What's going on with open-weight models?

No matter the trough from which you greedily snarf your newsslop, be it X, Insta, or god forbid LinkedIn, you’ve probably seen some dramatic headlines around unfamiliar AI models with weird, foreign sounding names:

Kimi3 just murdered Claude Sonnet

Qwen 3.8 just made OpenAI’s GPT 5 look like a child's plaything

Deepseek v4 just obliterated Google’s Gemini 3.6 on ChudBench XXL, outperforming the smartest person you know on the hardest thing ever

AI Lab CEOs HATE this one set of weights. You won’t believe where they came from

These models are the new kids on the block, and they’re open weights. This means that, and we’ll get into this in more detail in a bit, the researchers that built them openly publish their secret mathematical sauce on the web for anyone with a big enough computer to use. Contrast this with models like Claude(s) and ChatGPT, where their weights are carefully guarded behind lock and key.

But despite all the progress and hype for open weights models, almost all of us are still using Claude and ChatGPT. This is no accident. The frontier labs have spent an unprecedented amount of capital to do a few key things:

1. Create amazing AI models that write code, build financial models, and draft legal memos better and faster than you.
2. Create great, user friendly applications like Claude Code and ChatGPT desktop, and pair them with highly optimized user acquisition funnels such that you slide down it into their ecosystem as if covered in venture-backed baby oil. And, and this is the one I take issue with,
3. position themselves as the only viable options for serious work. The Coke and Pepsi; the Marlboro and Camel; the McDonalds and Burger King of AI. Would you bring RC cola to your niece’s quinceanera? Would you light her up a Parliament? Absolutely not. Me neither, my niece is going to be a Marlboro girl.

The truly superior model and harness quality of the big labs was a reality for a brief moment around 2024, which made their claims about being the only serious options generally true unless you were a turbonerd (we are turbonerds, which is why you pay us).

But over the past 2 years, the rate of advances in open source models and harnesses have outpaced the frontier labs, and that old RC cola setup is starting to taste pretty damn good.

In this series, we'll cover what the open source AI community has been up to, why you might want to leverage their work, and how to get started if you're serious about churning off the big two.

The components of an AI stack#

When you ask ChatGPT what to make for dinner or why your life didn’t go how you wanted it to go, you’re not just using a model. You are using two things:

1. A model
2. A harness

These terms are worth defining in detail. Recently, my colleague at Technically, Paul Iusztin, wrote a great article on Harness engineering that lays out the components of an AI stack beautifully, so I'm going to steal heavily from him here.

A model can only talk. A model answers a prompt with the words it predicts are most likely to come next in an appropriate response based on its weights. Weights is an astoundingly unsexy term for the most important component of an AI model. Weights tell the model how to mathematically predict the best responses to given inputs. Anthropic and OpenAI serve closed weights models, keeping their math behind a wall, but allowing you to talk with the model through their products. Open weights models from companies like Qwen and Moonshot publish their weights on the internet for anyone to host and use.

A harness is everything around the model and how you use it; and increasingly where much of the real value in AI comes from. Harnesses include pretty much everything you need to use a model that’s not the model itself:

• The literal application you use to interact with the model’s API
• What allows the model to run code that it writes, using sandboxes
• What makes it feel like the model "remembers" things about you between sessions, by using agent memory
• What allows it to search the web for answers or draft word documents via tools
• …and more, depending on your setup

The harness is what makes AI useful for real day to day work, as opposed to the raw model which is really just the brains of the operation. This combination of a model and harness is known as an agent.

Claude and ChatGPT are examples of closed source chat harnesses, and Cursor, Codex, and Claude Code are closed source coding harnesses. Specialized harnesses are increasingly popular as well, such as Harvey for legal matters and Figma AI for design.

On the open source side, OpenCode and Pi lead the coding harnesses. LibreChat, AnythingLLM, and Jan are open-source chat harnesses, giving you a self-hosted ChatGPT-style interface that can plug into any model. For general-purpose personal assistance, OpenClaw does the same thing outside a chat window, connecting any model to messaging apps like Telegram and WhatsApp with persistent memory and a skills system to let it run your life. OpenClaw is also a harness! It’s a piece of software that's primary job is to talk to a model and make its output useful for your task.

For a deeper dive into harnesses, check out Paul's post here.

Alright, so we've got the components down. With these definitions in mind, let's discuss what's been going on in the open weights/source world.