开放模型回顾：Kimi K3、Qwen 3.8、习近平 WAIC 演讲、蒸馏、开放与闭源差距，以及下一步

Nathan 和 Florian 坐下来讨论开放模型领域正在发生的一切。继上周 Kimi K3 发布后，似乎一切都在加速——中美地缘政治、开放模型与闭源模型的经济学、AI 前沿安全等。

章节：
00:00 欢迎与背景
04:38 使用 Kimi K3 的体验
08:53 GLM 5.2 的持续作用
12:47 中国模型为何如此出色？
17:41 数据、环境与中国实验室巡礼
19:47 中国服务商盘点：Qwen、DeepSeek、MiniMax……
24:08 美国开放模型生态
30:25 前沿与准前沿，以及反对禁令的网络安全理由
34:58 蒸馏与 Ben Thompson 之争
44:12 预测与前沿模型分级
48:36 总结

可在 Apple Podcasts、Spotify 以及你常用的播客平台收听。更多 Interconnects 访谈请点击这里。

00:00:06 Nathan Lambert： 好，欢迎回到 Interconnects。我们正在做季度开放模型盘点，主要是我们解释——或者说吐槽，倒不是吐槽——为什么这么多关于蒸馏的观点并不好，以及理解目前的整体状态。

我想 Kimi K3 是在上周四发布的。未来不久，我们显然会看到更多进展，这几乎不可避免。比如周末时，习近平发表了演讲，直接承诺将开放性和开源作为一种战略。这并不是一份详细的现状说明。

Qwen 宣布他们的下一款大型模型将采用开放权重，这带来了很大的变化。我觉得有太多内容可以讨论。Flo，我想你已经在谈性能差距和蒸馏相关的观点了。我们可以从那里开始；然后我手头有一份小清单，我们总能按主题以及我写的博客文章来讨论，其中的内容都很微妙。所以，我觉得我们有无限多的话题可聊。继续你的吐槽吧。

00:01:17 Florian Brand： 是的，我认为，每一次模型发布——至少每次开放模型发布——最大的问题都是：它距离闭源前沿到底落后多少，或者说落后多少个月。

人们喜欢给这个问题套上一个明确数字，但这真的会让问题变得很混乱，因为如今有太多不同的基准测试提供方，也有太多不同的基准。每个网站——我自己也不能免俗——都会挑出自己喜欢的基准，以证明当前模型或新发布模型已处在前沿；然后另一方又会拿出另一个基准，说，哦，它实际上还落后一年左右。

看起来，许多讨论都取决于这个问题：我们的开放模型到底落后多少个月？

00:02:26 Nathan Lambert： 对。我的一个挑衅性观点是，有些基准实际上与人们在做的事情存在相当合理的相关性，比如 Agentic Coding 和 Agentic Computer Use 任务；还有一些基准与长尾需求相关，而我认为这正是 Claude 和 GPT 如此有价值的地方。

但如果现在 Claude Code 和 Codex 的市场主要是软件工程，那么模型在这方面哪怕只落后几个月，也可能是非常非常大的差别。我猜这款模型会——免责声明：模型权重还没有发布，据称将在 7 月 27 日推出，许多讨论都建立在它会发布这一假设上——但人们可以对这款模型进行后训练，使其极有可能在许多用户需要的细分领域匹配 Opus 和 GPT。

我认为，观察开放模型后训练行业会很有意思。我们两人都以不同视角接触这个行业，但目前对于将这些模型微调到特定高价值任务上，正在进行大量令人兴奋的工作。历史上，这通常是在 Qwen 和 GLM 的组合上完成，而 GLM 5.2 显著加快了这一进程。

我很好奇第一个发布博客文章、说“我们已在自己的任务上微调 Kimi K3”的人会是谁，因为我打赌这会带来很大的提升。即使你使用 Kimi K3 比我更多，我的直觉仍是，考虑到它扩展到如此大的规模，它的后训练可能会有些粗糙边缘；而这通常意味着，其中还有许多性能可以被进一步挖掘。对吧？

00:04:03 Florian Brand： 对，运行、运行、运行。特别是后训练，这会非常困难，因为仅仅加载权重就需要一整个 B300 节点，这样的规模实在疯狂。因此，要把它真正调到可微调的状态，可能需要一些时间和大量工程工作。

不过，你想谈谈使用这个模型的体验吗？你实际上注册了它的编程计划并使用过它，所以把这个背景介绍出来会很有帮助。

00:04:38 Nathan Lambert： 对。我大概是在发布后第二天注册了他们的 200 美元套餐，这是最高档套餐，和其他服务类似；我想他们还有 40 美元和 100 美元的套餐。最高档套餐拥有 100 万上下文窗口，而且我认为——或者至少感觉上——它的 API 请求也有一定优先级，因为网上很多人都说他们持续遇到 API 错误，而到目前为止，我的体验还算相当不错。

就模型能力而言，除了它在前端方面确实很出色之外，它在一些领域也真正闪耀、表现优异，甚至超出我的预期。

比如一些研究任务。Interconnects 现在拥有一年多的开放模型数据，我让前沿模型提出一些我们之前没有做过的有趣分析，因为我们自己会做分析并发布。我让它们做些新东西，基本上就是给我惊喜。

许多模型，或者说前沿模型，实际上几乎所有模型，都会抓住我们已经做过的内容，重新完成数据分析部分，然后添加一些奇怪而冷门的内容。

Kimi K3 做了一些更有意思的事情。我明确告诉它去抓取 Reddit，它找到了几个我甚至没考虑过的 subreddit。随后它发现，例如 Reddit 上的讨论会早一两个月出现；人们通常会比下载量真正起飞早一两个月发现有意思的模型。比如大家都在讨论 Qwen，然后更多人开始下载 Qwen 模型。这类分析具有开创性；与其他所有前沿模型相比，Kimi 给了我这样的惊喜。

