Simon, A little offtopic but it seems that your website isn't working.

> An error occurred in the application and your page could not be served. If you are the application owner, check your logs for details. You can do this from the Heroku CLI with the command

I get this error when I go to simonwillison.net

Any random blog/link works for example though: https://simonwillison.net/2026/Mar/19/openai-acquiring-astra...

(I checked your website because I wanted to see if you had written something about trivy/litellm as well, I highly recommend checking out what has happened within litellm space if possible as I would love to read your thoughts on it)

Have a nice day simon!

Edit: now the website works but I am not sure what had gone wrong previously, (an issue from heroku maybe?) as its working now

Edit-2: after the website working, I am able to see that you have already made a post about it.

The lack of a token rate metric for the kimi example is disappointing.

The latter link says they get ~1.7 tok/s which is quite impressive for a near-SOTA local model running on ordinary hardware.

Thanks for sharing this! If you'd be interested in running the benchmark yourself with Hypura I'd happily merge into our stats. Otherwise will add to my todo list :)

The Qwen 3.5 MoE local performance numbers are striking but the cloud pricing picture for the same models is equally interesting. On inference platforms right now Qwen class models are running significantly cheaper than closed equivalents, open source has about an 81% pricing advantage on equivalent platforms. The local vs cloud crossover math gets genuinely interesting for MoE models because the sparse expert loading that makes Hypura useful locally is the same property that makes cloud inference cheaper per token. Worth knowing both sides of that equation when deciding where to run.