(I have both and haven't run into any limits yet, so I'm probably not a very heavy user.)

I'm quite impressed with the model. I have been using GLM-4.6 in Claude Code instead of Sonnet, and finding it fine for my use cases. (Simple scripting and web stuff.)

(Note: Z.ai's GLM doesn't seem to support web search (fails) or image recognition (hallucinates). To fix that I use Claude Code Router and hooked up those two features to Gemini (free) instead.)

I find that Sonnet produces much nicer code. I often find myself asking Sonnet to clean up GLM's code. More recently, I just got the Pro plan for Claude so I'm mostly just using Sonnet directly now. (Haven't had the rate limits yet but we'll see!)

So in my experience if you're not too fussy you can currently get "80% of Claude Code" for like $3/month, which is pretty nuts.

GLM also works well in Charm Crush, though it seems to be better optimized for Claude Code (I think they might have fine tuned it.)

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I have tested Kimi K2 at 1000 tok/s via OpenRouter, and it's bloody amazing, so I imagine this supercharged GLM will be great too. Alas, $50!

This is clearly the future of Software Development, but the models are so good atm that the future is possible now. I'm still getting used to and having to rethink my entire dev workflow for maximum productivity, and whilst I wouldn't unleash AI Agents on a decade old code base, all my new Web Apps will likely end up being AI-first unless there's a very good reason why it wouldn't provide a net benefit.

I've been using GLM 4.6 on Cerebras for the last week or so, since they began the transition, and I've been blown away.

I'm not a vibe coder; when I use AI coding tools, they're in the hot path. They save me time when whipping up a bash script and I can't remember the exact syntax, or for finding easily falsifiable answers that would otherwise take me a few minutes of reading. But, even though GLM 4.6 is not as smart as Sonnet 4.5, it is smart enough. And because it is so fast on Cerebras, I genuinely feel that it augments my own ability and productivity; the raw speed has considerably shifted the tipping point of time-savings for me.

YMMV, of course. I'm very precise with the instructions I provide. And I'm constantly interleaving my own design choices into the process - I usually have a very clear idea in my mind of what the end result should look like - so, in the end, the code ends up how I would have written it without AI. But building happens much faster.

No affiliation with Cerebras, just a happy customer. Just upgraded to the $200/mo plan - and I'll admit that I was one that scoffed when folks jumped on the original $200/mo Claude plan. I think this particular way of working with LLMs just fits well with how I think and work.

For reference, each new request needs to send all previous messages - tool calls force new requests too. So it's essentially cumulative when you're chatting with an agent - my opencode agent's context window is only 50% used at 72k tokens, but Cerebra's tracking online shows that I've used 1M input tokens and 10k output tokens already.

Fast forward to now, when GLM 4.6 has replaced Qwen3 coder in their subscription plan. My subscription was still active so I wanted to give this setup another shot. This time though, I decided to give Cline a try. I’ve got to say, I was very pleasantly surprised - it worked really well out of the box. I guess whatever Cline does behind the scenes is more conducive to Cerebra’s API. I used Claude 4.5 + Thinking for “Plan” mode and Cerebras/GLM 4.6 for “Act”.

The combo feels solid. Much better than GPT-5 Codex alone. I found codex to be very high quality but so godawful slow for long interactive coding sessions. The worst part is I cannot see what it’s “thinking” to stop it in its tracks when it’s going in the wrong direction.

In an essence, Cerebras + GLM 4.6 feels like Grok Fast 1 on steroids. Just couple it with a frontier + thinking model for planning (Claude 4.5/GPT-5/Gemini Pro 2.5).

One caveat: sometimes the Cerebras API starts choking “because of high demand” which has nothing to do with hitting subscription limits. Just an FYI.

Note: For the record, I was coding on a semi-complex Rust application tuned for low-latency mix of IO + CPU workload. The application is multi-threaded and makes extensive use of locking primitives and explicit reference counting (Arc). All models were able to handle the code really well given the constraints.

Note2: I am also evaluating Synthetic's (synthetic.new) open-source model inference subscription and I like it a lot. There's a large number of models to choose from, including gpt-oss-120 and their usage limits are very very generous. To the point that I don't think I will ever hit them.

Currently on the 50 USD tier, very much worth the money, am kinda considering going for the 200 USD tier, BUT GPT-5 and Sonnet 4.5 and Gemini 2.5 Pro still feel needed occasionally, so it'd be stupid to go for the 200 USD tier and not use it fully and still have to pay up to around 100 USD for tokens in the other models per month. Maybe that will change in the future, when dealing with lots of changes (e.g. needing to make a component showcase of 90 components, but with enough differences between then to make codegen unviable) Cerebras is already invaluable.