一个简单的问题是：你能否用它完成自己大部分核心工作？你平时会处理一系列任务，我认为其中大部分是用 Codex 完成的。你更偏向 Codex 用户，而不是 Claude 用户。你觉得两者的能力集合有多大比例重叠，也就是说，这个模型在多大比例的任务上已经足够好？

00:07:24 Florian Brand： 这真的取决于我给它多大的容错空间。就我目前在 Kimi K3 上看到的情况，我正在开发我们在 Prime Intellect——也就是我工作的地方——所做的框架。Kimi 最明显的特点是，它生成的代码简单得多，因此可读性更强；但它会遗漏一些 Codex 能发现的东西。我们说的 Codex 大概是 5.6、5.5，尤其是 5.4 所能达到的水平。

所以，对这类任务，我会说 Kimi K3 大概处于 5.4 至 5.5 水平。

但如果我阅读它的代码，觉得“好，这代码确实很好”，然后再让 Codex 过一遍，Codex 就能发现各种它不擅长处理的细微边缘情况。不过，对于监督运行或执行一些实验，它确实很实用。对于另外一些细分任务，你也可以让它直接运行。

唯一的缺点是——但这也是因为 API 完全被用户挤爆了，而且它们的服务器在中国——实际耗时明显、明显高于 GPT。但我会说，如果我真的把它纳入日常工作流，我会变慢，但不会慢到让我说：“好，这根本无法使用。”

00:08:53 Nathan Lambert： 那它和 GLM 5.2 相比如何？在我看来，GLM 5.2 仍是一个正在发展的故事。比如我会在旧金山到处走走，人们会说：对，我确实在 Agentic Coding 和工作流的这一部分使用它。

你属于使用 GLM 的那一派吗？

00:09:20 Florian Brand： 是的，我以前用过、现在也在用 GLM，主要是因为我们有一个非常快的内部端点；在此之前，我也使用过一个 API，速度大概是每秒 200 或 300 个 token。

如果你能以足够好的水平、非常快速地完成很多任务，那么你就会使用这个模型，而不是先进入 Codex、再选择较小模型、再选择合适的推理强度、再选择快速模式。我直接使用 GLM，也能得到相同结果，而且效果相当不错。

它在能力上绝对处于 Sonnet 类似的水平。对于很多清理型任务，或者只是体力活性质的任务，它确实有效。我会说，对于大量工作，你大概可以用 Kimi K3 作为主 Agent、GLM 作为子 Agent，走得非常远。

00:10:24 Nathan Lambert： Kimi 的公告与这个规模模型的情况有一个明显不同：我认为，这些开放模型需要更长时间才能真正被推理服务提供商优化并广泛提供。

GLM 5.2 相当快，但第一，我们还没有权重；第二，我不认为它的采用和部署速度会像 500B、700B MoE 模型那样快。我觉得会出现更多问题。这是一个很不一样的阶段：过去，中国模型完成 RL 训练后，会在几小时到几天、或许一周内发布开放权重，然后整个生态几乎立刻就知道该怎么做。

我认为，下一规模的开放权重模型需要大得多的基础设施升级，我们必须将此纳入考虑。闭源实验室会在模型公布前就在幕后完成这些工作。因此，这在某种程度上会扭曲时间差：人们可能还要额外等一个月，才能真正对 Kimi 进行后训练，并在自己的工作流中大规模使用它。

作为开放权重的支持者，我们喜欢说，只有闭源模型真正可用后，才能开始计算时间差；但现在开放模型中也出现了类似动态：Kimi API 完全坏掉了，需求过多、供给不足。所以，这款模型并不是立刻扩散开来。我只是在思考，这与性能时间差之间的关系。

00:11:54 Florian Brand： 这确实如此，但另一方面，开放生态在过去几个月里已经专业化了很多。

比如在最初的发布阶段，模型通常会与一些提前拿到权重的合作伙伴一起发布。如今，vLLM 的补丁会提前几天、甚至几周推出。这与一年前完全不同：当时基本上就是权重突然发布，然后模型开发者说：“好，你们自己想办法。”

因此，我预计首日的总体可用性会相当不错。接下来，各家服务商会展开竞赛，不断优化以获得越来越高的速度，因为这件事极具声望。

00:12:47 Nathan Lambert： 对。好，我们可以往两个方向讨论。为什么我们认为中国模型能够做到这么好？

我写过相关内容。我们 Discord 里曾与 Epoch 的 JSD 展开讨论，我认为那场讨论非常好。我在文章中有一部分提到，我开始倾向于认为，中国实验室的资本效率更高：它们能够将资本转化为算力、数据和人才，并以一种使模型变得更好的方式完成这件事。

如果这真的是某种结构性优势，无论其原因是什么，我认为都极其重要。原因可能是：由于某种教育体系，中国人才在解决让 LLM 变得更好的问题上受到了更好的训练。

也可能只是中国的算力、人才以及其他一切成本都低得多。无论是补贴，还是平均薪资较低。但在模型迭代不断推进时，这会是非常重大的问题。

如果 Anthropic 的下一代模型需要花费 100 亿美元，而 Kimi 只需 40 亿美元，那么这可能会非常巨大；但目前并不清楚为何会是这样。例如，我认为 Kimi 的工程师 Big Eagle 回复了我关于此事的推文，他说这有帮助，是因为“我们并不是试图推进前沿，只是在努力追赶”。这确实可能是一种心态问题：中国实验室对目标的界定方式，可能让它们构建这些模型所需的资金少得多。

但在过去一年里，我们一直在问很多问题，比如中国模型会不会掉队。我曾认为……

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Nathan and Florian sit down to discuss everything happening with open models. Following the Kimi K3 release last week, it feels like everything is accelerating — geopolitics of US v China, economics of open vs. closed models, security at the frontier of AI, and so on.

