WorldofAI explores the capabilities of this large-scale model, focusing on its performance in coding, research, and long-horizon reasoning. The review examines its multimodal functionality and utility in generating 3D assets, web applications, and interactive simulations, while demonstrating the test-sprite CLI tool as a mechanism for verifying agent-driven code outputs.
Poolside, the San Francisco AI lab that has spent most of its three-year existence quietly selling coding models to governments and defense agencies, released its most capable model to date — and made an unusually aggressive bet that radical transparency, not raw scale, is how a smaller lab competes at the frontier.
The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.
Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 — a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI.
The release, timed to land just ahead of the 2026 World Artificial Intelligence Conference in Shanghai, is a dramatic escalation in the global AI arms race and a watershed moment for the open-source AI movement. It also marks a remarkable comeback for a company whose market position had eroded significantly over the past 18 months following DeepSeek's meteoric rise.
Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, released its first in-house AI model Wednesday morning, called Inkling. And unlike the flagship models from OpenAI, Anthropic, or Google, it’s open-weight, meaning outside developers and companies can download it and modify it directly.
Inkling is a mixture-of-experts system with 975 billion total parameters, though it only draws on a fraction of that — about 41 billion — for any given task, a common design that keeps very large models faster and cheaper to run. It was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all four.
Chinese delivery app company Meituan officially unveiled LongCat-2.0 on GitHub, Hugging Face, and its native platform, unmasking the model as the computational engine behind "Owl Alpha," the anonymous stealth model that has spent the last two months commanding global developer charts on OpenRouter.
Developed to fundamentally disrupt closed-source enterprise dominance in autonomous software engineering, the 1.6-trillion-parameter Mixture-of-Experts (MoE) system brings a native 1-million-token context window to the public domain under a highly permissive, enterprise grade, commercially viable MIT license.
Sam Witteveen explores this new family of self-scaffolding models designed to generate their own task-specific harnesses alongside solutions. By utilizing a two-stage reinforcement learning process, these models aim to optimize both the coding environment and agentic trajectories, offering a versatile approach for handling complex local coding tasks without requiring human-authored scaffolds.
Kimi K2.7 Code might be one of the most impressive open-weight coding models released so far. In this video, we fully test Moonshot AI’s latest coding-focused model and see how it compares against Opus 4.8 Max, GPT-5.5, Fable 5, Qwen 3.7 Max, Grok 4.3, and other top coding models.
Today, Chinese AI startup Z.ai (formerly Zhipu AI) announced the immediate release of GLM-5.2, a 753-billion parameter open-weights large language model (LLM) engineered specifically to dominate "long-horizon" autonomous coding and engineering tasks.
Available immediately on Hugging Face, the Z.ai API, and more than 20 third-party coding environments, the model boasts a highly stable 1-million-token context window alongside enterprise subscription tiers starting at just $12.60 per month.
Google's DiffusionGemma, released this week, is an open source experimental model that applies diffusion to text generation at production scale. Built on the Gemma 4 backbone and released under the Apache 2.0 license, it is the first diffusion language model natively supported in the open source vLLM inference platform. It generates a 256-token block in parallel rather than sequentially, with every token position attending to every other. Google says DiffusionGemma generates text up to 4x faster than standard models on GPUs.
While many AI open source model providers are pursuing larger and more powerful models, Google is still giving attention to the smaller, more local side of the market. Today, the tech giant released Gemma 4 12B, an 11.95-billion-parameter open-weights model with permissive Apache 2.0 license optimized to execute locally on a standard enterprise laptop using just 16GB of VRAM or unified memory.
That means those enterprise users looking to keep working with AI while on a flight without WiFi, or trying to keep it offline for security reasons, can now do so far more easily and at far less cost (free to download and operate).
In this video, I fully test MiniMax M3, the new open-weight frontier model from MiniMax that combines coding, agentic reasoning, multimodal understanding, and long-context capabilities into one model. M3 supports up to a 1 million token context window, is natively multimodal from day one, and delivers some seriously impressive benchmark results across SWE-Bench Pro, BrowseComp, SVG-Bench, KernelBench Hard, OSWorld Verified, and more.
What makes this release even more insane is the pricing. MiniMax M3 is not only competing with models like Opus 4.7 and GPT-5.5, but in several benchmarks it actually beats them while being dramatically cheaper. MiniMax is also offering huge token plans, aggressive API pricing, and open-weight access, making this one of the most accessible frontier-level models available right now.
Poolside, founded in San Francisco in 2023. has launched its two new Laguna large language models, both of which offer affordable intelligence optimized for agentic workflows (AI that does more than just chat or generate content, but can, in this case, write code, use third-party tools, and take actions autonomously), as well as a new coding agent harness called (fittingly) "pool" and a new web-based, mobile optimized agentic coding development and interactive preview environment, "shimmer," which lets you write code with the Laguna models on the go.
As Poolside wrote in a blog post today, it's spent the last few years "focused on serving our government and public sector clients with capable models deployable into the highest-security environments," yet is now going open source "to support builders and the wider research community."
In this video, I fully test Kimi K2.6 across real-world tasks, from long-horizon coding to agent workflows and even front-end website generation. This model isn’t just competitive — it’s going head-to-head with top proprietary models like Opus 4.6 and Gemini 3.1 Pro… and in some cases, outperforming them.
Z.ai, also known as Zhupai AI, a Chinese AI startup best known for its powerful, open source GLM family of models, has unveiled GLM-5.1 today under a permissive MIT License, allowing for enterprises to download, customize and use it for commercial purposes.
The new GLM-5.1 is designed to work autonomously for up to eight hours on a single task, marking a definitive shift from vibe coding to agentic engineering.
Gemma 4 arrives as four distinct models organized into two deployment tiers. The "workstation" tier includes a 31B-parameter dense model and a 26B A4B Mixture-of-Experts model — both supporting text and image input with 256K-token context windows. The "edge" tier consists of the E2B and E4B, compact models designed for phones, embedded devices, and laptops, supporting text, image, and audio with 128K-token context windows.
In this video, we look at LiteParse, a new open document parser created by the people at LlamaIndex. This library allows you to pass a variety of different types of documents. and output easily to text files or JSON.
I was curious if Block's Goose agent, paired with Ollama and the Qwen3-coder model, could really replace Claude Code.
Goose, developed by Jack Dorsey's company Block, is an open-source agent framework, similar to Claude Code. Qwen3-coder is a coding-centric large language model similar to Sonnet-4.5.
Cinese company Moonshot AI upgraded its open-sourced Kimi K2 model, transforming it into a coding and vision model with an architecture that supports an agent swarm orchestration.
The new model, Moonshot Kimi K2.5, is a good option for enterprises that want agents that can automatically pass off actions instead of having a framework be a central decision-maker.
In this video, I look at the Open Responses Standard that's been released by OpenAI to support open models with their Responses SDK.
Nvidia dropped 13 open models at their CES 2026 event. NVIDIA’s open models — trained on NVIDIA’s own supercomputers — are powering breakthroughs across healthcare, climate science, robotics, embodied intelligence and autonomous driving.
