Even trying it out, it seems like our account has gotten moved to some limbo where we can no longer add billing information.
``` Billing access restricted Self-serve billing is not available on Enterprise accounts. Please contact your team for further questions. ```
We have no team (they removed themself from our slack channel after we talked about rate limits). Perplexingly, none of this even shows up in the request, which gives:
``` {"message":"Model does not exist or you do not have access to it.","type":"not_found_error","param":"model","code":"model_not_found"} ```
When the error is really about billing.
I always want to like Cerebras, but I get the vibe that as a tokens in tokens out consumer you are not valued at all.
Just the math there... 150k TPM... and 15k TPS means... you can run for 10 seconds every minute?
The basic math boggles the mind.
They do appear to host other models on OpenRouter so maybe Qwen3.8 will be there soon: https://openrouter.ai/provider/cerebras
https://mixlayer.com, LAUNCH-Q38-27B gets you $5 in credits if you want to kick the tires.
The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot faster (we've seen it break 300 tok/s).
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.
I'm confused. If it's 1500t/s, isn't that only 90k per minute? How do you hit a 450k/minute limit?
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
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")
I wonder if they will do that with sol ultrafast!
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.
> There is no additional fee for using prompt caching. Input tokens, whether served from the cache or processed fresh, are billed at the standard input token rate for the respective model.
Well, talk about flipping the narrative.
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.
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.
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, $1.32 buys back about 9 minutes of your time. Not a bad trade IMHO but the cache situation is a real bummer. The longer your session the more relatively expensive Cerebras gets. The "good" news is you're also limited by its short context window.
(Also, I used to be on the Cerebras coding plan and the support is pretty bad for end users. My guess is these public endpoints are really just product demos for potential enterprise customers.)
The CEO was on Gradient Dissent a couple years ago: https://www.youtube.com/watch?v=qNXebAQ6igs
[1] https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultraf...
It would look bad for cerebras if other people are hosting the 27b version and show a higher TPS than cerebras.
Sure, 4x input , but cheaper output. Though Cerebras doesn't have prompt caching, so not great for agentic workloads. (they do, but it doesn't affect the price.
Most of the cost for agentic coding is input tokens, you pay for the whole context at each tool call or message. Output tokens is just a small rate
Having the choice is good as you can make a trade-off between speed, perf, and quality.
Until last year, people had a single AI god they believed in (mostly Anthropic stuff). Now we have power to make choices (open-weights, SOTA, speed-optimized, etc) the same way you do for system designs.
But I burn my 5$ allowance in 10 minutes ... and only because I was hitting rate limits, without it would probably be less than a minute.
Hope they add such models to Code too :)