Agreed, I use Z.ai and the usage is fantastic the only temper that recommendation that it's often unreliable. Perhaps a few times per week it's unresponsive. Maybe more often it seems to become flakey.

It's very variable though recently I'm noticing it's more reliable but there was a patch where it was nearly unusable some days.

I guess I won't complain for the price and YMMV.

If you're ok with a model provider that goes down all the time and has such a poor inference engine setup that once you get past 50k tokens you're going to get stuck in endless reasoning loops.

That's only good for the web based UI. If you want Gemini API access which is what this article is about then you must go the AIStudio route and pricing is API usage based. It does have a free usage tier and new signups can get $300 in free credits for the paid tier so it's I think it's still a good deal, just not as good as using the subscriptions would be.

I bought one of the google AI packages that came with a pile of drive storage and Gemini access.

Unfortunately gemini as a coding agent is a steaming useless pile. They have no right selling it, cheap open weight Chinese models are better at this point.

It's not stupid it just is incompetent at tool use and makes bad mistakes. It constantly gets itself into weird dysfunctional loops when doing basic things like editing files.

I'm not sure what GOOG employees are using internally, but I hope they're not being saddled with Gemini 3.1. It's miles behind.

Good luck sticking within limits, I have been burning up my baseline limits insanely fast within a few prompts, a marked change from a few weeks ago.

There's a few complaints online about the same happening to multiple users.

Otherwise anti-gravity has been great.

I feel they will go token base at some point, currently if you only use it with precise prompts and not random suggestions, switch between models 5.4 and 5.4 mini depending on the work, it is the best deal.

Yeah, it's really good. Probably going to be the next best deal until they cut back.

I need to try the command line version.

I'm developing software in this area right now, so I try a lot of the new models. They're not even close for coding tasks. It basically comes down to 26b parameters vs 1T parameters / quantisation / smaller context sizs, there's no comparison. However, for agentic work, tool calling, text summarisation, local LLMs can be quite capable. Workloads that run as background tasks where you're not concerned about TTFB, cold starts, tok/s etc., this is where local AI is useful.

If you have an M processor then I would recommend that you ditch Ollama because it performs slowly. We get double or triple tok/s using omlx or vmlx, respectively, but vmlx doesn't have extensive support for some models like gpt-oss.

I recently experimented creating a Python library from scratch with Codex. After I was done, I took the PRD and Task list that was generated and fed them to opencode with Qwen 3.5 running locally.

Opencode was able to create the library as well. It just took about 2x longer.