Chapters:
00:00 Welcome & context
04:38 Living with / using Kimi K3
08:53 GLM 5.2’s continued role
12:47 How are the Chinese models this good?
17:41 Data, environments, and a tour of the Chinese labs
19:47 Roundup of Chinese providers: Qwen, DeepSeek, MiniMax…
24:08 The US open-model ecosystem
30:25 Frontier vs. near-frontier, and the cybersecurity case against bans
34:58 Distillation and the Ben Thompson debate
44:12 Predictions and a frontier tier list
48:36 Wrap-up

Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here.

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00:00:06 Nathan Lambert: Okay, welcome back to Interconnects. We’re doing our quarterly open model roundup, which is mostly us just making fun of or explaining, not making fun, why so many distillation takes are bad and understanding the state of where things stand. I think last Thursday was when Kimi K3 was released. I think we will see much much more in the near future. It seems pretty inevitable. Like over the weekend, Xi gave his speech where he directly committed to openness and open source as a strategy. It wasn’t a detailed layout state of affairs.

Qwen announced their next big model is going to be open weight, which is a big change of things. I think there’s just so much to get into. I think Flo you kind of were already going off on some of the performance gap and distillation takes. So we could probably start there and then as I go I have a little bit a little list and we could always go through the topics and the blog that I wrote which all are very nuanced. So I think we have infinite to talk about. So continue rant kind.

00:01:17 Florian Brand: Yeah, I think, or the biggest thing at every model release at least at every open model release is how much or how many months it is behind the closed frontier. Um and people love to put a definite uh definitive number onto this uh which is really really mudding because we have so many different benchmark providers these days and such uh so many different benchmarks as well that every site and I’m not innocent in that either um pulls up their favorite benchmarks to show that the current model or the newly released model is at the frontier which is then counted by the other side pulling up another benchmark and showing oh it’s actually a year behind or something. Um and it like a lot of it seemingly hinges on that question how many months we open models are behind.

00:02:26 Nathan Lambert: Yeah. So I my provocation is that some of the benchmarks are actually reasonably correlated with what people are doing and this is agentic coding and agentic computer use tasks and some of the benchmarks are correlated with the long tail which is where I think Claude and GPT is so valuable. But if it’s it’s like what is the market for Claude Code and Codex right now and if it is software engineering then like the fact that the models are say a couple months behind on that can be a very, very big deal and then I suspect that this model will be okay disclaimer the model weights aren’t out yet supposedly on 20 July 27th and a lot of the discussion will impinge on the assumption that they come.

But like people could post-train this model to very likely match Opus and GPT in many of these kind of niche domains that people want I think watching I mean we both have different views into the post-training open model industry, but there is a ton ton of excitement in progress on making these models like fine-tuned for specific high-value tasks and this has been historically done on a mix of like Qwen and GLM and GLM 5.2 really accelerated this and I I curious on the first person that puts out a blog post like we fine-tuned Kimi K3 on our task because I bet you could get big gains. I think even the you use Kimi K3 more than I do, but my hunch is that it would be a bit of a um rough edged post-training just by how big of a scale up it is and that normally means there’s a lot of performance that could still be extracted from it. No.

00:04:03 Florian Brand: Yeah. Running, running, running and especially post-training that one will be super hard because you need like one node of B300s to just load the weights which is crazy in terms of scale. So will probably take some time and uh a lot of engineering I’ve heard to actually get this into a state where it’s fine-tunable. Um but people you want to talk about using the model like you actually signed up for the the coding program and used it. So like getting this out there is good context.

00:04:38 Nathan Lambert: Yeah. So I signed up on day after release or so uh for the $200 plan uh which is their biggest one similar to to all the others but they have um like I think $40 and $100 as well. Uh but the biggest plan has uh 1 million context and I think or at least it feels like it has also some priority in terms of the API requests because so many people um online are saying that they hit API errors constantly and so far I’ve been I’ve been uh pretty well off if I’m uh going to say that. Um and in terms of model capabilities, it at some ways aside from front end where it is really good, it in some ways it really shines and it excels.

Um even my um expectations even with things like uh some research tasks like I have or at interconnects we now have over a year of data on on open models um and I ask the frontier models to come up with some interesting analysis which we haven’t done before in uh because we do our own analysis and have this published uh and I asked them all right do something new and um surprise me, basically. And a lot of the models or the frontier models u or basically all models latch onto the things we’ve done redo the data analysis part and then do some weird esoteric parts.

Uh Kimi K3 did some more interesting things um I’ve told it explicitly to scrape Reddit um and then it found uh some subreddits I haven’t even considered and then found out for example that the Reddit discussions are um one or two months in uh more recent or they found they find the interesting models one or two months before the download numbers usually take off like they are all onto Qwen and then the people download more Qwen models like those kind of analysis is groundbreaking um but it is something that Kimi surprised me at compared to to all the other frontier um models.

A simple question like can you use this for most of the core work you do in terms of like the exp you you have a distribution of stuff you tend to do most of them are with Codex I think you’re a Codex person rather than Claude person like what percentage do you think the Venn diagram overlaps where this model would be fine

00:07:24 Florian Brand: uh it’s really depends on how much leeway I give it like, the big thing I have seen with Kimi K3 right now I’m I’m working on u the framework we are doing at uh at Prime Intellect, where I work, and the main thing I found with Kimi is its code is a lot simpler uh which makes it way more readable uh but it misses some things that Codex just or like we’re talking 56, 55 and especially 54 would be on those levels. So, I would say that Kimi K3 is like 54-55 level for these kind of tasks.

