#Model release

AIGoogle's Koray Kavukcuoglu says Gemini 3.8 Flash is a major step up from Gemini 3.7 Flash and outperforms most larger frontier models on complex engineering problems at a fraction of the cost. The attached DeepSWE V1.1 chart, sourced to Datacurve AI, plots average cost per task against score for Gemini 3.8 Flash and other models. A link to Google's blog post with more details is included.

AIGoogle launches Gemini 3.8 Flash Cyber and Gemini 3.8 Flash. The post describes Flash Cyber as its most capable cybersecurity model for finding and fixing vulnerabilities, placing it on the Pareto frontier for patching on CWE-Bench. Flash Cyber is available to trusted defenders through the new Fairwind Program.

AIGoogle's Logan Kilpatrick says Gemini 3.8 Flash costs the same as Gemini 3.7 and runs at roughly the same speed. The model is available through the Gemini API, AI Studio, Antigravity, the Gemini App, and other surfaces.

AIGoogle announced Gemini 3.8 Flash, its third updated Flash model in six weeks, citing improvements in agentic and coding capabilities. The benchmark table lists input at $0.75 and output at $3.75 per 1M tokens, with introductory pricing of $1.50 and $7.50 expiring December 31, 2026. Terminal-bench 2.1 shows 89.4% for Gemini 3.8 Flash against 85.8% for Gemini 3.7 Flash.

AIGoogle AI Studio announced Gemini 3.8 Flash, which it calls its most intelligent workhorse model. The company says it brings significant improvements over 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning in specialized domains. It is available at the same introductory price as 3.7 Flash, $0.75 per million input tokens and $3.75 per million output tokens, through the Gemini API and AI Studio.

AICohere Labs released Tiny Aya En-Thinker, an open-weights 3.35 billion parameter multilingual reasoning model with a 32K context length. It is trained on English reasoning traces for 44 languages plus English, with coverage extending to 20+ more languages through non-reasoning instruction data. The model is available under a CC-BY-NC license that also requires adherence to Cohere Labs' Acceptable Use Policy.

AICohere Labs released Tiny Aya L2-Thinker, an open-weights 3.35 billion parameter multilingual reasoning model that thinks in the same language as the user's prompt before answering. The model supports in-language reasoning for 44 languages plus English, with coverage extended to 20+ more languages through additional non-reasoning instruction data, and has a 32K context length. It is licensed under CC-BY-NC and is available on Hugging Face.

AIUnsloth says MTP enables Qwen3.8-Flash-Next to run about 1.3 to 1.7 times faster at inference with no accuracy change. GGUF versions can reach 170 tokens/s on an RTX PRO 6000, and the source lists memory requirements from 76 GB at 1-bit to 355 GB at BF16.

AIGoogle has released gemini-3.8-flash, described as its most intelligent Flash model, for long-horizon software engineering, autonomous agents, and complex enterprise workflows. The Gemini 3.8 Flash model page and the Latest model guide are linked for getting started.

AIClaude Fable 5.1 is now available in v0 for users on Premium and Plus plans. The post links to v0.app for users to try the model.

AIAnthropic has released Claude Fable 5.1, the latest upgrade to its most capable class of models, and it is available everywhere today. The company says it handles complex, long-running, multi-step work and avoids shortcuts when fixing root causes of software issues. At lower effort levels, Fable 5.1 can match or beat Fable 5 at a much lower cost, according to Anthropic's benchmarks.

Why it matters: The source names the upgraded model class and its cost tradeoff at lower effort levels, which helps readers weigh it against the earlier version for their own workloads.

AIThe model keeps working until it reaches a given target and says so when it is stuck, rather than reporting false success. It is priced at $10/$50, the same as Fable 5, and cache reads on the API are cut 75% to $0.25/MTok.