Plus the performance actually makes iterating faster, to a degree where I believe that other models should also eventually run this fast. Oddly enough, the other day their 200 USD plan showed as "Sold out", maybe they're scaling up the capacity gradually. I really hope they never axe the Code plans, they literally have no competition for this mode of use. Maybe they'll also have a 100 USD plan some day, one can hope, but maybe offering just the 200 plan is better from an upsell perspective for them.

Oh also, when I spill over my daily 24M limits, please let me use the Pay2Go thing on top of that, if instead of 24M tokens some day I need 40M, I'd pay for those additional ones.

This is important because their premium $50 (as opposed to $20 on Claude Pro or ChatGPT Plus) should be justified by the speed. GLM 4.6 is fine but I don't think it's still at the GPT-5/Claude Sonnet 4.5 level, so if I'm paying $50 for it on Cerebras it should be mainly because of speed.

What kind of workflow justifies this? I'm genuinely curious.

I write what I want, the LLM responds with edits, my 100 lines of Python implement them in the project. It can edit any number of files in one LLM call, which is very nice (and very cheap and fast).

I tested this with Kimi K2 on Groq (also 1000 tok/s) and very impressed.

I want to say this is the best use case for fast models -- that frictionlessness in your workflow -- though it burns tokens pretty fast working like that! (Agentic is even more nuts with how fast it burns tokens on fast models, so the $50 is actually pretty great value.)

It is definitely fun playing with these models at these speeds. The question is just how far from real pricing is 500M tokens for $50?

Either way the LLM usage will grow for some time to come and so will grow energy usage. Good times for renewables and probably fusion and fission.

Selling shovels in a gold rush was always reliable business. Cerebras is only rated at $8.1B as of one month ago. Compared to Nvidia that seems pocket change.

Yes, Qwen3 made more mistakes than GLM, around 15% more in my quick throwaway evals, but it was a more professional model overall, more polished in some aspects, better with international languages, and being non-reasoning, ideal for a lot of tasks through the API that could be ran instantaneously. I think the Qwen line of models is a more consistent offering, with other versions of the model for 32B and VL, now a 80B one, etc. I guess the problem was that Qwen Max was closed source, signalling that Qwen may not have a way forward for Cerebras to evolve. GLM 4.6 covers precisely that hole. Not that Cerebras is a model provider of any kind, their service levels are "buggy" (right now it's been down for 1h and probably won't be fixed until California wakes up at 9am PST). So it does feel like we are not the customers, but the product, a marketing stunt for them to get visibility for their tech.

GLM feels like they (Z.ai) are just distilling whatever they can get into it. GLM switches to Chinese sometimes, or just cuts off. It does have a bit of more "intelligence" than Q3C, but not enough to say it solves the toughest problems. Regardless, for tough nuts to crack I use my Codex Plus plan.

Ex: In one of my evals, it took 15 turns to solve an issue using Cerebras Q3C. I took 12 turns with GLM, but overall GLM takes 2x the time, so instead of doing a full task from zero-to-commit in say 15 minutes, it takes 24 minutes.

In another eval (Next.js CSS editing), my task with Q3C coder was done in 1:30 minutes. GLM 4.6 took 2:24. The same task in Codex took 5:37 minutes, with maybe 1 or 2 turns. Codex DX is that of working unattended: prompt it and go do something else, there's a good chance it will get it right after 0, 1 or 2 nudges. With CC+Cerebras it's a completely different DX, given the speed it feels just like programming, but super-fast. Prompt, read the change, accept (or don't), accept, accept, accept, test it out, accept, prompt, accept, interrupt, prompt, accept, and 1:30 min later we're done.

Like I said I use Claude Code + a proxy (llmux). The coding agent makes a HUGE difference, and CC is hands-down the best agent out there.

Stalin used to say that in war "quantity has a quality all its own". And I think that in terms of coding agents, speed is quality all its own too.

Maybe not for blind vibe coding, but if you are a developer, and is able to understand the code the agent generates and change it, the fast feedback of fast inference is a game changer. I don't care if claude is better than GLM 4.6, fast iteractions are king for me now.

It is like moving from DSL to gigabit fiber FTTH

I have a lot of questions about how models are run at scale; so curious to know more. With such a massive wafer as chip as Cerebras, it feels like perhaps switching might be even more consuming. Or maybe there's some brilliant strategy to have multiple contexts all loaded that it can flip between! Inventorying & using so much ram so spread out is it's own challenge!