But if I like I read the code and I say all right that’s really good code and then I give it a pass over with with Codex and it finds all these niche niche cases where it doesn’t excel but for supervising runs or for running uh some experiments it is actually really usable. Um and for some other niche things like you can just let it run. The one downside is but that’s also because the API is completely swamped in terms of users and it their servers are in China. The wall clock time is significantly significantly higher than GPT. But I would say like if I was to to push it and use it in my daily workflow, I would be slower, but I wouldn’t be slowed down by so much that I would say, “All right, that’s unusable.”

00:08:53 Nathan Lambert: And how does this compare to GLM 5.2? Because GLM 5.2 was still a story unfolding in my opinion where like I would go bop around SF and people are like yeah I genuinely use this for this part of my like agentic coding and/or workflow. Um, how do you like I feel like were you in that camp using GLM at all or

00:09:20 Florian Brand: Yeah. like where do you I also use used and use uh GLM mostly because we have an internal endpoint which is really fast and we have or or before that I I also used an API which had I don’t know 200 or 300 tokens per second. Um and if you can do a lot of task at a good enough level like really fast you just use that model compared to going to Codex then selecting the lesser model then selecting the right reasoning effort then selecting fast like I just use GLM get the same result and uh and it’s uh pretty fine like it it definitely is Sonnet-ish level in terms of capabilities and for a lot of cleanup task for a task that just is grunt work. It really works. Like I I would say you could probably go really far for a lot of the work uh with Kimi K3 as the main agent and GLM for for sub agent work.

00:10:24 Nathan Lambert: Something that’s pretty different with Kimi’s announcement and the scale of models this is. I think it’ll take a bit longer for these open models to really be optimized and available across the inference providers. Like GLM 5.2 is pretty fast, but one, we don’t have the weights yet, and then two, like I don’t think it’s going to be as fast of a roll out on adoption as the like 500B, 700B MoE. like I I there’s going to be more problems there which is a very different regime where in the past the Chinese models would finish their RL run and release the model with open weights within hours to days maybe a week and then like immediately the ecosystem kind of knew how to do this.

I think there’s a lot bigger of an infrastructure kind of uplift on this next scale of open weight models which I think we have to factor in like the closed labs do this behind the scenes before announcing the models. So it’s just like that is kind of manipulating the time gap in a way where it could be like an extra month before people can actually post-train and use Kimi at scale for their workflows. And like we love to say as an open weight fan like oh it’s only when the closed model is available that you could take the time gap but like now there’s similar dynamics in open models where it’s like the Kimi API is totally broken. There’s too much supply. There’s too much demand. There’s not enough supply. So it’s not like this model is immediately diffusing like the I’m just I’m just thinking about this as it relates to the performance time gap

00:11:54 Florian Brand: because that that is true but on the other hand the open ecosystem has professionalized quite a lot in the last few months. uh like during or in your initial roll out they all come with some partners which have the weights beforehand. They have the vLLM patches out days or or even weeks before these days which is completely different from from a year ago where basically weights got dropped and the model makers were like all right you got to figure this out. So I expect like the general availability on day one will be pretty okay and then race starts of all the providers starting to optimize to get even higher and higher speeds because it’s so much prestige.

00:12:47 Nathan Lambert: Yeah. Okay. Two directions to go. Why do we think the Chinese models are able to be this good? I think I’ve wrote about there’s a debate in our Discord with in with JSD at at Epoch and I think it’s very good and I had this section in my piece that I’m like coming around to think that the Chinese labs are more capital efficient and you can turn capital into compute data and talent in a way that makes the models better and this is really I think this is super important if it actually is some structural advantage whatever the cause I think the cause could be talent is better trained for whatever their education system was to work on problems that make LLMs better.

It could just be that all the compute and talent and everything cost way less in China somehow. Whether it’s a subsidy, whether it’s just average pay being lower. But this is a very big deal as we turn the crank in the model iterations. And if a next generation model costs $10 billion for Anthropic but only $4 billion for Kimi like this this is like could be very huge but it’s not clear why this is the case. For example, I think Big Eagle the Kimi engineer replied to my tweet on this and was like it helps because we’re not trying to push the frontier. are just trying to catch up, which really could be a mindset thing where how the the goals of the labs are scoped in in China so that it cost them way less money to build these models.

But I in the last year we’ve asked a lot of questions on like will the Chinese models fall off. I have thought that the gap between closed and open bottles would grow due to this kind of capital intensity of training and it seems like it’s going the opposite direction which is just like it’s it’s hard to unpack but like do you agree that the labs are keeping up a bit more than we would have expected as in the Chinese labs and why?

00:14:43 Florian Brand: Well, I I actually looked at our uh predictions for uh for this year based on our last year’s recap and we basically said that the gap will stay with within a few months. Uh so that prediction seems to largely hold. Um luckily for us, we didn’t put a concrete number whether it’s 3 months, 6 months or 9 months. So we are safe on that side. Um but I think like the general thing we both felt when we were in China and talking to these people like they are like the researchers themselves are teams of two or 300 people all mid20s and all just want one model to be really good like they don’t seem to do any side quests.

They don’t seem to do anything that uh deviates from from these things. And um they might or in terms of compute which is a really hard question for for us to answer especially as uh these Chinese uh chips are now coming online. We have I also think chips I think chip smuggling increased substantially in the last like 6 to 9 months or the chips that have been smuggled started to become online.

00:15:58 Nathan Lambert: Smuggling is a general term for getting around export restrictions. If the chips are in Malaysia and they’re using them, I I count that similar and I think that that has massively increased in the last six to nine months, this is the partially the result of that and and you’re saying but I just wanted to put that out there of like I do think that they have a lot more compute though than they did when they were training the previous generation of models.

