Fast AI seems genuinely exciting and somewhat unsettling to me. Right now Claude is faster than me on some tasks but we’re at least close. I have a prompt to clean up a PR that’s been running for 1h now and I expect it to take another few. It’s hard to imagine how the workflow would look like if it was near-instant. On the one hand, it might be easier to focus. Some prompts take so long that I start to multitask and regret it later. On the other, AI that takes a few seconds to max few minutes to solve what used to take hours or days? That’s a game changer and I don’t even know where we fit in.
not OP but usually for me this means long verification loop; waiting 10min on CI checks, that kind of thing, rather than actual 1hr wall clock of token generation
This is very dystopian in my opinion. I'm not the arms, legs, sensors and actuators for a machine super intelligence. I wouldn't treat another human as my slave because they aren't as intelligent as I am any more than I would expect to become a slave for a machine. This is our world (for now) and that is why we fit in. Not because we can serve.
I'm using Deepseek-v4-pro as my main model and this is sometimes pretty annoying, I have to do some easy boring task, think "I'll just leave the agent to do it and go take a nap", but it's already done writing the code before I even walk away from the computer
Do you mean Flash and not Pro? I haven't tried it personally, but according to OpenRouter, the fastest DeekSeep V4 Pro providers are only ~50tps. That's slower than Claude Opus.
These price and speed optimization from Chinese providers, combined with the raising prices from American ones will change the game sooner than later. Many companies are finding issues with the AI bills already.
I wonder what are the economics driving these pricing decisions? Are the Chinese companies just subsidizing their models to a greater degree than the US, or is this an emergent property of energy policy between countries?
Throwing out another factor: Chinese companies have been banned and/or limited from buying nvidia, and turned to local companies for their hardware. I haven't actually seen pricing/benchmarks comparing Chinese AI accelerators, but it wouldn't surprise me if that also worked out in their favor as well.
Lower cost of labor, lots of under the hood optimizations (e.g. cache hits for DS), many of these companies have existing infra (fewer upfront costs for deployment), etc
I see bigger problem with model inconsistency. You never know whether Anthropic will route your request to a cheaper model for the price of Opus. So you can never estimate how much a task will cost, because you might have to restart several times and pay for each attempt. Then you have to prompt models to gauge whether they are real or impostors which also adds to token usage.
i've a Github copilot yearly subscription. Microsoft recently changed their billing to based on token. i'm still getting billed per premium request but GPT 5.4 is now 6x compare to 1x before.
Another problem is that US models are all closed source, and if you're a large corporate you may not want your org to be held hostage by OpenAI / Anthropic.
I genuinely don't understand what moat these US model labs have. If they're saying recursive self improvement is just around the corner and Chinese labs are only slightly behind the leading US models, what moat does the US labs have? Are the US models going to recursively self improve better than the Chinese open source ones or something?
I might be completely wrong about this, but if I had money in OpenAI or Anthropic I'd be pulling it all right now. I think the chance of them going to near-zero over the next few years is very significant.
It is another thing the the BigLabs accuse open weight models of benefitting from distillation & other techniques & essentially avoid higher training costs (which typically bleed into bills end users pay for inference).
True, but why would end users care about that? If anything, training on synthetic AI output is more ethical than on scraped human works (of course, not to say the Chinese labs aren't doing the latter)
I may sound like a shill, but exponential growth and all. We are going to get near instant software from prompt, multiple ones and then choose the best one.
Discussions about choosing a library with the best syntactic sugar method naming is just as crazy as suggesting we type in assembly.
But I think the eventual goal is that documentations won't even be needed. LLM should just itself understand the nuances of frameworks by analyzing their codebase.
> when a new frontend framework came out every 3 months.
> No one cares anymore.
I never cared about this.
I think this captures something that I've been searching for the words for. (Maybe I should have gotten an LLM to write the words for me.) The biggest AI boosters are the kind of dev that would have cared about the new frameworks of the last 3 months. They had a "the framework does all the thinking for me" attitude already, so it is easy for AI to slot into that.
Sounds like exponential growth of crappy software. I'm not saying that before we didn't have mass produced crap in SE, but now it will turn into explosive overflow.
We are living in a ZIRP-like era where builders at the fastest pace layer have misattributed their velocity to exponential gains in model capability. In fact, they are surfing on decades of careful effort to build a robust foundation of highly reusable software libraries.
This strategy will seem to work really well until the economy that enabled that foundation to form is hollowed out. Then, there will be a reckoning (but we will have no choice but to march forth from there).
> This strategy will seem to work really well until the economy that enabled that foundation to form is hollowed out. Then, there will be a reckoning (but we will have no choice but to march forth from there).
