I regularly hit 200-300M cached reads every day on some of the models I use. It has exceeded 7-800M on a couple of occasions. At $0.04/M, that is $8-12 per day only for cached reads.
I have used Kimi 2.5 and GLM 5.3 (& 5.3 Flash). Do not need them for what I do outside of spec hardening (basically, a lot of chatting).
I tend to know exactly what I want and most of the weaker models are enough to get me there. I have mainly been using MiMo, DeepSeek V4 Flash and MuseSpark Contributor over the last month or so.
Without any Pokémon-specific optimization, Step 5 Preview has so far sustained progress for more than 3,000 turns and 6 million tokens of interaction. By turn 3,082, it had unlocked Cut, earned three Gym Badges, and defeated Lt. Surge. The run is now roughly one-third of the way through the main story.
Finally FireRed is being used as a benchmark again! I believe Astra can beat it in 18 hours. Not sure how that compares.
> Built on a sparse Mixture-of-Experts architecture, Step 5 Preview has 600B total parameters, with 27B active per token, and supports a 1M-token context window and vision input.
> Step 5 Preview scores 44 on the Artificial Analysis Intelligence Index.
> The model will be released with open weights on October 15.
I guess being Chinese company they decided to skip version 4, while also giving impression to be on the similar iteration with leading companies (claude opus 5).
I wonder if other Chinese labs like Kimi/Moonshot will follow suit.
Parent commenter hinted at that. Yet DeepSeek has released their V4 which was hugely successful, and even their new architecture is marked V4.1. Qwen internals mark their Flash-Next model, also very compelling, as "qwen4exp". So both of them are bucking the negative stereotype.
Their posisitoning is nice. Instead of saying they are cheaper and a bit less performant (in terms of intelligence), they say they are best among the cheaper and a bit less performant ones.
IT's Artificial Analysis Index is the same as Kimi K3, which is about 4.6x bigger, and GLM 5.3, which is about 1.25x bigger. Pricing is $1/$2.70 i/o. Openweights on October 15.
GLM-5.3 and Kimi K3 are just below where I can use them to completely replace frontier models. Oddly,* SWE-2 is there for me.
If this performs similarly in the real world, we're approaching a level of capability where for most devs, it only makes sense to pay for Anthropic or OpenAI subscriptions if they are heavily subsidized and actually cheaper than these alternative options.
* Oddly, because I perceived Devin as being kind of a joke before trying SWE-2.
In their first video, to make a 3D render of the photos, the thinking traces gives away the game:
Interesting! It turns out there's already an existing project here [...] The project is fully built [...]
I'm always astounded how little effort is put into the details of these announcements. Back when I paid more attention, I remember OpenAI's and Google's demos having basic facts wrong every time, which is even worse than what Step 5 is doing here.
For anyone else looking for the pricing: https://platform.stepfun.ai/docs/en/guides/pricing/details#p...
I regularly hit 200-300M cached reads every day on some of the models I use. It has exceeded 7-800M on a couple of occasions. At $0.04/M, that is $8-12 per day only for cached reads.
Unless you meant step-3.7-flash, the input cache hits are $0.05 per mil for step-5-preview.
Pretty decent "API" rates for ~500M+ tokens on Step Fun 5, a Kimi K3 / GLM 5.3 level model?
Their "Step Plan" is ridiculous, by comparison: ~$60 usage on $6.99/mo; ~$220 on $9.99/mo. https://platform.stepfun.ai/docs/en/step-plan/overview
Yes, I meant the Flash version.
I have used Kimi 2.5 and GLM 5.3 (& 5.3 Flash). Do not need them for what I do outside of spec hardening (basically, a lot of chatting).
I tend to know exactly what I want and most of the weaker models are enough to get me there. I have mainly been using MiMo, DeepSeek V4 Flash and MuseSpark Contributor over the last month or so.
Huh wonder why they skipped 4?
Sometimes 4 is skipped due to being considered unlucky.
In China and in places influenced by Chinese culture, due to homonymy between "4" and death.
Sounds like death in Chinese.
Do they rename it like in Japanese 7?
Finally FireRed is being used as a benchmark again! I believe Astra can beat it in 18 hours. Not sure how that compares.
I guess being Chinese company they decided to skip version 4, while also giving impression to be on the similar iteration with leading companies (claude opus 5). I wonder if other Chinese labs like Kimi/Moonshot will follow suit.
Moonshot has already teased K3.1 so not likely
K3.1 would likely be a deeper/longer post-train from K3, so that’d make sense.
It’s all marketing anyways, but that’s at least how a lot of labs have been naming things (sometimes).
Another possible reason is that the number 4 is considered unlucky in traditional Chinese culture.
Parent commenter hinted at that. Yet DeepSeek has released their V4 which was hugely successful, and even their new architecture is marked V4.1. Qwen internals mark their Flash-Next model, also very compelling, as "qwen4exp". So both of them are bucking the negative stereotype.
Their posisitoning is nice. Instead of saying they are cheaper and a bit less performant (in terms of intelligence), they say they are best among the cheaper and a bit less performant ones.
How about adding a contested historical facts benchmark?
IT's Artificial Analysis Index is the same as Kimi K3, which is about 4.6x bigger, and GLM 5.3, which is about 1.25x bigger. Pricing is $1/$2.70 i/o. Openweights on October 15.
GLM-5.3 and Kimi K3 are just below where I can use them to completely replace frontier models. Oddly,* SWE-2 is there for me.
If this performs similarly in the real world, we're approaching a level of capability where for most devs, it only makes sense to pay for Anthropic or OpenAI subscriptions if they are heavily subsidized and actually cheaper than these alternative options.
* Oddly, because I perceived Devin as being kind of a joke before trying SWE-2.
In their first video, to make a 3D render of the photos, the thinking traces gives away the game:
I'm always astounded how little effort is put into the details of these announcements. Back when I paid more attention, I remember OpenAI's and Google's demos having basic facts wrong every time, which is even worse than what Step 5 is doing here.