00:16:24 Florian Brand: Yeah. like we like or just for for context two weeks ago I think LongCat released their model which they uh claim and I we know that it is very likely true uh is trained entirely on uh on Chinese chips. uh they didn’t specify publicly which ones but people speculate that it’s uh that it’s some uh Ascends from Huawei um and as the domestic production ramps up and you can they’re probably used most or they are used for for training but they are especially useful for inference which is a huge part of training as well.

So they probably use some mix of uh of Nvidia and other chips for the training part and then an increasingly larger part for the inference part during which during the stage is is really important. So I think their overall compute is increasing and also they don’t actually have a lot of users. So they don’t need to power 1 billion users like ChatGPT has to do, hundreds or thousands of enterprises like Anthropic has to do because they don’t have that magnitude of uh of of paying customers.

00:17:41 Nathan Lambert: Yeah. And I think even those paying customers also, at least on the enterprise side, there’s just like there is company time and chatter when you’re supporting these things. Even if you’re like not a research, even if it’s not in your job, it like does change the attention of the company. And if SSI comes out with a good model, it’ll be the ultimate validation that distractions are are a problem, but that’s an aside that we we can wait on. I think the there’s also rumblings of the data and environments industry starting to appear there.

Do you remember any specific ones? Because when we were in China, it was kind of shocking how little they seem to utilize external data. So just a few months hearing a whole bunch of a month months after our trip we went in April and then just months later in July, we’re are hearing a few things of like new companies in China and them wanting to buy data and things. And that is uh like a funny timeline of how that changes.

00:18:40 Florian Brand: And I would put error bars on what they actually told us.

00:18:44 Nathan Lambert: And that cuz it’s like so close in time that I don’t know.

00:18:49 Florian Brand: Yeah. That that that might that might be true. Uh but like those things are hard to to pinpoint. I but I would say it it seems like the buying of external data is becoming more of a factor. Um which will help the open models catch up to the closed ones if they just buy the same data maybe at a discount because um they buy the the data environments later. But it is it is a factor. How big of a factor like we don’t know. we don’t have any public insights and I doubt that we will get those insights uh from from anyone b uh really uh so that’s definitely one of the parts why um why we are able to to catch up or improve their their model scores.

00:19:47 Nathan Lambert: Okay, roundup of other Chinese model providers. We’ve talked about Kimi, we talked about Zhipu / GLM. I think there will be more GLM models soon that are very good. They might call it like GLM 5.5. Um Qwen, we talked about their biggest model coming. Qwen’s biggest models I will say have tended to relative to the excellence of their small models not had the same like absolute ranking in performance which is a probably a cost of focus. I think it goes with a cloud companies. It’s it’s almost like it’s if you squint it’s almost like Google.

It’s like Qwen has Alibaba has so much opportunity here and the opportunity of getting developers associated with Alibaba Qwen with these small models is such a huge opportunity for their cloud that I think they’re succeeding wildly. But their big models have always not been as excellent as their small models. So I don’t expect their model to be as breakthrough as Kimi K3 or GLM 5.2. I expect it to be covered in the news as major open quite as the open bottle name in China drops giant bottle but I don’t think it will be as sustained as a um news story um DeepSeek you can go if chime in whatever

00:21:01 Florian Brand: the the interesting thing is don’t know how how much you follow this but they are have or they have an endpoint which you can use for a preview version and they’ve updated this endpoint daily so they have some really fast iteration cycle because we the we progress in all these um Twitter um benchmarks. So a lot of these SVG things and three.js like all these visual generation tasks the model has been improving a lot over the last few days. So they have figured out some kind of fast feedback mechanism um which other companies have as well. Uh we we know this or cursor has a lot of blogs about this how they iterate really fast. Um but they seem to continuously upload new checkpoints and make them available.

00:21:47 Nathan Lambert: Um but I agree. I’m guessing it’s like a time gated within their final RL run. It’s like still slightly improving at the end of their RL run and they’re just like checking the box.

00:22:03 Nathan Lambert: Okay. Qwen DeepSeek V4 is supposed to come out a preview version. Um the thing about DeepSeek V4 I think is that the flash model is actually way more popular which is their smaller which seems to be an absolute workhorse for people. So that I think is the model to watch for them. I don’t expect V4 Pro to be a dramatic breakthrough. This is similar to anything like if Xiaomi were to release a new MiMo Pro model soon. I don’t expect it to be as big of a drop but it would probably be a very solid model. It’s just like it’s hard to know. They’re still a pretty new entrance. MiniMax, I think, is playing a different game. I don’t think MiniMax is chasing this um Kimi/GLM moonshot to AGI type vibe.

00:22:46 Florian Brand: Oh, I would, I would disagree there.

00:22:49 Nathan Lambert: You think, Do you think MiniMax is still in this?

00:22:52 Florian Brand: Yeah, I I I I think they they are seeing the tension especially because they are a public company similar to GLM and if you look at the stock performance RIP those stocks in the last few days um it it it it make it seems to make a huge difference and the interesting part will be uh the license because they’ve changed the license a lot uh to be more and more restrictive and um if there’s now a change of heart again after the Xi, uh, speech.

Uh it will be interesting to see whether MiniMax goes back to completely open licenses. It’s also an interesting thing to see um which license will be the license for for K3 because they have said they will open source it but I don’t think they have done any commitments in terms of the actual license where you put on top.

00:23:45 Nathan Lambert: Yeah. I mean that’s it’s super important is the thing. Yeah, we we’ll see. Um, Ling, Meituan, LongCat kind of similar, very strong models, probably getting a lot of value out of them internally. Aren’t don’t have the same developer breakthrough. Um, so what that’s like seven seven to eight Chinese labs. I might have forgotten some. And we can also talk about US labs. Aside um, Gemini 3.6 flash dropped. It looks fine. It’s like it’s like it’s it’s a tiny bump. It’s faster. It’s less of a yapper, but like doesn’t really matter. We’re going to stop we’ll stop sharing this. Um that’s that’s the amount of mention that Gemini gets for us.