There will only be a reckoning if models don't get much better.
If they do get much better you can just have them refactor, fix bugs in, or replace the existing codebase.
The concept of tech debt is sort of meaningless if you anticipate intelligence gains in models to continue.
It's not just software libraries. Specs, applications (the browser!), expectations, device integrations, operating systems, etc. So much that starting from scratch seems impossible.
I'm not agreeing or disagreeing with you, but my brain cannot comprehend how machines can advance such interconnected systems while keeping humans in focus.
Perhaps I shouldn't have watched the Animatrix again.
"exponential growth of crappy X" applies to every industry that went from being an artisanal craft to being mass produced with little or no human input. and we live much better lives than we did before the industrial revolution.
I am more and more inclined into not believing this crappy software theory.
Especially as teams invest in proper agentic harnessing.
We have had a champion in our team that has invested a lot of time into it over the last 4 months, and if anything, quality has improved, not decreased. Architecture is more coherent, codebase has been cleaned up, agents find information quickly, code produced is very solid and my role is more and more checking that the output meets the requirements. But I cannot confidently say that I would've done a better job than AI more often than not I have to admit it does a better job than mine.
The mistakes are less and less technical and merely in the domain mapping. And AI is still not creative as I am for finding solutions quickly to unlock stakeholders' issues. Also, AI is still not creative as I am for finding the proper solutions for advanced technical problems. But it does a better job than me, even on that front, one shotting few solutions in a fraction of a time it would've taken me to test one idea myself.
Mind you, I don't like AI and I think it ruined the job, I don't like working this way, it's exhausting, way more work on one side, way less fun and fiddling with technical parts.
And yet, I have the genuine belief that few years from now we'll be cloning open source repositories that are already optimized/harnessed and tested for agentic loops and best practices left and right with software engineers mostly overseeing the domain translation and putting their 2 cents on the non-boilerplatey parts of the product (which, in general, are a small part of the surface).
I think that the next years of my career will be mostly spent in setting up and writing the harnessing and domain mapping part. Then I will move to another sector, not because I necessarily believe I won't have a job, but because I want to vomit thinking that's going to be my job.
You won't. Because 80% of the complexity is just "knowing what to build". You will get something that gives you a prototype in 1 min, then you break it, then you get a slightly better prototype one one side, but newly broken in another way, and you're going to repeat over and over.
And for any non-trivial application, the space of possibilities grows so quick that you'll never even be able to _touch_ all the moving parts of the application and verify them.
The models might be so fast that they can autocomplete your prompt before you even finish it, and generate dozens of possible applications before you're even done asking.
And how are you going to determine which is the best?
Going through all the possible combinations of users and usage?
So mostly it shifts the work from generation to validation.
> Discussions about choosing a library with the best syntactic sugar method naming is just as crazy as suggesting we type in assembly.
I have a more hopeful take. As AIs improve and get faster we can more quickly and iteratively improve code which we may have historically avoided due to the work involved.
I know i've made several refactors that would have otherwise been insane lifts. Not only because the work involved but because sometimes you don't know if it will work, and so you have a sort of double friction; you don't know if it will even succeed. With an AI you can just throw it at the refactor to see if it runs into a problem all while you're having a coffee break or w/e.
In general AI is going to enable humanity to be more extreme versions of itself. For good and bad. I suspect more bad than good, though.
Neat. The frontier models have gotten pretty impressive, but they're all a bit too slow for interactive, human-in-the-loop coding. It incentivizes vibecoding and running multiple agents in parallel. A fast agent feels more like a partner.
For a while I was running Cerebras GLM 4.7 for a bunch of tasks. Not a very smart model, but it's fantastic to be have a live prototype of a site up and be able to type "make the fonts bigger. No not that big" and see it change in real time. And MiMo 2.5 is a lot more capable than GLM 4.7.
i tried glm 4.7 for agents that write code. simple scripts 200-1000 LOC. extremely bad . Had to abandon cerebras oferning, their smart models are only on enterprise plan.
This will be really powerful for voice. Being able to reason makes LLM so much smarter but with voice your latency budget is so tight that you can't spare the time typically.
MiMo V2.5 Pro (regular speed) remains the strongest open weights agentic coding model we've tested -- it's been interesting to see how little attention it has received relative to some lower performing releases. And the "fast mode" pricing is very competitive here.
Cerebras is trialing Kimi K2.6 at 3000t/s (invite only). I'm excited for when the fast hardware gets more mainstream for frontier models. Models designed for speed on Nvidia are nice addition that could bridge the gap.
