webAI

The cost of completing a task matters more than the raw intelligence of the model running it.

That’s the thesis behind TwIL-8B, our new 8.8B reasoning model. Open weights. Runs on hardware you already own.

Against Qwen3.5-9B, it opens a 16-point lead on LiveCodeBench (81.6 vs 65.6) and tops MATH-500 (98.2 vs 96.6). Against Gemma 4 31B, 4x the size, it still wins on MATH-500 (98.2 vs 87.9) and AIME 2025 (88.3 vs 87.1).

Tiny by frontier standards. Does the reasoning work anyway.

Download the model on @huggingface: huggingface.co/webAI-Official…

Local ai wins.

Base-conversion arithmetic breaks most small reasoning models.

There’s no base-10 shortcut, so every carry has to be worked through directly in the target base. Most small models skip the carry logic, or convert back to base-10 at the end and hope.

TwIL-LM3-Pro doesn’t. It walks each step in the target base and holds state across the derivation.

The same pattern shows up across our full formal logic suite. TwIL-LM3-Pro leads every model we tested on in-domain formal logic, including Qwen3-8B at 2x the parameters, gpt-oss-120b at 33x the parameters, and both VibeThinker variants.

A 3.66B model built for formal reasoning, beating models 33x its size at it.

And even on held-out general reasoning benchmarks we didn’t build for, it stays competitive with those same larger models.

Pattern matching gets you fluent answers. Formal reasoning gets you right ones.

Open weights, runs locally.

Download the model on @huggingface: huggingface.co/webAI-Official…

AI shouldn’t think for you. It should help you think for yourself.

The things that make your work different, the things that make it worth paying attention to, aren’t on the internet. They live in you.

That’s what webAI is built on: webai.com/waitlist

Most of the AI conversation is about scale. Bigger models, more parameters, longer context windows. The assumption being that bigger is smarter.

We’ve been building toward the opposite.

TwIL-LM3-Pro is a 3.6B reasoning model that handles formal problems models 7 to 10x its size struggle with, and it runs on a laptop.

Small isn’t a compromise. For the problems we care about, it’s the point.

A model that fits in RAM, answers in seconds, and can be understood end to end gets us closer to what AI should actually be: specialized, local, and yours.

Download the model on @huggingface: huggingface.co/spaces/webAI-O…