But I do think it’s worth talking about the US ecosystem a bit. I think there are emerging players. Thinking machines released their first model. I’ve talked to some of them. they’re very on board for figuring out this how to make a fine-tunable model with Tinker and I think that’s a research area that I really really recommend for most of the open model builders. I think if you can get mind share there you will get massive adoption because it’s more about being fine-tunable for real tasks than it is about having that be best best numbers. Um, so this was their Inkling model which is a one trillion parameter which has like decent but not frontier scores.

I think kind of like DeepSeek V4 they’re going to they’re planning to release a smaller which is like a quarter of the size in total parameters which has really really good performance and if Inkling small preview comes out in a few weeks I do think that that will be a really used model. It’s a good size for kind of automating tasks and kind of domain specific tasks and might not be a like general agent type thing like Kimi and GLM 5.2 but I think that suits their business really well. Um I know that there’s some other the I would say like the smaller players in the US seem well like Arcee released their models earlier this year still chugging along. Poolside has started releasing some models.

They’ve gotten a few in the last few months and seem poised to release more models on top of that. So they’re really going Reflection is perpetually in the model coming soon camp and it really behooves them to get some models or some code or something out so that they can just start getting the developer flywheel going if they’re really committed to open source. It just takes a lot this it’s hard to get the models out. Like I talked to some people at Thinking Machines and it’s like kind of like oh that’s a lot of it’s a lot of work to actually do this I think. And um Nvidia chugging along. I think they’re at the stable player at this point. They’re keeping to release models. They’ll release more soon. They release a lot of data. I’m bullying them to try to get them to release Qwen style small models, which is like Gemma.

Gemma only has these like Qwen competitor models that are super popular. Um, the Gemma models are a little they’re all over the place in sizes or in architectures for the sizes and things like this, but the Gemma models are really really matching the Qwen models in terms of adoption. Um, I’m not sure they’re as easy to use for research, which could take a while. It could take multiple iterations. Like so much of language model research is now designed around small Qwen models and Qwen-based models that like it takes a while. Like people know how to use these models really well and if with the research results. So I hope Gemma keeps coming and can kind of compete in that niche. I don’t know any anyone that I missed here.

00:27:22 Florian Brand: No, I think both are the big players. Uh it’s, it is becoming broader. Uh in terms of model creators like last year, did we have any release aside from Gemma 3 and um GPT-OSS?

00:27:41 Nathan Lambert: was GPT-OSS 2 would go hard and obviously and obviously Nemotron as well. Um, oh, and I think Llama 4 at the start of the year, but uh, I don’t want that to be forgotten, but we are seeing like more players are are are now joining and turning out models at a really incredible rate.

00:27:59 Florian Brand: like Poolside has been releasing three or four models in the last two or three months. Uh and they seem to have figured out some way to turn out models pretty consistently. Um and that’s also something we are seeing on the open source side as well. we are talking about GLM like I think their iterations uh times for the model releases are now between 1 or 2 months with each new iteration becoming better and better which closely resembles what the closed labs are doing like we get a new GPT we get a new Claude every uh 6 weeks or so these days uh so in terms of having uh good enough pipeline uh to release stronger and stronger models they have to or the open source ecosystem has really figured it out or seemingly figured it out.

00:28:59 Nathan Lambert: Yeah, I agree. It’s it’s promising, but it is also so funny that like the US ecosystem started releasing some models and then then you have like Xi on the mic and these two models. It’s just like it’s so hard to catch up because it takes a lot of institutional expertise to train models that people actually use. And I think this is is what the American companies that are releasing models are now realizing is like these are not just benchmaxxed distilled IP theft models.

These are like genuinely good models that people are comparing to on their internal trading benchmarks and then like seeing how hard it is to beat them on measurable things. And I think that that is like I I’ve I’ve picked this sentiment up from a few people in the US trading models and it is just like there’s some I I think people should innovate on like size and fine-tunability and try to like use this potential market that is really close to home but also the pressures for every company is so high to release a model that you can claim as Frontier. I think investors expect that out of so many of these players that they’re kind of trying to do a a pretty hard thing and it’ll be interesting how the next year unfolds for the US China balance.

00:30:25 Florian Brand: Yeah, I think or in general I and a lot of other people have talked about the general ecosystem and that’s also something you’ve talked about at the very beginning. I think we are seeing more and more of a split between the capabilities of models that is good enough for a lot of tasks like uh for a lot of coding tasks the current frontier models both open and closed are good enough. um improvements feel less and less uh important here.

But if we look at the frontiers frontier, so finding new math proofs, finding uh new uh cures, finding new drugs, and inventing new things, that seems to be a whole different beast and probably will be dominated by the very frontier for quite a long time. The big question then becomes how much does that matter uh in terms of the addressable market and also how much of a focus will this be. I think, or my general base case is that we are seeing the frontier close down more and more. We have seen this with Mythos for cyber security GPT... or for biotech that those models won’t be accessible for everyone um and maybe not even external partners if we consider the reports that Anthropic is now spawning or or creating some internal labs to develop drugs.

Um so the very frontier is inaccessible for everyone and then the near frontier capabilities is becoming more and more commoditized um which has a lot of different implications especially if you think about things like uh cyber security. There was that report from Hugging Face two or three days ago that they had some agent trying to to hack their system. um and they tried to analyze it with GPT and with Claude but were unable to because all the guardrails blocked them. So they had to use GLM, a lesser capable model, but it had no guardrails for this kind of defensive action. And they had to use a worse model to defend themselves or to analyze the data, which is a horrible state to be in that we have US-based companies now relying on lesser models because the closed frontier is inaccessible to them.

