Today we're launching OM2. Your AI re-reads your entire company from scratch every time you ask it something. It’s why more than 50% of your token bill isn't in the answer, but in the search for it. OM2 gives your AI a permanent memory of your company. 🧠

It is clear open source models and harnesses are having a moment. There's a few factors at work 1/ It is now obvious that you can catch up to near-SOTA performance and do so with a clear training lineage. See:@thinkymachines Inkling launch today. 2/ There are several well-funded, talented teams building open weight models now in the US and abroad. Along with the explosing of other near SOTA models (Grok/Cursor, Muse Spark), it is clear we are going to have a diverse ecosystem of models atleast on coding and agentic use. 3/ Organizations are increasingly looking for control over how their data is used and are willing to trade off some access to frontier level tokens for this control. Organizations and countries are increasingly nervous about the frontier labs potentially competing with them down the road and don't want their data to enable a future competitor. 4/ Open source is a slider: you could bring your own open harness, your evals, your business context and are free to pick and choose your model of choice. 5/ Companies have now actively shifted from "how do we get our people to use tokens" to being uncomfortable with their token cost ballooning without a clear line to revenue. 6/ Geo-politically, countries will be weighing open weight models as a way to get frontier-level tokens inside controlled environments that may not be otherwise possible. All of this leads to more choice for all of us !

IT'S HAPPENING any second now

🚨 YOU'RE OVERPAYING FOR AI BY 5X. AND ALMOST NOBODY HAS NOTICED. every task you run goes to the most expensive model, even when an open model one nails it for a fraction of the price (yes, the one china dropped during the Fable 5 ban benchmarks near Opus 4.8). You can fix it and save thousands with this new tool: → it routes each task to the right model, open or closed, automatically → same frontier quality, ~5x less in tokens → ask for a deck, dashboard, doc or app, get better results with less. I ran it on real client work and they couldn't tell the difference. except in the bill lol. stop paying frontier prices for intern tasks.

Introducing Open Artifacts by Coworker. Build beautiful work products, routed to the right model for every task. Frontier outputs. Exactly the right context. 80% lower cost.

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching. Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work. Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task. Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented. Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted. Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect. The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable. Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.