6-luna is no improvement over 5.6, merely a price cut.
And info from the help page with message limits suggests the 50% price cut does not apply to the subscription, where they applied only a 1/3 price cut instead.
I'm not thrilled with this release.
Opus 5.5, which matches GPT-6 Astra performance at a cheaper price, is much more interesting.
You're right, it's probably quite unfair of me to say it eats lots of tokens when I am paying double for claude than codex and complaining about tokens.
The rest still stands, though.
But if I've learned anything is that in a 2 months I might have completely turned around, who knows
>Opus 5.5 has classifiers similar to Fable models for a small set of capabilities related to the development of frontier LLMs, such as kernel development for certain ML accelerators. They shouldn't impact the vast majority of traditional AI or ML development, research, or general coding. These classifiers cause Claude to fall back from Opus 5.5 to Opus 5.
But hey, they 'should not impact the vast majority' of ML development. Great.
The UI/UX seems impressively bad. DeepSWE's cost curve has a better, more obvious way to sort by only the top level of reasoning to avoid 80% of the graph just being the same 3-5 models at their 8 different reasoning levels...
It's also less clear what a lot of their metrics mean. Does Cost per Task include only things that can be verified to work and passed? As best I can tell, it does not.
I'm less concerned if one model's cost per task is $0.10 and another model's cost is $1.50 if the $0.10 task got it right 1% of the time and the $1.50 model got it right 66% of the time.
An equalized / weighted cost/time per task is much more valuable - being massively penalized for taking a lot of time and ultimately not passing when OTHER models did pass.
OpenAI decreased prices with the 5.6 model family.
And later they further cut Sol and Terra pricing by 20% (maybe only in the API) and Luna by 80%.
In fact Luna still outperformed DeepSeek Flash 4.1 in cost per task on Artificial Analysis when I last checked.
However, Luna is slightly less intelligent. I have a feeling that it's pretty dumb and prone to hallucination unless running at xhigh or max effort, where it somehow manages to work quite well.
I did not personally test the open weight models beyond the old Qwen 3.6 27B, which produced unusably bad results for me.
The competition is great, and I hope Chinese models will continue to force leading US labs to offer models at a low price point.
That said, I don't think the Chinese labs have anything over OpenAI and Anthropic when it comes to capability or efficiency - I have no reason not to believe the US labs have even lower cost to serve the models.
OpenAI had to cut costs because of Anthropic. I also do not trust the benchmarks when it comes to models anymore. I have tried both Claude and OpenAI models and while it is true that the 5.6 series is smarter than Deepseek (at the time i tested it against 4.0) at that price it is still not worth it and sometimes randomly refuses to do tasks or stops midway etc.
Do also remember China is this far in the AI race despite all chip restrictions from America. If they were in equal standards I truly think Chinese models would have long surpassed American ones. Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling meanwhile their own models claimed to be Qwen¹ and their stance against open models is negative² and they still keep blaming China for it.
> Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling
Why wouldn't he? If there really was 25,000 accounts breaking ToS any CEO would at minimum be upset. Evidence of Claude distilling qwen would be damning but that a) makes no sense b) doesn't exist afaik.
They have the logs, created a report and sent a letter to Congress. Whether you believe it is entirely up to you. Given Chinese firms record on IP theft, it's entirely believable. I don't have any doubts, but I might question how they attribute it to a specific firm.
And they took multiple measures presumably to stop "distillation", such as hiding reasoning steps.
Chinese models kept improving in capability regardless, and are in some ways more impressive than Claude/ChatGPT.
So yeah, I think they are bulshitters. The can create reports and send letter to congress simply because they know if allowed to compete freely the Chinese models will eventually prevail.
Also, very rich of you to mention Chinese firms record on IP theft when Anthropic and OpenAI are companies entirely built on large scale IP theft.
> If they were in equal standards I truly think Chinese models would have long surpassed American ones.
Limitations often lead to creativity to overcome them. The Chinese AI labs have had to focus much more on efficiency so they got good at it. Meanwhile breaking new ground is often harder than replicating it. So even if they had matching compute it's not a given they'd be better.
So first it’s “Chinese companies cut costs, and you’d never see American companies do that”, and then when it’s pointed out that one of the leading American labs literally just did that, it’s “yeah, but they had to because of competition”.
What do you think is motivating the Chinese labs, benevolence?
> I did not personally test the open weight models beyond the old Qwen 3.6 27B, which produced unusably bad results for me.
So you don't have much perspective on things, it seems. Let me introduce you to the GLM 5.2 and then 5.3/5.3 flash series of... "oh, wow, I should have bought some RTX PRO 6000's while they were 'cheap'" stage of progression.
As someone carrying multiple max subscriptions to both claude and codex - primary workhorse is glm 5.3 flash running on rented GPUs for less than a latte/hr.
I also found qwen 3.6 27B nearly useless for my own needs. DS4 flash 0731 and then 4.1 have been nearly as eye opening as glm 5.3 flash, but have their own warts.
Have you tried Qwen 3.8 Flash Next? You can run it on one spark with reasonable context sizes at about 30 tps, and it's as good as DS Flash 0731. Maybe even a tie with GLM 5.3, though like everything it depends on the use case.
I’m having the same issue. Hold max subscriptions on both frontier labs but I’ve been forced to use open source models because token limits are not what they used to be. So I end up using Astra and Fable for reviewing, and open source models for implementing.
Yeah, for better or worse, writing style is practically uncorrelated with agentic performance, which is all the rage right now and the thing that most popular benchmarks currently prioritize.
I think we are still far from nailing down good LLM benchmarks, because the more general-purpose your software the harder the question of what makes it good becomes. Is Python a good programming language? Is Java? Is C? I think it's a similar class of problem. You can benchmark rudimentary things like execution speed similar to how you can benchmark tokens/second, but these metrics don't tell the whole story.
We're past the one model fits them all kind of LLM. Most of the recent release actually regress on world knowledge for example, but optimize for something different: tool usage, thinking process, and agentic approach. And yes, in my own usage, some usecases Opus beats Fable.
Yeah it’s a shame a lot of these benchmarks are behind. My favourite was ‘SlopCodeBench’ [1] as I’m most interested in ai reinforcing its own bad decisions, but it’s not even up to current gen oai
They match my experience. Astra and Fable I rate below Sonnet. They are incredibly poor. They were excellent for a couple of days after release and then plummeted.
Maybe I am being routed to more quantised versions or less capable models with system prompt to fake Astra or Fable.
And info from the help page with message limits suggests the 50% price cut does not apply to the subscription, where they applied only a 1/3 price cut instead.
I'm not thrilled with this release.
Opus 5.5, which matches GPT-6 Astra performance at a cheaper price, is much more interesting.
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