00:33:10 Nathan Lambert: Yeah. And I think this is actually one of the best arguments for not doing anything. It’s like if the rest of the world has access to these open models and we ban them for the companies in the US to use and it’s just like a growing disparity between US companies ability to defend and the attackers all over the world in terms of cyber and we could debate like how much of an immediate risks the cyber stuff is at the current capability levels but if you’re setting it up structurally so that the defenders get don’t get better over time and the attackers can like that seems like when the Why would cyber risk become more real? And that would to be very clear that would be if you ban the best Chinese openweight models from being used at companies in the US.

And this ban would likely be a kind of shadow ban, which is the threat of legal threat of legal action or punishment without it being clear on exactly what the pathway to do it is. And there are a lot of talks about this right now. I don’t like like I don’t know if we’re going to have a ton to say about this, but it’s clear that DC is flirting with different ways of restricting the best Chinese openweight models in the US. This is I think downstream of some fear-mongering. We’ll transition into the distillation question too. It’s like all these things from the primary AI media narrative in the US that is pointing towards Chinese models as stealing IP or being dangerous or being affiliated with the Chinese government, an authoritarian government.

And it’s like all these things are leading up to this moment of interest in taking action on AI and then not really knowing where to do it. So potentially taking a crude instrument to the like quote unquote enemy and we could transition into distillation. I think there’s a lot of discussion on it. Most recently Ben Thompson finally chimed in on distillation. I think Ben is probably one of the is probably the highest read blog in tech (Stratechery). I think that the the debate let’s see where do we even start the debate. The core question is like how much does distillation help and what should you do about it? I’ve been of the opinion that distillation has becoming less and less impactful over time as the Chinese models get closer to the frontier and the trading regime shifts to RL. The way that distillation tends to happen is that the Chinese labs hack the APIs. Hack is like maybe a strong word, but they jailbreak the APIs of Claude and GPT to extract the reasoning tokens.

When you have the reasoning tokens with the tool calls, that is perfect SFT data and or mid-training data to train the base model with to seed some agentic behaviors in an important domain. And now after that the core part of post-training is to do large-scale RL in agentic domains to so like push the frontier and everything that they’re doing today and RL is only becoming more prevalent with this as SFT becomes less prevalent in previous generations you could get very close to the frontier just by scaling up SFT and that would be what really impactful if you could say take a million agentic rollouts from Claude or GPT have that be your SFT set and train on it.

I think in previous years that would have done a lot more to get you to the frontier. What Ben Thompson has said which made me really annoyed is that he very strongly proclaimed that distillation is getting more impactful as you do RL. He did this in his article who’s afraid of Chinese models. We can link it below. It’s a public one. And then he was also on his own podcast tour. He has also podcast as well saying the same things. And I think it’s really important to say that distillation during the RL stage is a lot harder.

What he said was that the kind of grading models that can be used during RL, which is essentially you can have a model check over the agentic trajectory of a roll out and grade different parts on if it completed the reward, what actions it took. And he’s insinuating that the Chinese labs are using Fable and GPT 5.6 and the strongest models to actually do this supervision in RL. The problem is that big RL runs are millions and millions of rollouts. I think Thinking Machines blog post had like 20 to 40 million or something for their final RL run. So to do this on an API like Fable or GPT 5.6 would be insanely expensive and potentially it would probably be a time bottleneck because these models are pretty slow and to be frank might not even give you a performance uplift versus using your own tailored greater model or and many things like this.

And so I just think the argument that distillation is helping more because RL is becoming more prevalent is not grounded in literature that we have today. This is tough for me because Ben’s article also concludes that we should like make terms of service disallowing distillation illegal, which I kind I like want to support his radical conclusion to make distillation legal for US companies, but I can’t support any conclusion that I think is on um infactual mis like misguided information. So, I’m also a fan of Ben. If you’re a fan of Ben and could also nudge him on this, I would you really should because there’s probably one more podcast. What is he going to record it on? Like when does he record Sharp Tech? Thursday.

We We got to get on and get him to correct the record because I I don’t know. I I find it so annoying that the most prominent voice in tech is trying to be an ally for our point of view on distillation is that we should do nothing. Um but like it’s hard. It’s like he has such wide reach that this is now going to be the status quo that we have to debunk which I guess it’s a better status quo than I don’t know actually no it’s not helpful because he’s saying that distillation is more important which means the people who are afraid about that are going to use that as a data point to say that we should take action even if they don’t because they probably won’t agree with his conclusions. I don’t know. That was my rant. Ben, you’re wrong.

00:39:16 Florian Brand: Yeah, I-I do think it is important to to differentiate these phases. Um and especially like there is no doubt that it is used during the SFT stage which is the first stage of or one of the stages for post-training and that’s also where the model picks up its manners like that’s why the models say oh I am Claude because they learn this during the SFT stage that’s where uh this personality is formed but the strong capabilities come during the RL stage which is where the money is spent which is where you need to have a fast enough judge which in the best case just runs in at the same GPUs or very close to your GPUs with smallish or with a fast enough model so you can uh are not bottlenecked by this.

Um and it in terms of impact it is also very hard to say how much impact or how much of a boost the better model SFT data gives you versus a lesser model. Um so if you are able to to have 10 million tokens from the latest Claude model versus two generations behind open model how much of a boost that really gives you if you keep the stage right uh the same and the pre-training stage the same is an open question which I don’t think we will see answered in a paper because then you have to showcase your uh SFT and your jailbreaking capabilities

00:40:48 Nathan Lambert: but I I wanted to double down on this like there’s been a good amount of literature on generating SFT reasoning traces whether it’s the most prominent ones have been opens line of work they did Open Thoughts 3 and Open Thoughts Agent have kind of been the foundational like scaling reasoning SFT works in the last few years and whenever somebody revisits this question they have not found the answer that the strongest model on performance in your domain is the best teacher for SFT people have try I’ve tried many people have tried the idea is so simple is like the state-of-the-art open SFT data set is built on QwQ-32B like an ancient reasoning model or something.

