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- Hacker News
- The question is whether Cerebras is available... I've been trying to get https://www.cerebras.ai/code for at least 1 year now. It's all sold out. Always. I once joined their Discord, waited for the drop, and it all sold out in seconds. I haven't had enough time to put my card details. Somebody recommended that I should put my card details in advance, lol.
The next time I hear about them I am laughing, because when I could enjoy these powers? How many years I should be sitting in a waitlist...
by RomanPushkin - I have used their gemma 4 31b model through kagi and getting real instantaneous answers is absolutely crazy. A very different feeling and UX. Even if the model is smaller, there is definitely a use case for these. I was wondering if they would put the qwen 27b model, it sounds very interesting to try.by freehorse
- May I ask what you used Gemma 31B for? Last time I try it wasn't bad but then it wasn't particularly good either.by blaesus
- Really an aside, but yesterday I got the Gemma-4-12b (128k context) to build it's first web app in the minimal Dark Software Factory I've been building for myself.
https://joeldare.com/a-local-open-weight-model-builds-its-fi...
by codazoda - The thing I didn’t realize for a while is 27B is rather smart. As many (or more) activated parameters as the flash models of the universe that we know about. It reasons very well. It just doesn’t have a lot of knowledge.by bitexploder
- Noticed they are present in OpenRouter, but Qwen 3.8 is not there yet. Hopefully it'll get there soon.
For those who haven't noticed though, the context size they allow for Qwen is just 128k. Still interesting as a specialized sub-agent but not really well suited for long tasks.
by tacone - Great observation. That’s not enough context even for some one shot xhigh requests.
When I put Qwen3.8 27B xhigh towards adding scope proxying to the Guice library, it one shotted a great impl using 250k context before stopping.
Part of the greatness of the model is that it just keeps going until it gets a great result. 128k context is disappointing.
by srcreigh - 128k is effectively useless on even trivial toy size "not for real business use" coding projects, by the time you reach 105k to 115k tokens with loading code into context and new research/tasks, and ask it to modify something, it'll be vacating older stuff out of context and forgetting the whole picture of what you're working on.by walrus01
- I really wish they had their customer support somewhere else than Discord, which seems to think I'm a bot and doesen't accept my email or phone numbeby foundfontic
- discord support can fix such issues
- I'm saddened that Gemma4 is replaced by Qwen 3.8 on PayGo plan. Gemma4 31B is not coding model but it is excellent at intent understanding and task execution used in agentic software. This just shows that real world dominant usage for llms so far is to code generate. And not to augment business products. They must had barely anyone using Gemma to remove it from that tier.by dshat
- I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is likely one of the strongest models they've hosted so far.
Edit: it looks like this is only available on a API token pricing. Does anyone know if they have rolled out prompt caching yet? It used to get pretty expensive for agentic coding tasks with no prompt caching.
by gardnr - Agreed, but in our SAAS I can tell some UX will sky-rocket to next level with thisby altertable
- The coding plan is gone now right?by singpolyma3
- Strongest model that they host on the public endpoint. They do a super fast version of GPT 5.6 Sol for OpenAI and have bigger open models on dedicated endpoints.by eli
- i believe they used to have monthly plan, what happened to that?by cute_boi
- It appears that they do support Prompt Caching: https://inference-docs.cerebras.ai/capabilities/prompt-cachi...by jasongill
- Just tried it on a medium size coding/debug problem on an existing codebase, observations: - Input doesn't look faster than other models, it spends a lot of time reading Read about 5M tokens - Output is awesome, super fast as you expect from the 1500t/sec I think that's correct - Tool call is failing more than say DS4, which leads to time wasted on retries (complex tools like browser control for example) - Shell commands are still somewhat of a bottleneck
The net effect is that I spend about the same time waiting, and I still need to read that output so, at least for coding, it actually reconciles me with the 100-200t/sec you can get on DS4 or the like. Maybe that's a good sweet spot after all and faster t/sec is not where the bottleneck is.
Also maybe my setup (OMP) doesn't do the cache correctly but that's a huge cost driver... so atm it's quite pricy
by hexa00 - I can't believe this situation has not improved in years. Is cerebras' main business selling the hardware, then?
- > "Also maybe my setup (OMP) doesn't do the cache correctly but that's a huge cost driver... so atm it's quite pricy"
I don't believe Cerebras has a cached input pricing? They don't list one on the model page:
https://inference-docs.cerebras.ai/models/qwen-3.8-27b
edit: See the sibling discussion,
https://news.ycombinator.com/item?id=49554520#49555094 ("Input tokens, whether served from the cache or processed fresh, are billed at the standard input token rate")
by peri-cl - I just did a little anecdotal test. Had pi + cerebras review a recent commit and asked a few quick followups on it. Worked great.
The Cerebras session cost me $1.60 and took a total of 5.1 mins. I did get a few brief 429 rate limit errors in there. The p50 speed was 890 tok/s and 0.64s TTFT.
Using OpenRouter averages, that would've cost $0.29 (no cache discount at Cerebras!) and would've taken about 14.4 minutes.
So on this one short session, cerebras was 5.6x more expensive in exchange for being 2.8x faster. Or, another way, $1.32 buys back about 9 minutes of your time. Not a bad trade IMHO but the cache situation is a real bummer. The longer your session the more relatively expensive Cerebras gets. The "good" news is you're also limited by its short context window.
(Also, I used to be on the Cerebras coding plan and the support is pretty bad for end users. My guess is these public endpoints are really just product demos for potential enterprise customers.)
by eli - Thanks! Is there something about their platform that prevents caching? Or are they just not passing on the discount?
- It would be great if they made their inference capacity for this model available via OpenRouter; the fastest provider on OpenRouter right now is at ~80tps https://openrouter.ai/qwen/qwen3.8-27b#providers
They do appear to host other models on OpenRouter so maybe Qwen3.8 will be there soon: https://openrouter.ai/provider/cerebras
by jasongill - We're serving it around 150-200tok/s (uses our new speculative decoding implementation on a DFlash2 draft model).
https://mixlayer.com, LAUNCH-Q38-27B gets you $5 in credits if you want to kick the tires.
by zackangelo - Just a couple days ago I learned about ninfer (https://github.com/Neroued/ninfer) and on RTX 5090 I can now get ~200 tok/s and over 400 tok/s on concurrent requests which is plenty fast for a local model of this strength.by pllbnk
- dang only for certain nvidia GPUs, had my hopes upby jakswa
- can't second ninfer enough. amazing techby beastman82
- Is there an equivalent but for 4090s?by aizk
- Ok, I need to try that. I'm getting 45tok/s with vLLM on my 6000. >600tok/s concurrent, but 45tok/s single request.
- I was wondering whether this was any good for programming, but it is too fast for its own good. There is a limit of 450,000 tokens per minute. I hit this limit in about 90 seconds and burned through $1.10 while doing so. This is because cached tokens count towards the token limit.
For comparison, I ran the same task with DeepSeek-V4-Flash, which finished in 172 seconds and cost $0.024 with a final context window size of 55217 tokens, while Qwen3.8-27B was not even close to being done with a 64178 context window.
This is a very efficient way to burn your money, but I would not recommend it for programming.
On the positive side, I got a $5 signup bonus, so it wasn't my own money.
by gpugreg