AIMike Krieger announced that Claude Fable 5.1 launched this morning and is built for complex, multi-step work that runs on its own. He says it sets a new standard across coding, knowledge work, and long-running problem-solving tasks. The quoted @claudeai post also introduces Claude Mythos 5.1 alongside it.

AIAlex Albert describes Claude Fable 5.1 as a model that fills in gaps from vague, messy instructions the way he would. He calls it impressive in many ways and encourages people to try it. The quoted post from @claudeai announces Claude Fable 5.1 and Claude Mythos 5.1 as the world's most advanced models for coding and knowledge work.

AIAnthropic has released Claude Fable 5.1, an upgrade to its most capable model class, and says it is available everywhere today. The company reports that at lower effort levels, Fable 5.1 can match or beat Fable 5 at a much lower cost. It is described as strong at complex multi-step work, such as long proofs and contracts with hundreds of cross-references, and at fixing root causes in software issues.

Why it matters: The source reports cost and effort-level tradeoffs for long-running tasks, helping readers judge whether the upgrade changes their workloads or budgets.

AIAi2 has published supplemental checkpoints for its ACE2S-SHiELD+ climate model on Hugging Face, covering four ablation configurations that test random CO2 data and energy conservation. Each configuration includes two random-seed models, and the repository recommends the main ACE2S-SHiELD+ checkpoint for most uses. The checkpoints are licensed under Apache 2.0 for research and educational use.

AIWorld Labs has introduced Atlas, a multimodal world model it describes as trained from scratch. The post says Atlas generates frames with pixel-perfect camera control, reconstructs large scenes from as few as one input image, and outputs 3D spaces from one or more images. The author cites use cases including VFX and robotics.

AIGoogle has released GNM v3.0, a parametric 3D statistical model of the human head, with weights published on Hugging Face and Kaggle under the Apache 2.0 license. The model gives controllable identity, expression, head pose, and internal anatomy including eyeballs, teeth, and tongue, and supports NumPy, JAX, PyTorch, and TensorFlow backends.

AITencent Hunyuan says its Hy4 preview model found inference bottlenecks on its own and raised end-to-end throughput by 31.8% through operator fusion and communication optimizations. The post says the gain holds across context lengths and concurrency levels. The release is listed at 770B total parameters with 49B active and a 1M context window, with links to the Hy blog, Hugging Face, and GitHub.

AIOpenBMB released MiniCPM5-2B-Midtrain, a BF16 mid-training checkpoint taken before SFT in the MiniCPM5-2B series, on Hugging Face and ModelScope. The series is a 2B dense Transformer with 2,516,756,480 total parameters and a 131,072-token context length, and the final MiniCPM5-2B reports an average score of 53.9 against 51.1 for the best larger comparison model. The release also includes GGUF, MLX, and GPTQ variants, along with the UltraData datasets.

AIShanghai AI Lab's InternLM has published Intern Lumina U2, a 16B-parameter MoE model with 1B active parameters that handles text QA, image generation and editing, and image, video, and 3D understanding. The model uses an 8-codebook fully-discrete visual representation built on AToken. Checkpoints are provided for Huawei Ascend NPUs and NVIDIA GPUs under Apache 2.0, with the technical report still listed as coming soon.

Why it matters: The model unifies text, image, video, and 3D understanding with image generation in one framework, a broader scope than single-modality releases.

AIAnthropic has launched Claude Fable 5.1 and Claude Mythos 5.1, which it describes as the world's most advanced models for coding and knowledge work. The release notes link to a blog post with more details, but the notes themselves give no benchmarks or specifications.

Why it matters: The source names two new model versions and points to a companion blog post, so readers can compare the release details there.

AIMicrosoft Research released GigaPath-Flash and GigaTIME-Flash, efficient pathology foundation models built on a distilled ViT-S backbone and released under the Apache 2.0 license. GigaPath-Flash, with 22M-parameter tile and 21M-parameter slide encoders, reportedly scores within 3% of the original GigaPath on PANDA and EBRAINS benchmarks at roughly 50 times less compute. The models are research tools, not validated for clinical use.