Why can we not just generate completions from GLM 5.2 do SFT on it and improve the model? We don’t know. It’s like the research so many people have tried and it is not an answered research question. There might be something like the base model the mid-training is too close to Qwen. So therefore it’s like hard to break. You have to redo the mid training. I think you have to redo the mid-training for reasoning. I think reasoning mid-training and reasoning SFT are so closely intertwined. It almost doesn’t make sense to have different words for them. That could be the issue. But the literature doesn’t even know how to ext like if I had a magical API that gave me reasoning traces from Claude/Gemini. I actually don’t know if me like fine-tuning an OLMo model on that would make OLMo smarter.

It’s one of the most wild unanswered research questions. And this is just makes the distillation thing so funny where it’s like yes the Chinese labs I think are using strong models like Opus for some SFT data but they’re also innovating. I was like I would love to them to tell us how to make this freaking work. And I think it’s the the paradigm I think is like open AI and anthropic find a niche domain that they do so well at and then the Chinese labs can get some samples there to kind of bootstrap their data engine and that’s where you will gain you will gain a few months on a specific domain.

But a hill climbing on these core domains like math and code and like Terminal-Bench like they’re just doing the same thing which is like so hard to generate prompts which are problems with environments that are hard for the current models and provide real nonreward hacking um learning behavior. And like that is what frontier data research looks like right now. And it is like it’s hard to generate these hard problems. And I’m sure the Chinese labs are doing the same the same things. And I don’t I don’t know. That’s that’s my rant. I’m kind of lost the context of our conversation.

00:43:23 Florian Brand: No, no, I would I would agree. Or to to to recap, yeah, SFT or distillation has some effect. Yeah, it gives them a boost, but not that much uh as people would like or or seem to think it gives.

00:43:39 Nathan Lambert: I think that’s that’s a good good summary of the of the of the conversation. It also becomes kind of tiresome because it says uh that open all all these open models are just good because they are distilling. um which definitely isn’t the case cuz if if it were the case, everyone would would be easily able to catch up to a GLM or to a K3 um by using its data for distillation. But we have not or we won’t see this from SFT alone.

00:44:12 Florian Brand: Yeah, I agree. Do you have any predictions or or more topics you want to get to?

00:44:18 Nathan Lambert: Um in terms of predictions, I think we are or I I revisited uh ours from from last year and it basically said everything will continue uh like it did uh the previous year. Uh we predicted that we will see bigger models uh up over two trillion parameters which it did and I don’t think we will see a much bigger explosion in terms of model size this year. we might see something or some model a bit bigger than three trillion parameters uh total but I don’t expect a five or 10 trillion parameter model and we open this year that would really surprise me um then list from last year can we redo this we don’t have to do the whole thing

00:45:03 Florian Brand: oh sure

00:45:03 Nathan Lambert: this is where we were at the end of 2025 who do you put in frontier now well it is Kimi and it is Zhipu DeepSeek is kind of a hard nut these days. Like I think they would be in close competitors. So I would put DeepSeek and Qwen the one as close competitors with Kimi and Zhipu as Frontier. Do you think anyone else would deserve close competitor? Cuz after that noteworthy and below like there’s so many.

00:45:38 Florian Brand: I I think we will see a surprise from MiniMax by end of the year. I think we will see a big model which like a really big model not uh M3 size but trillion parameters plus which will surprise us in terms of uh the outputs of MiniMax compared to before uh so I would still put them at close competitors by the end of the year

00:45:54 Nathan Lambert: do you think any US companies will be in the closed competitors by end of the year Nemotron I don’t think I would put there Thinking Machines closer especially if the smaller model really breaks through. But I don’t think I would put them there yet. Reflection is supposedly like only wants to release if they have a model that’s frontier. But then the question is will we get it? Like do we think that any US companies will get into this what is roughly like our top five by the end of the year? So the top five are the same but reshuffled.

00:46:36 Florian Brand: I would say it is possible uh that they are really close. Um it it also depends on what we think matters for closeness. Like I think uh Nemotron and um uh Thinking Machines will release models which act as really good base to be fine-tuned for your domain which doesn’t mean they are usable like a frontier model but they have so much utility uh that I would put them into close competitors because you would just need to find your data and uh to push the model into the right direction.

00:47:12 Nathan Lambert: Um as a I was going to think that we would make this a group of six with a US company by then like if we do this in late November I would guess that a US company pro most likely Nvidia thinky or Reflection mo does stuff that gets us to say that there is an American company in this like top cluster which would be a first time for a while.

00:47:43 Florian Brand: Yeah, I I I think that is realistic. My my one wild card is Tencent, which I think we might see something by end of the year. Uh they got some new leadership. Uh they released their Hunyuan model under Apache this time. Wait, so Tencent always had these custom licenses which disallowed anyone in the UK and South Korea and the entirety of the EU to to use their their model and also had acceptance use policy and so on. Um, and with Hunyuan and their new leadership, they got a really competent model at 250ish billion parameters. Um and I think by end of the year we might see a big model release which will surprise the people not following the ecosystem.

00:48:36 Nathan Lambert: Yeah, I I am also sure we will be in for some surprises. This is always the thing with AI and especially open models. It’s very very unpredictable. Okay, I I think this is a good place to stop. We probably should really do this quarterly. It’s not that hard and people will enjoy it. Um, but good to see you and we’ll talk soon. Hopefully in person soon.

00:49:02 Florian Brand: Peace.