GLM-5.2 at one-tenth the cost, with stable 1M-token long-context performance.

Why it matters: The post reports GLM-5.3-Flash's native multimodal design, its efficiency claims, and day-0 SGLang support, which bear on running it in practice.

AISGLang announced Day-0 support for Qwen3.8-Flash-Next, a 125B MoE model with 6B active parameters and 51B N-gram embeddings, in collaboration with Alibaba Qwen, NVIDIA, and AMD. The post reports 540 tok/s decode speed at BS=1 on NVIDIA B200 (TP4) with an NVFP4 checkpoint, and says N-gram host offloading saves 23.5 GiB VRAM per GPU and raises KV capacity by 78.5%.

AIGeneralist AI introduces GEN-1.5, its latest foundation model for the physical world, with additional task demonstrations shared in a YouTube playlist. The post links to a blog post with further details about the model.

AIZ.ai has published standard API pricing for GLM-5.3-Flash, charging $0.15 per 1M input tokens and $0.50 per 1M output tokens. Cached input tokens are priced at $0.03 per 1M tokens.

AIAi2 has released Llama-B-8B on Hugging Face, a byte-level autoregressive language model retrofitted from Llama 3 8B through a short additional training procedure. The model operates over bytes instead of tokens and is licensed under the Llama 3 Community License for research and educational use. It requires transformers 4.57.3 and the xlstm package, and the source notes that model outputs can be inaccurate and should be verified.

AIAi2 has released allenai/Llama-B-8B-Stage1, a Llama 3 8B model retrofitted to operate over bytes instead of tokens through a short additional training procedure. This Stage 1 checkpoint contains only Stage 1 training, with inner model parameters unchanged, and is licensed under the Llama 3 Community License for research and educational use. It requires transformers 4.57.3 or later and the xlstm package, and is loaded with trust_remote_code.

AIAi2 has released Bwen-8B, a byte-level autoregressive language model retrofitted from Qwen3 8B Base through a short additional training procedure called byteification, which lets it operate over bytes instead of tokens. The model is licensed under Apache 2.0 for research and educational use, and requires transformers 4.57.3 or later and the xlstm package.

AIAi2 has released Bwen-8B-Stage1 on Hugging Face, a byte-level autoregressive model retrofitted from Qwen3-8B-Base through a short additional training procedure. This Stage 1 checkpoint contains only Stage 1 training, with inner model parameters unchanged, and is licensed under Apache 2.0 for research and educational use.

AIAi2 has released Bolmo-1B-Stage1, a 1B-parameter byte-level language model retrofitted from OLMo 2 1B to process bytes rather than tokens. This checkpoint includes Stage 1 training only, with inner model parameters unchanged, and is available on Hugging Face under an Apache 2.0 license for research and educational use.

AIAi2 has released Bolmo-7B-Stage1, a 7B byte-level language model retrofitted from Olmo 3 7B through a short additional training procedure. This checkpoint includes only Stage 1 training, with inner model parameters unchanged, and is licensed under Apache 2.0 for research and educational use.