AIDeepSeek introduces DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model in the DeepSeek-V4 family, built on V4-Flash with visual modules. It reports substantial gains over DeepSeek-V4-Flash-0731 on multimodal agent benchmarks, such as ApexBench at 36.5 versus 26.2, while keeping text agent performance comparable. The repository provides tokenizer files, prompt encoding, vLLM and SGLang serving instructions, and is licensed under MIT.

Why it matters: The source compares the model with its text-only predecessor and Opus-4.8 on agent benchmarks, showing where vision gains occur and where text performance holds.

AIAlibaba NLP has released core-emb-8b, an MLLM-based multimodal embedding model that distills a reranker's compositional judgments to distinguish attribute-object bindings such as "a white plate and a black chair" versus "a black plate and a white chair." The 8B dense embedding model, built on the Qwen3-VL-based VL-Emb backbone, scores 0.666 total average on compositional benchmarks, 5.7 points above its backbone. It is part of a family that also includes 2B embedding and reranker models.

AIAlibaba-NLP has published Core-Reranker-8B on Hugging Face, an 8B-parameter multimodal reranker fine-tuned from Qwen3-VL-Reranker to better distinguish attribute-object bindings in text and image relevance scoring. On compositional reasoning benchmarks COLA, SugarCrepe++, and NegBench, it reports an 82.7% total average, 10.7 points above Jina-Reranker. The model is part of the Core-Embed family, which also includes 2B and 8B embedding models, with Core-Embed-8B reporting a 0.666 total average.

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.

AIUnsloth quantized GLM-5.3 to a dynamic 1-bit version of 217GB, versus 1.5TB for BF16, retaining about 76% top-1 accuracy while cutting size by 83%. The team said a 1-bit build ran a simple snake game well in Unsloth Desktop. The post credits Z.ai's GLM-5.3-Flash and GLM-5.3 releases.

AIUnsloth AI published a guide for running GLM-5.3 locally using quantized GGUF weights. The 2-bit version is reduced from 1.51TB to 239GB and retains about 81% accuracy, and it can run on a 256GB Mac or RAM/VRAM setups.

Why it matters: The guide shows which quantization levels fit local memory budgets and how much accuracy each costs, useful for planning a local deployment.

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.

AIZ.ai has made GLM-5.3 open-weight, so users can download, run, and customize its weights. The company describes it as its most capable model for agentic coding and cyber defense, with weights on Hugging Face and details in a tech blog.

AIMidjourney is starting tests of its first V8.2 edit model, which supports instruction-based editing and generating images from up to 4 reference images. The model also offers brush-based inpainting and outpainting, and works with personalization, moodboards, and srefs.

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.

AIReplicate announced that Gemini Omni 1.1 Flash from Google DeepMind is now live on its platform. The update adds scene extension, control of a shot's starting and ending frames, video input references, upscaling to 4K, and 360p fast prototyping.

AIGLM-5.3-Flash (ox-alpha) can be quantized to 4-bit while retaining 93% accuracy, according to Daniel Han. The post says the 4-bit model runs on a 256GB Mac or two DGX Sparks, and 5-bit may also work. Unsloth separately says 3-bit GGUF runs on 128GB RAM and that the model rivals Claude Opus 4.8 on DeepSWE, coding, and agentic benchmarks.

AIUnsloth says GLM-5.3-Flash can run locally, with a 3-bit GGUF version needing 128GB of RAM and the 1-bit version working on 102GB of RAM or VRAM. The guide's table lists memory needs from 100GB at 1-bit to 650GB at BF16, and reports that the 1-bit quant keeps 71% of top-1% accuracy while being 85% smaller than BF16.

Why it matters: The guide gives concrete memory requirements for each quantization level, which helps readers judge whether the model fits their hardware.

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.