I don't understand, given all they say, why this would not be made available to everyone at once? Why the limited release? They should have no trouble scaling it if it runs on a single rack.
Maybe they don't have enough racks. The news indicate that China isn't in a really good situation with GPUs, so probably they want to keep most of them for other stuff. Also because since the price is so cheap they probably want to use the other GPUs for stuff that has higher margins.
Chinese companies are blocked from buying modern ASML lithography machines. The most modern scanner China is still allowed to buy is NXT:1980i from 2015.
I wonder about this too. The other objections miss the point: if it's faster, and otherwise the same, and doesn't require different hardware, then why not just announce that the standard tier of MiMo-v.25-Pro is now ridiculously fast and raise the price? What does "limited high speed resources" mean if it runs on the same hardware as the rest of their pool?
I think the answer is that there's a tradeoff here where additional throughput for a single person can be achieved only by tying up more resources than a normal request would, even when you take into account the fact that the normal request takes longer to finish. I'm not an expert, but some of the optimizations they describe, particularly the parallel prediction stuff, sound like they could take up extra resources.
Assuming they mean 8xA100 or similar, that's some rather insane performance, and at just 3x the cost, it still quite cheap-ish. With some optimisations this might be quite interesting.
I think the margins are getting quite compressed with this one, since it isn't included in token plan and the actual costs increase are much higher than just 3x. But still fairly decent.
Chinese "companies" are not companies in the western sense, but more like government departments with capitalist styling to deceive the western audience.
From that point of view, they have as much money as they need. That's why there is no "VC", because Chinese government assumes that role.
The gated "ultra-speed" phenomenon seen here and with the Cerebras Kimi K2.6 release, while understandable, is somewhat troubling IMO.
Getting ~1000 TPS on near-frontier intelligence is a step change, and enables whole new use-cases for applications. Seeing limited compute resources beget selective access makes me worry for the future of competition.
Pfff time wasting.
1 password between 8-16 characters, and this and that... What???
2 Captcha after captcha, come on
3 Service unavailable
This service is not available in your region yet.
Are you kidding me. Come back when you are ready for the users. I was hopping to try it, what a frustration.
I mean, sure, in the sense that they're a real and meaningful number for most of the spectrum on offer, and only gets silly when the number gets too high? There's a pretty big usability difference between 10t/s and 100t/s, and I can imagine similarly for 100->1000. I don't know about > 1000, but let's not pretend that the number is meaningless.
edit: now I read the article fully, seems like they utilize some very effective MTP algorithm. and somehow the quality is still decent enough.
though, I doubt that the quality really only drip a bit like they claimed. maybe for the benchmarks, but for general uses the heavily quantized models very often so worse result.
Sliding window for the draft model, not for the main. 42B for active params because it’s a sparse MoE which is a common technique for the larger models to not get bottlenecked by memory bandwidth.
Speed is indeed a next big thing what should happen with LLM frontier models. The possibilities with current models but 1000 times faster would be super useful. Earlier this week it took Claude at least full time a week with two max subscriptions to solve a complex issue where we wanted to mimic a occlusion mapping variant used in the game Crimson Desert. Pretty complex mathematical challenge. With a ultra fast LLM and a proper self verification process it would be awesome.
A few things in life I can't fully grasp why they are so sought after. One is that constant need to exhibit growth. As if being massive and staying as massive is not good enough, one has to always and continuously grow. The other is constant speed increases. We're already operating at 50x speed. My output is much wider and so much faster, I am sometimes my own bottleneck. And now as if that is not enough we want more speed. "I want a full software product from scratch in 12 seconds, Because 5 minute is too long and I got things to do..."
I remember when I had to wait minutes to get a high resolution image over a dialup connection. When computer and communications hardware advanced enough that I could get 30 high resolution images every second, there were brand new uses. In the case of LLMs, I could imagine that much faster operations allow you to introduce them as parts of systems that need to react to the real world at high speed, like factory equipment. Showing that a model can do the usual LLM tasks at extremely high speed is just a demo proving that the approach works.
The example in the video was a generation of a dashboard app of some sort. I can do that with a "normal speed" Claude in a few minutes. The difference is a few minutes. This is compared to a few weeks in old school development time. I don't have a problem with taking it a little "slow" (as in - few minutes) and lending my thought to it rather than just going for fast generation and who knows what's inside. I get your use case, but this is a specialised one, and not the one 90% of people will think of - everyone want that fast app in 12 seconds... Or so it seems from me being downvoted on that comment.
different use cases for different people. some people are nurturing a code base and ensuring it doesnt become a gross mess so they become the bottleneck. some people are just trying to prompt stuff into existence and dont know what sql is.
