multi-model arbitration, synthesis, parallel reasoning etc. Judging large models with small models is quite effective.

I think it would assist in exploiting exploring multiple solution spaces in parallel, and can see with the right user in the loop + tools like compilers, static analysis, tests, etc wrapped harness, be able to iterate very quickly on multiple solutions. An example might be, "I need to optimize this SQL query" pointed to a locally running postgres. Multiple changes could be tested, combined, and explain plan to validate performance vs a test for correct results. Then only valid solutions could be presented to developer for review. I don't personally care about the models 'opinion' or recommendations, using them for architectural choices IMO is a flawed use as a coding tool.

It doesn't change the fact that the most important thing is verification/validation of their output either from tools, developer reviewing/making decisions. But even if don't want that approach, diffusion models are just a lot more efficient it seems. I'm interested to see if they are just a better match common developer tasks to assist with validation/verification systems, not just writing (likely wrong) code faster.

I've tried a few computer use and browser use tools and they feel relatively tok/s bottlenecked.

And in some sense, all of my claude code usage feels tok/s bottlenecked. There's never really a time where I'm glad to wait for the tokens, I'd always prefer faster.

Once you make a model fast and small enough, it starts to become practical to use LLMs for things as mundane as spell checking, touchscreen-keyboard tap disambiguation, and database query planning.

Interesting perspective. Perhaps also the user would adopt his queries knowing he can only to small (but very fast) steps. I wonder who would win!

I think your comment is a bit unfair.

> no reasoning comparison

Benchmarks against reasoning models:

https://www.inceptionlabs.ai/blog/introducing-mercury-2

> no demo

https://chat.inceptionlabs.ai/

> no info on numbers of parameters for the model

This is a closed model. Do other providers publish the number of parameters for their models?

> testimonials that don't actually read like something used in production

Fair point.

Reading such obvious LLM-isms in the announcement just makes me cringe a bit too, ex.

> We optimize for speed users actually feel: responsiveness in the moments users experience — p95 latency under high concurrency, consistent turn-to-turn behavior, and stable throughput when systems get busy.