Open-source ecosystem

AIThinking Machines is releasing Inkling-Small, a model it says achieves performance comparable to Inkling at a quarter of its size. The model has 276B total parameters with 12B active, and full weights are available. It can be fine-tuned on Tinker or used for text, image, and audio chat in the Tinker Playground.

Why it matters: The release gives comparable performance at a quarter of Inkling's size, with full weights available, which matters for teams weighing self-hosting against larger models.

AIPoolside released Laguna S 2.1, an open-weights agentic coding model with 118 billion total parameters and about 8 billion active per token, supporting up to a million tokens of context. Quantized, it fits on one NVIDIA DGX Spark, and Poolside reports 70.2% on Terminal-Bench 2.1 with thinking enabled, with its evaluation trajectories published online. The same week it shipped the Poolside Desktop Assistant for macOS, which runs Laguna locally or alongside Claude Code, Codex, and Gemini agents.

Why it matters: The piece ties Laguna S 2.1's open weights and published trajectories to Poolside's release cadence, showing how its model factory compounds gains across successive releases.

AIMiniMax released H3, an open-weights omni-modal model that generates video with native stereo audio up to 2K and 15 seconds. The system combines H3-Context-IR preprocessing, the H3-Base generator at 768p, and H3-Regenerate-2K for 2K output, with the Context-IR and 2K modules available only through API.

Why it matters: The source details a three-module pipeline and open weights with deployment paths, showing how a video model is served and reproduced locally.

AIBlack Forest Labs released FLUX 3, a multimodal model trained on images, video, and audio, and mimic built FLUX-mimic on its video backbone to control robots. In a soft-body kitting task, mimic reports a 95% success rate without single-task fine-tuning, compared with 55% for an adapted π0.5 model. FLUX 3 Video is in early access, with action prediction offered to selected partners and an open-weight backbone planned.

Why it matters: The piece shows how a video generation backbone can be repurposed for robot control, with benchmark results and an explanation of the frozen-backbone ablation.

AIFireworks AI made the open-weight Kimi K3 available for inference and training on its platform, with US-only serverless endpoints and Zero Data Retention. In its own head-to-head with Opus 5, the post reports K3 at 92.7% accuracy and $0.52 per task on SWE (480) against Opus 5's 94.8% and $1.05, with the vendor claiming up to 5x better cost efficiency per task.

Why it matters: The post compares Kimi K3 with Opus 5 on accuracy and cost per task, giving readers concrete figures to judge the open model against closed alternatives for their own workloads.

AIBAAI has released AREX-Base, a 122B-total, 10B-activated Mixture-of-Experts deep research agent built on Qwen3.5-122B-A10B with a 262,144-token context. The model uses an inner research loop and an outer self-improvement loop, and the source reports it scoring 82.5 on BrowseComp and 85.4 on GAIA, under Apache 2.0.

Why it matters: The release pairs a 122B-parameter deep research agent with benchmark tables against frontier and open models, letting readers compare its search-agent results directly.

AILeandro von Werra introduces The Stack v3, a dataset of 5T tokens ready for training and 120TB of raw data. He says the dataset is meant to support open code models for cyber defence, and links the download on Hugging Face.

Why it matters: The release is a large training dataset for code models, a resource that matters to teams building open code models for security work.

AIThinking Machines has introduced Inkling, an open-weights model that reasons across text, image, and audio, with full weights made available. The model is available today for fine-tuning on Tinker, and the company also offers an Inkling Playground for trying it out. The author describes Inkling as a foundation model intended for broad capabilities in practical use and customization.

Why it matters: The quoted announcement names the modalities and fine-tuning access, which helps readers judge whether Inkling fits their open-weights workflow.

AIThinking Machines introduced Inkling, a model that reasons across text, image, and audio, and is making its full weights available. It is available today for fine-tuning on Tinker and can be tried in the Inkling Playground. John Schulman says pretraining began last winter and a small team added coding, reasoning, and agentic training starting in mid-January.

Why it matters: The post links an open-weights release to a stated training timeline, showing how a small team moved from pretraining to coding, reasoning, and agentic training.

AIThinking Machines introduced Inkling, an open-weight model with 975B parameters that reasons across text, image, and audio. The full weights are available, with fine-tuning on Tinker and access through the Inkling Playground and Hugging Face and partners.

Why it matters: The release is a 975B open-weight model covering text, image, and audio, so its availability on Tinker and Hugging Face matters for fine-tuning and open use.

AIMira Murati announced Inkling, the first model from Thinking Machines, trained from scratch with its full weights made available. The model reasons across text, image, and audio, and is available today for fine-tuning on Tinker and testing in the Inkling Playground.

Why it matters: The post names the model, its training origin, and open weights with Tinker fine-tuning, giving readers the access terms for evaluating it.

AIZ.ai has released GLM-5.2, its flagship model for long-horizon tasks, which it says substantially improves on GLM-5.1 and supports a 1M-token context. The model adds IndexShare, which cuts per-token FLOPs by 2.9× at 1M context, and is released under the MIT open-source license.

Why it matters: The source gives benchmark tables against named rival models and deployment settings, useful for judging where GLM-5.2 sits among current flagship models.

AIMoonshot AI released Kimi K3 on Hugging Face as an open-weight, native multimodal agentic model with 2.8T total parameters and 104B activated parameters. It supports a 1-million-token context window and text and image input, with weights released under the Kimi K3 License. The model card reports benchmark results for coding, agentic, and vision tasks against several closed models, and recommends vLLM, SGLang, or TokenSpeed for inference.

Why it matters: The release pairs open weights with a 2.8T-parameter MoE architecture and benchmark tables against several named closed models, useful for comparing frontier capability claims.

AIMoonshot AI published Kimi-K2.7-Code, a coding-focused agentic model built on Kimi K2.6, with a 1T-parameter MoE architecture and 32B activated parameters. The model card reports about 30% fewer thinking tokens than K2.6 and benchmark results against GPT-5.5 and Claude Opus 4.8, with weights and code released under a Modified MIT License.

Why it matters: The model card gives benchmark comparisons against GPT-5.5 and Claude Opus 4.8 on coding and agentic tasks, useful for judging its position among current coding models.