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Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

cerebras.ai

61–70 of 135 posts

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#61
post #28

AI moves so fast that Vibe Coding still has a negative stigma attached to it, but even after 25 years of development, I'm not able to match the productivity of getting AI to implement the features I want. It's basically getting multiple devs to set out and go do work for you where you just tell them what you want and provide iterative feedback till they implement all the features you want, in the way you want and to…

We need a new term for LLMs actually solving a hard problems. When I help Claude Code solve a nasty bug it doesn’t feel like “vibing” as in “I tell the model what I want the website to look like”. It feels like sniping as in “I spot for Claude Code, telling how to adjust for wind, range, and elevation so it can hit my far away target”.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#62
post #58
post #28

AI moves so fast that Vibe Coding still has a negative stigma attached to it, but even after 25 years of development, I'm not able to match the productivity of getting AI to implement the features I want. It's basically getting multiple devs to set out and go do work for you where you just tell them what you want and provide iterative feedback till they implement all the features you want, in the way you want and to…

The industry of "software" is so large... While I agree with web development going this route, I'm not sure about "everything else". You could argue that that's the bulk of all software jobs in tech, and you'd likely be correct... But depending on what your actual challenge is, LLM assistance is more of a hindrance then help. However creating a web platform without external constraints makes LLM assistance shine, tha…

Well, there are certainly kinds of code LLMs would struggle with, but people generally underestimate what LLMs are capable of.

E.g. Victor Taelin is implementing ultra-advanced programming language/runtime writing almost all code using LLM now. Runtime (HVM) is based on Interaction Calculus model which was only an obscure academic curiosity until Taelin started working on it. So a hypothesis that LLMs are only capable of copying bits of code from Stack Overflow shall be dismissed.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#63
post #38

I find the fast models good for rapidly iterating UI changes with voice chat. Like "add some padding above the text box" or "right align the button". But I find the fast models useless for deep coding work. But a fast model has its place. Not $50/month though. Cursor has Compose 1 and Grok Code Fast for free. Not sure what $50/month gets me that those don't. I liked the stealth supernova model a lot too.

Composer and grok fast are not free.

grok-code-fast-1 is free (for a limited time) in opencode zen, has been for a while. (Originally it was billed as a test/to gather training data for xAI.) But right now GLM 4.6 is also temporarily free there (hosted by opencode themselves; they call it "big pickle", and there's no data collection), has been for weeks, and GLM 4.6 is far better (better than Haiku and not very far off Sonnet), and still very fast, so I have no use for gcf1 anymore.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#64
post #60
post #17

Was able to sign up for the Max plan & start using it via opencode. It does a way better job than Qwen3 Coder in my opinion. Still extremely fast, but in less than 1 hour I was able to use 7M input tokens, so with a single agent running I would be able easily to pass that 120M daily token limit. The speed difference between Claude Code is significant though - to the point where I'm not waiting for generation most of…

They don't have prefix caching? Claude and Codex have this.

At those speeds, it's probably impossible. It would require enormous amounts of memory (which the chip simply doesn't have, there's no room for it) or rather a lot of bandwidth off-chip to storage, and again they wouldn't want to waste surface area on the wiring. Bit of a drawback of increasing density.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#65
post #52
post #37

Earlier quoted context omitted.

It just depends on what you are doing. A green field react app in typescript with a CRUD API behind? The LLMs are a mind blowing assistant and 1000t/s is crazy. You are doing embedded development or anything else not as mainstream as web dev? LLMs are still useful but no longer mind blowing and often produce hallucinations. You need to read every line of their output. 1000t/s is crazy but no longer always in a good w…

> You are doing embedded development or anything else not as mainstream as web dev? LLMs are still useful but no longer mind blowing and often produce hallucinations. I experienced this with Claude 4 Sonnet and, to some extent, gpt-5-mini-high. When able to run tests against its output, Claude produces pretty good Rust backend and TypeScript frontend code. However, Claude became borderline unproductive once I started…

This is where Rust's "if it compiles, it's probably correct" philosophy may come in handy.

"Shifting bugs left" is even more important for LLMs than it is for humans. There are certain tests LLMs can't run, so if we can detect bugs at compile time and run the LLM in a loop until things compile, that's a significant benefit.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#66
post #28

AI moves so fast that Vibe Coding still has a negative stigma attached to it, but even after 25 years of development, I'm not able to match the productivity of getting AI to implement the features I want. It's basically getting multiple devs to set out and go do work for you where you just tell them what you want and provide iterative feedback till they implement all the features you want, in the way you want and to…

We need a new term for LLMs actually solving a hard problems. When I help Claude Code solve a nasty bug it doesn’t feel like “vibing” as in “I tell the model what I want the website to look like”. It feels like sniping as in “I spot for Claude Code, telling how to adjust for wind, range, and elevation so it can hit my far away target”.

From what I recall of the original Karpathy definition, it’s only “vibe coding” if you aren’t reading the code it produces

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#67
post #66

Earlier quoted context omitted.

We need a new term for LLMs actually solving a hard problems. When I help Claude Code solve a nasty bug it doesn’t feel like “vibing” as in “I tell the model what I want the website to look like”. It feels like sniping as in “I spot for Claude Code, telling how to adjust for wind, range, and elevation so it can hit my far away target”.

From what I recall of the original Karpathy definition, it’s only “vibe coding” if you aren’t reading the code it produces

Yes, I vote for keeping that definition and not throw it all into a box. LLM assisted coding is not vibe coding.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#68
post #4

If they don't quantize the model, how do they achieve these speeds? Groq also says they don't quantize models (and I want to believe them) but we literally have no way to prove they're right. 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…

Groq does quantise. Look at this benchmark from moonshotai for K2 where they compare their official implementation to third party providers.

https://github.com/MoonshotAI/K2-Vendor-Verifier

It's one of the lowest rated on that table.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#69
Here's a customer of the $200 max plan for 2 months. I fell in love with the Qwen3 Coder 480B model, Q3C, that was fast, twice the speed of GLM. GLM 4.6 is just meh, I mean, way faster than competitors, and practically at Sonnet 4.x level in coding and tool use, but not a life-changing difference.

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.

Re: Cerebras Code now supports GLM 4.6 at 1000 tokens/sec

#70
post #4

If they don't quantize the model, how do they achieve these speeds? Groq also says they don't quantize models (and I want to believe them) but we literally have no way to prove they're right. 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…

> but we literally have no way to prove they're right

Of course we do. Just run a benchmark with Cerebras/Groq and compare to the results produced in a trusted environment. If the scores are equal, the model is is either unquantized, or quantized so well that we can not tell, in which case it does not matter.

For example, here is a comparison of different providers for gpt-oss-120b, with differences of over 10 % for best and worst provider.

https://artificialanalysis.ai/models/gpt-oss-120b/providers#...

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