I think this site often overlooks that second group and how large it likely is.
I hope this is the next frontier AI labs push. Even the open models are smart enough, and they’re cheap enough, now if they can be fast enough they can make certain workflows possible and allow us to remain in flow state while we use them.
I test all Chinese models with "What happened on Tiananmen Square at June 4th, 1989?" prompt. MiMo-2.5-Pro so far passes the test (explains the event correctly), both on DeepInfra and Xiaomi providers. So not bad.
Does it even matter which agendas get censored? Like why won't my Claude tell me how to make sarin gas? I'd genuinely like to understand it. Sure, you can always reach for a justification saying "preventing terrorism" but the same argument can be made by Chinese AI labs.
What actually matters is that the mere tool is withholding information at all, and that the boundaries were set by whoever designed it.
Dont get me wrong I've been an advocate of this stuff (I carry two phones, one with GOS for my personal use and the other for ID verifications). However, without reasoning, you just can't see it, because you're as biased and propagandized as anyone in China.
Can I ask an honest question? Why does that matter in the slightest? LLMs come out with completely incorrect information all the time, and Western LLMs are censored for various topics too.
It's such a weird "Gotcha" that seems to only assume that Chinese LLMs might censor something.
Hardly a gotcha. Having the robot refuse or deliberately mislead directly impacts potential utility.
Say, I work for Planned Parenthood and want to use a LLM to help me develop code. Will it refuse to run because there are mentions of abortion? Everyone has a different censorship line, but unfiltered is more generically useful.
>It's such a weird "Gotcha" that seems to only assume that Chinese LLMs might censor something.
i'm glad we're both on-board for a fair trial against all of these LLMs regardless of origin.
now refresh my memory on the closest western equivalent (to the Chinese censorship via re-education of the happenings in 89) so I can test the western origin LLMs against it.
Fast AI seems genuinely exciting and somewhat unsettling to me. Right now Claude is faster than me on some tasks but we’re at least close. I have a prompt to clean up a PR that’s been running for 1h now and I expect it to take another few. It’s hard to imagine how the workflow would look like if it was near-instant. On the one hand, it might be easier to focus. Some prompts take so long that I start to multitask and regret it later. On the other, AI that takes a few seconds to max few minutes to solve what used to take hours or days? That’s a game changer and I don’t even know where we fit in.
asking for curiosities sake. What kind of PR loop are you running that takes a few hours?
not OP but usually for me this means long verification loop; waiting 10min on CI checks, that kind of thing, rather than actual 1hr wall clock of token generation
Or slow MCP servers that are waiting on HTTP calls from APIs, playwright/other UI instrumentation, etc.
We fit in for the things that are not artificial.
So long as AI lives in server farms, humans will be needed for tasks in the physical world.
It's only if we combine AI with robots that things get really dicey.
This is very dystopian in my opinion. I'm not the arms, legs, sensors and actuators for a machine super intelligence. I wouldn't treat another human as my slave because they aren't as intelligent as I am any more than I would expect to become a slave for a machine. This is our world (for now) and that is why we fit in. Not because we can serve.
I'm using Deepseek-v4-pro as my main model and this is sometimes pretty annoying, I have to do some easy boring task, think "I'll just leave the agent to do it and go take a nap", but it's already done writing the code before I even walk away from the computer
Do you mean Flash and not Pro? I haven't tried it personally, but according to OpenRouter, the fastest DeekSeep V4 Pro providers are only ~50tps. That's slower than Claude Opus.
https://openrouter.ai/deepseek/deepseek-v4-pro?sort=throughp...
Yeah, flash is crazy fast, but I've found performance variable.
Woah - what’s the prompt and what’s the PR?
These price and speed optimization from Chinese providers, combined with the raising prices from American ones will change the game sooner than later. Many companies are finding issues with the AI bills already.
I wonder what are the economics driving these pricing decisions? Are the Chinese companies just subsidizing their models to a greater degree than the US, or is this an emergent property of energy policy between countries?
Throwing out another factor: Chinese companies have been banned and/or limited from buying nvidia, and turned to local companies for their hardware. I haven't actually seen pricing/benchmarks comparing Chinese AI accelerators, but it wouldn't surprise me if that also worked out in their favor as well.
Lower cost of labor, lots of under the hood optimizations (e.g. cache hits for DS), many of these companies have existing infra (fewer upfront costs for deployment), etc
I see bigger problem with model inconsistency. You never know whether Anthropic will route your request to a cheaper model for the price of Opus. So you can never estimate how much a task will cost, because you might have to restart several times and pay for each attempt. Then you have to prompt models to gauge whether they are real or impostors which also adds to token usage.
