OpenRouter or Fireworks are two of many that allow you to run numerous different models. You can use open code harnesses like Pi or Opencode that connect easily to these services.

https://openrouter.ai/

https://fireworks.ai/

http://pi.dev/

https://opencode.ai/

A lot of companies are starting to run towards open models on walled off infra. There might be a moat to be had in really good infra-as-a-service for hosting models, but there’s zero moat to be had in the models themselves. Which is a problem for those trying to convince people a proprietary model company should be worth trillions.

Opencode go for 10$ a month is fantastic. Just use it with Glm5.2-flash.

Openrouter is not ideal, because you don't know who they send your traffic to and there are rumours of vendors cheating by providing quantized models.

I'm very happy with Deepinfra. Less model coverage, but good prices and quite fast.

However I have to caution you about one thing.

No one will give you as many input tokens for so little money as Claude Max x5 (maybe x20 too, I use x5).

I tend to use 1.1B to 1.4B a week about 0.8-1B cached. Even with cache were talking thousands of $ in API prices a week. Hundreds if we're talking cheap cloud like Deepinfra.

However, local AI well setup is actually a good alternative for this if Claude Max was unavailable.

For example my system a ryzen 7950x 192GB ram, 5x rtx3090 plus an rtx5060 ti 16gb. (3 rtx3090 cards via usb4 egpu dock). Let's me run Qwen3.8-Flash-Next with 3slots (no rtx5060 used) at 55tok/s decode dropping to 50 at the end of a 260k context, 1200tok/s refill dropping to 950 at the end of context.

With RAM and ssd cashing and 80% cache were talking on the order of 4B a week could be ingested by this setup (roughly) if it was running 24/7. I found 6 interactive cloud code sessions are fairly pleasant with this 3 user setup.

If I include the rtx5060 in the mix I can bump to 5 users, but it slows down by about 15% (note the speeds are give are for one active user, multiple users at once see maybe 70% of tgat per user so aggregate is much higher in multi user setup).

So in theory I should be able to run 10 cloud code sessions. Although I'm testing CC alternative now (pi with own plugins) because this model, while multimodal has only 260k context 30k of which CC eats on the getgo.

Many people say local AI makes no sense financially. But in the event you process huge inputs that are often cached it does make sense.

Nobody thinks a distillation crackdown is a long term plan, but it does delay Chinese labs for the couple years while they are compute-starved. Of course they could make competitive models if they were drowning in NVIDIA chips but they aren't, and the native ones aren't at scale production for at least a year.