> On March 26–27, 2026, customers experienced elevated error rates when using Claude Opus 4.6 and Claude Sonnet 4.6. The issue was caused by a networking performance degradation within our cloud infrastructure that disrupted communication between components of our serving stack. We resolved the incident by migrating the affected workloads to healthy infrastructure, restoring normal service by 9:30 AM PT on March 27.
Not one of the usual ones that has service problems :)
I personally prefer per-token, it makes you more thoughtful about your setup and usage, instead of spray and pray.
You can also access the notable open weight models with VertexAI, only need to change the model id string.
However, from a game theory perspective, when there's a subscription, the model makers are incentivized to maximize problem solving in the minimum amount of tokens. With per-token pricing, the incentive is to maximize problem solving while increasing token usage.
I do agree that Big Ai has misaligned incentives with users, generally speaking. This is why I per-token with a custom agent stack.
I suspect the game theoretic aspects come into play more with the quantizing. I have not (anecdotally) experienced this in my API based, per-token usage. I.e. I'm getting what I pay for.
Any tips?
Very few cases these days.. feels like we are lucky to get 2 9s anymore.
Have you noticed any change in that trend in the past year or two, or is it continuing to get better?
We are working on some big improvements to the backend and should have some cool stuff to share later this year :)
Tired of all the people online with anxiety who project their own personal issues by spamming this kind of doomer posts.
It should be low risk to offer such guarantees then.
You just won't like the price.
- Stalin probably
time to give your devops guy his job back.
They're in serious risk of losing their lead with this sort of performance.
God, I wish this inane bullshit would just fucking die already.
Models are not "degrading". They're not being "secretly quantized". And no one is swapping out your 1.2T frontier behemoth for a cheap 120B toy and hoping you wouldn't notice!
It's just that humans are completely full of shit, and can't be trusted to measure LLM performance objectively!
Every time you use an LLM, you learn its capability profile better. You start using it more aggressively at what it's "good" at, until you find the limits and expose the flaws. You start paying attention to the more subtle issues you overlooked at first. Your honeymoon period wears off and you see that "the model got dumber". It didn't. You got better at pushing it to its limits, exposing the ways in which it was always dumb.
Now, will the likes of Anthropic just "API error: overloaded" you on any day of the week that ends in Y? Will they reduce your usage quotas and hope that you don't notice because they never gave you a number anyway? Oh, definitely. But that "they're making the models WORSE" bullshit lives in people's heads way more than in any reality.
I've seen Sonnet consistently start hallucinating on the exact same inputs for a couple hours, and then just go back to normal like nothing ever happened. It may just be a combination of hardware malfunction + session pinning. But at the end of the day the effects are indistinguishable from "secret quantizing".
Nobody goes there anymore, it's too crowded.
https://www.reddit.com/r/GeminiCLI/comments/1s49pag/this_is_...
> this sort of performance
only people who do not even look at code anymore need anything more than that.
Anthropic has had more than that.
Yikes.
They are the best.
ChatGPT is walmart.
Gemini is kroger.
Claude is... idk your local grocer that is always amazing and costs more?
GPT4.5 + COT would have been the best, but OpenAI got cheap.
Sometimes Claude wants more lunch breaks, takes a half day and leaves the desk early just like any human would. (since AI boosters like comparing LLMs to humans all the time) /s
They are going in the "Claude is alive" direction already and that line of communication is likely going full throttle in the nearby future.
I think the big reveal is going to be that frontier models are no better than the open source models that you could feasibly run on retail hardware however they have a highly complex harness behind the API where the magic is.
It went a bit too deep into the role-playing bit.