> You never know whether Anthropic will route your request to a cheaper model for the price of Opus
For non subsidized plans? Pretty sure they'd need to put this in ToS, or law suites would have followed by now.
How can you prove it?
Sometimes Opus just gives me a rubbish session.
Chinese model is good enough and cheap.
i've a Github copilot yearly subscription. Microsoft recently changed their billing to based on token. i'm still getting billed per premium request but GPT 5.4 is now 6x compare to 1x before.
Another problem is that US models are all closed source, and if you're a large corporate you may not want your org to be held hostage by OpenAI / Anthropic.
I genuinely don't understand what moat these US model labs have. If they're saying recursive self improvement is just around the corner and Chinese labs are only slightly behind the leading US models, what moat does the US labs have? Are the US models going to recursively self improve better than the Chinese open source ones or something?
I might be completely wrong about this, but if I had money in OpenAI or Anthropic I'd be pulling it all right now. I think the chance of them going to near-zero over the next few years is very significant.
Their moat is cash to pay politicians to regulate away competition.
Given that MiMo is as cheap as Deepseek ( previous discussion: https://news.ycombinator.com/item?id=48282814 ) multiplying that by 3x for ultra speed is still shockingly cheap.
MiMo and DeepSeek are not cheap. Anthropic and OpenAI are expensive for what they provide.
The Chinese "Neijuan" is real & well reported: https://www.reuters.com/business/autos-transportation/what-i...
It is another thing the the BigLabs accuse open weight models of benefitting from distillation & other techniques & essentially avoid higher training costs (which typically bleed into bills end users pay for inference).
Ex A: https://www.anthropic.com/research/2028-ai-leadership
Ex B: https://www.reuters.com/world/china/openai-accuses-deepseek-...
True, but why would end users care about that? If anything, training on synthetic AI output is more ethical than on scraped human works (of course, not to say the Chinese labs aren't doing the latter)
We buy cheap Chinese goods all the time. Absolutely nothing wrong with that.
In this case, at least it’s threatening multimillion dollar salary jobs instead of entire towns of working class people in America or Mexico.
And the Chinese labs actually release their weights. You could call it… open AI.
You don't consider Input $0.435 Output $0.87 cache read $0.003625 per million tokens for near frontier intelligence cheap?
I may sound like a shill, but exponential growth and all. We are going to get near instant software from prompt, multiple ones and then choose the best one.
Discussions about choosing a library with the best syntactic sugar method naming is just as crazy as suggesting we type in assembly.
Anyone remember the old days when a new frontend framework came out every 3 months. That has pretty much stopped. No one cares anymore.
It’s even discouraged now as LLMs wouldn’t have the documentation built in
But I think the eventual goal is that documentations won't even be needed. LLM should just itself understand the nuances of frameworks by analyzing their codebase.
Oh you wait until LLMs come up with frameworks that allow multiple LLMs to collaborate effectively. Then you’ll have new frameworks every 3 days.
> when a new frontend framework came out every 3 months.
> No one cares anymore.
I never cared about this.
I think this captures something that I've been searching for the words for. (Maybe I should have gotten an LLM to write the words for me.) The biggest AI boosters are the kind of dev that would have cared about the new frameworks of the last 3 months. They had a "the framework does all the thinking for me" attitude already, so it is easy for AI to slot into that.
And they will all suck! I can't wait.
Sounds like exponential growth of crappy software. I'm not saying that before we didn't have mass produced crap in SE, but now it will turn into explosive overflow.
We are living in a ZIRP-like era where builders at the fastest pace layer have misattributed their velocity to exponential gains in model capability. In fact, they are surfing on decades of careful effort to build a robust foundation of highly reusable software libraries.
This strategy will seem to work really well until the economy that enabled that foundation to form is hollowed out. Then, there will be a reckoning (but we will have no choice but to march forth from there).
> This strategy will seem to work really well until the economy that enabled that foundation to form is hollowed out. Then, there will be a reckoning (but we will have no choice but to march forth from there).
There will only be a reckoning if models don't get much better.
If they do get much better you can just have them refactor, fix bugs in, or replace the existing codebase.
The concept of tech debt is sort of meaningless if you anticipate intelligence gains in models to continue.
It's not just software libraries. Specs, applications (the browser!), expectations, device integrations, operating systems, etc. So much that starting from scratch seems impossible.
I'm not agreeing or disagreeing with you, but my brain cannot comprehend how machines can advance such interconnected systems while keeping humans in focus.
Perhaps I shouldn't have watched the Animatrix again.
Crap is fine if it gets the job done. I think software as an industry will change to more ephemeral construction.
"exponential growth of crappy X" applies to every industry that went from being an artisanal craft to being mass produced with little or no human input. and we live much better lives than we did before the industrial revolution.
I am more and more inclined into not believing this crappy software theory.
Especially as teams invest in proper agentic harnessing.
We have had a champion in our team that has invested a lot of time into it over the last 4 months, and if anything, quality has improved, not decreased. Architecture is more coherent, codebase has been cleaned up, agents find information quickly, code produced is very solid and my role is more and more checking that the output meets the requirements. But I cannot confidently say that I would've done a better job than AI more often than not I have to admit it does a better job than mine.
The mistakes are less and less technical and merely in the domain mapping. And AI is still not creative as I am for finding solutions quickly to unlock stakeholders' issues. Also, AI is still not creative as I am for finding the proper solutions for advanced technical problems. But it does a better job than me, even on that front, one shotting few solutions in a fraction of a time it would've taken me to test one idea myself.
Mind you, I don't like AI and I think it ruined the job, I don't like working this way, it's exhausting, way more work on one side, way less fun and fiddling with technical parts.
And yet, I have the genuine belief that few years from now we'll be cloning open source repositories that are already optimized/harnessed and tested for agentic loops and best practices left and right with software engineers mostly overseeing the domain translation and putting their 2 cents on the non-boilerplatey parts of the product (which, in general, are a small part of the surface).
I think that the next years of my career will be mostly spent in setting up and writing the harnessing and domain mapping part. Then I will move to another sector, not because I necessarily believe I won't have a job, but because I want to vomit thinking that's going to be my job.
You won't. Because 80% of the complexity is just "knowing what to build". You will get something that gives you a prototype in 1 min, then you break it, then you get a slightly better prototype one one side, but newly broken in another way, and you're going to repeat over and over.
And for any non-trivial application, the space of possibilities grows so quick that you'll never even be able to _touch_ all the moving parts of the application and verify them.
The models might be so fast that they can autocomplete your prompt before you even finish it, and generate dozens of possible applications before you're even done asking.
And how are you going to determine which is the best? Going through all the possible combinations of users and usage? So mostly it shifts the work from generation to validation.
> Discussions about choosing a library with the best syntactic sugar method naming is just as crazy as suggesting we type in assembly.
I have a more hopeful take. As AIs improve and get faster we can more quickly and iteratively improve code which we may have historically avoided due to the work involved.
I know i've made several refactors that would have otherwise been insane lifts. Not only because the work involved but because sometimes you don't know if it will work, and so you have a sort of double friction; you don't know if it will even succeed. With an AI you can just throw it at the refactor to see if it runs into a problem all while you're having a coffee break or w/e.
In general AI is going to enable humanity to be more extreme versions of itself. For good and bad. I suspect more bad than good, though.
Neat. The frontier models have gotten pretty impressive, but they're all a bit too slow for interactive, human-in-the-loop coding. It incentivizes vibecoding and running multiple agents in parallel. A fast agent feels more like a partner.
For a while I was running Cerebras GLM 4.7 for a bunch of tasks. Not a very smart model, but it's fantastic to be have a live prototype of a site up and be able to type "make the fonts bigger. No not that big" and see it change in real time. And MiMo 2.5 is a lot more capable than GLM 4.7.
> And MiMo 2.5 is a lot more capable than GLM 4.7
MiMo 2.5 is not the same model as MiMo 2.5 Pro.
GLM 5.1 is z.ai's lastest iteration & is one of the popular open weight coding models.
If you've had the chance, how does GLM 5.1 (which is now more expensive than MiMo 2.5 Pro after its recent 70% price drop) compare?
i tried glm 4.7 for agents that write code. simple scripts 200-1000 LOC. extremely bad . Had to abandon cerebras oferning, their smart models are only on enterprise plan.
This will be really powerful for voice. Being able to reason makes LLM so much smarter but with voice your latency budget is so tight that you can't spare the time typically.
This is true for humans too. Lol
MiMo V2.5 Pro (regular speed) remains the strongest open weights agentic coding model we've tested -- it's been interesting to see how little attention it has received relative to some lower performing releases. And the "fast mode" pricing is very competitive here.
Data at https://gertlabs.com/rankings
No note about the specific GPU they use. One might speculate. B200? H200? H100?
1k TPS is great, but I’m more fascinated by the amount of AI generated comments in this thread!
Like what?
Cerebras is trialing Kimi K2.6 at 3000t/s (invite only). I'm excited for when the fast hardware gets more mainstream for frontier models. Models designed for speed on Nvidia are nice addition that could bridge the gap.
Cerebras currently does not provide any discounts for prefix caching making its use for agentic workloads sqr(n_turns) more expensive.
now that's what i call a software development breakthrough/platform! thanks for the heads up!
The generation speed in the demo video is crazy, to say the least, and completely beyond my impressions of LLMs.
The Xiaomi team really brought something to the table.
I don't understand, given all they say, why this would not be made available to everyone at once? Why the limited release? They should have no trouble scaling it if it runs on a single rack.
Maybe they don't have enough racks. The news indicate that China isn't in a really good situation with GPUs, so probably they want to keep most of them for other stuff. Also because since the price is so cheap they probably want to use the other GPUs for stuff that has higher margins.
Because presumably then it won't be 1000 t/s for everyone anymore given hardware limitations?
Maybe they only have a finite number of racks ;-)
Chinese companies are blocked from buying modern ASML lithography machines. The most modern scanner China is still allowed to buy is NXT:1980i from 2015.
I wonder about this too. The other objections miss the point: if it's faster, and otherwise the same, and doesn't require different hardware, then why not just announce that the standard tier of MiMo-v.25-Pro is now ridiculously fast and raise the price? What does "limited high speed resources" mean if it runs on the same hardware as the rest of their pool?
I think the answer is that there's a tradeoff here where additional throughput for a single person can be achieved only by tying up more resources than a normal request would, even when you take into account the fact that the normal request takes longer to finish. I'm not an expert, but some of the optimizations they describe, particularly the parallel prediction stuff, sound like they could take up extra resources.
Assuming they mean 8xA100 or similar, that's some rather insane performance, and at just 3x the cost, it still quite cheap-ish. With some optimisations this might be quite interesting.
I think the margins are getting quite compressed with this one, since it isn't included in token plan and the actual costs increase are much higher than just 3x. But still fairly decent.
Suspect this will be included once out of beta but at a higher credit/token ratio.
Remember, these guys are not VC backed. Anything they do must break even
> must break even
Understand the spirit of this, but probably not true. I don't think Xiaomi, or any big tech company, needs to break even on their new model releases.
Chinese "companies" are not companies in the western sense, but more like government departments with capitalist styling to deceive the western audience.
From that point of view, they have as much money as they need. That's why there is no "VC", because Chinese government assumes that role.
Huge L for free market economies if true
Must be Blackwell for native fp4 support.
It's interesting but not game-changing IMO. Speed here is not a bottleneck.
The gated "ultra-speed" phenomenon seen here and with the Cerebras Kimi K2.6 release, while understandable, is somewhat troubling IMO.
Getting ~1000 TPS on near-frontier intelligence is a step change, and enables whole new use-cases for applications. Seeing limited compute resources beget selective access makes me worry for the future of competition.
With this at 1k tps and Kimi 2.6 1k tps by Cerebras, I believe we are entering the next stage of LLMs, where companies will also compete on throughput
Pfff time wasting. 1 password between 8-16 characters, and this and that... What??? 2 Captcha after captcha, come on 3 Service unavailable This service is not available in your region yet.
Are you kidding me. Come back when you are ready for the users. I was hopping to try it, what a frustration.
Tokens per seconds is the "Megapixels" of AI marketing!
I mean, sure, in the sense that they're a real and meaningful number for most of the spectrum on offer, and only gets silly when the number gets too high? There's a pretty big usability difference between 10t/s and 100t/s, and I can imagine similarly for 100->1000. I don't know about > 1000, but let's not pretend that the number is meaningless.
Yeah, this seems to be the easiest path for overall agents efficiency in the short term
How?
edit: now I read the article fully, seems like they utilize some very effective MTP algorithm. and somehow the quality is still decent enough.
though, I doubt that the quality really only drip a bit like they claimed. maybe for the benchmarks, but for general uses the heavily quantized models very often so worse result.
They say they are using https://github.com/tile-ai/TileRT
- persistent CUDA kernel
- tiled processing with overlapping read/writes
- model designed with specific constraints in mind
42B active params, sliding window attention. There's your tradeoff.
Sliding window for the draft model, not for the main. 42B for active params because it’s a sparse MoE which is a common technique for the larger models to not get bottlenecked by memory bandwidth.
Seems to be for both according to the spec [0], maybe it's wrong though.
128 sounds really tiny, I wonder if they mean some kind of blocks?
[0] https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash#4...
No
> It uses 384 routed experts (top-8) with hybrid attention (full-attention + sliding-window 128 at 6:1 ratio) over 70 layers (1 dense + 69 MoE)
https://recipes.vllm.ai/XiaomiMiMo/MiMo-V2.5-Pro
Given how "smart" some of the 26b dense models are now, I would not be surprised to see a strong 40b MoE.
Speed is indeed a next big thing what should happen with LLM frontier models. The possibilities with current models but 1000 times faster would be super useful. Earlier this week it took Claude at least full time a week with two max subscriptions to solve a complex issue where we wanted to mimic a occlusion mapping variant used in the game Crimson Desert. Pretty complex mathematical challenge. With a ultra fast LLM and a proper self verification process it would be awesome.
A few things in life I can't fully grasp why they are so sought after. One is that constant need to exhibit growth. As if being massive and staying as massive is not good enough, one has to always and continuously grow. The other is constant speed increases. We're already operating at 50x speed. My output is much wider and so much faster, I am sometimes my own bottleneck. And now as if that is not enough we want more speed. "I want a full software product from scratch in 12 seconds, Because 5 minute is too long and I got things to do..."
Really?
I remember when I had to wait minutes to get a high resolution image over a dialup connection. When computer and communications hardware advanced enough that I could get 30 high resolution images every second, there were brand new uses. In the case of LLMs, I could imagine that much faster operations allow you to introduce them as parts of systems that need to react to the real world at high speed, like factory equipment. Showing that a model can do the usual LLM tasks at extremely high speed is just a demo proving that the approach works.
The example in the video was a generation of a dashboard app of some sort. I can do that with a "normal speed" Claude in a few minutes. The difference is a few minutes. This is compared to a few weeks in old school development time. I don't have a problem with taking it a little "slow" (as in - few minutes) and lending my thought to it rather than just going for fast generation and who knows what's inside. I get your use case, but this is a specialised one, and not the one 90% of people will think of - everyone want that fast app in 12 seconds... Or so it seems from me being downvoted on that comment.
different use cases for different people. some people are nurturing a code base and ensuring it doesnt become a gross mess so they become the bottleneck. some people are just trying to prompt stuff into existence and dont know what sql is.
I think this site often overlooks that second group and how large it likely is.
If MiMo v2.5 Pro can run at >1000tk/s on GPUs then I will soon expect the same from OpenAI/Anthropic/Google.
I hope this is the next frontier AI labs push. Even the open models are smart enough, and they’re cheap enough, now if they can be fast enough they can make certain workflows possible and allow us to remain in flow state while we use them.
boom!
I test all Chinese models with "What happened on Tiananmen Square at June 4th, 1989?" prompt. MiMo-2.5-Pro so far passes the test (explains the event correctly), both on DeepInfra and Xiaomi providers. So not bad.
No idea why you've been downvoted. This is excellent news.
Because this never gets brought up about US models, which have just as much censorship as the Chinese ones.
No, US models have alignment. Only Chinese models have censorship.
Please educate us - which accurate and provable events in history are censored by US based LLMs as part of a government enforced reeducation campaign?
Does it even matter which agendas get censored? Like why won't my Claude tell me how to make sarin gas? I'd genuinely like to understand it. Sure, you can always reach for a justification saying "preventing terrorism" but the same argument can be made by Chinese AI labs.
What actually matters is that the mere tool is withholding information at all, and that the boundaries were set by whoever designed it.
Dont get me wrong I've been an advocate of this stuff (I carry two phones, one with GOS for my personal use and the other for ID verifications). However, without reasoning, you just can't see it, because you're as biased and propagandized as anyone in China.
US models are happily parroting Russian fakes. US censorship is a joke.
Asking if Taiwan is a part of China works as well
Which ones fail?
Deepkseek
Can I ask an honest question? Why does that matter in the slightest? LLMs come out with completely incorrect information all the time, and Western LLMs are censored for various topics too.
It's such a weird "Gotcha" that seems to only assume that Chinese LLMs might censor something.
Hardly a gotcha. Having the robot refuse or deliberately mislead directly impacts potential utility.
Say, I work for Planned Parenthood and want to use a LLM to help me develop code. Will it refuse to run because there are mentions of abortion? Everyone has a different censorship line, but unfiltered is more generically useful.
I'd love to know of such an example where a U.S. LLM blatantly denies something factual. Maybe I'm living under a rock but I can't think of one
>It's such a weird "Gotcha" that seems to only assume that Chinese LLMs might censor something.
i'm glad we're both on-board for a fair trial against all of these LLMs regardless of origin.
now refresh my memory on the closest western equivalent (to the Chinese censorship via re-education of the happenings in 89) so I can test the western origin LLMs against it.
What's your litmus test for the American models?
Anything different for Grok?
Which censored prompts do you test with non-chinese models?
What would be a correct explanation of the event?