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I hope cheaper inference eventually means faster speeds at the lower tiers. I don't want to settle for 100 t/s, but I don't want to pay $10 per prompt either
Which raises the question, which are the fastest frontier models? are the enterprise hosted Anthropic models faster than what Anthropic serves? Somehow no one talks about LLM speed.
I'm not convinced raw costs matter: 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins. 2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples. 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time. 4. Many formerly open source infrastructure…
A lot of those things you mentioned have sticking power because they’re familiar to folks and migrating to something else is a big deal. I can’t imagine most people would be able to tell the difference between Sonnet and GLM 5.2. If the infrastructure around the model you’re using doesn’t change, then swapping models is extremely easy.
I'm not convinced raw costs matter: 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins. 2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples. 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time. 4. Many formerly open source infrastructure…
A lot of those things you mentioned have sticking power because they’re familiar to folks and migrating to something else is a big deal. I can’t imagine most people would be able to tell the difference between Sonnet and GLM 5.2. If the infrastructure around the model you’re using doesn’t change, then swapping models is extremely easy.
Comparing Z.ai GLM 5.2 to Claude Code w/Opus 4.8 is like comparing Linux Kernel 7.0 to Microsoft Windows 11. If you don't know much about computers, you'd say these are the same things. If you know a lot about computers, you know the latter has a thousand extra things that make a huge difference in what it does out of the box. Which one you use speaks to what kind of customer you are.
Sure, GLM 5.2 doesn't have vision; but an AI power user can plumb together any VLM with the text generation of GLM 5.2 in most AI harnesses, just like a Linux power user can combine the Linux kernel with KDE Desktop. Most people don't use Linux and KDE, because it's unpopular, difficult to use, hard to get support for. Instead they pay for Windows or Mac, because there's lots of support, with a giant company pouring money and effort into filling all the usability gaps, making it seamless.
Most people don't pay for the cheapest possible thing. They pay for the thing they can afford that improves their life while making it easier. An open weight alone is almost completely unusable by itself (like the Linux kernel), compared to an AI platform (a completely usable system). If you're constantly wondering about when open weights will reach parity with OpenAI/Anthropic, you're a Linux person. If you just pay $20/$50/$100 for OpenAI/Anthropic without thinking about it, you're a Windows/Mac person. There is nothing wrong with either of these groups, but they are fundamentally different, and always will be. An LLM weight is simply a different category of thing than an entire AI platform/provider.
They have a vision MCP to make up for the model itself not having the capability natively: https://docs.z.ai/devpack/mcp/vision-mcp-server I also found their web search to be mostly okay. Furthermore, in case this is of interest to anyone, if you use their ZCode harness then you get bigger Coding Plan quotas: https://zcode.z.ai/en Used it for a bit, it sits somewhere between OpenCode Desktop (still new but nice) and…
Seems like a pretty pointless post that still centers around output tokens. In agentic coding, cached input tokens is 90% of the API "cost". It doesn't require GPU compute, and DeepSeek has shown that it can be done 50~100x cheaper with MLA/CSA/HCA, and a whole bunch of disks. This should collapse the margin.
Aren’t these techniques all “lossy” compression, and one of the reasons people complain about loss in quality as the context size grows larger?
This article only promises to get into "the coming AI margin collapse" in a yet to be published "part two". This part only makes the point that GLM 5.2 is pretty good (no shit).
> the least understood upcoming shift in AI economics. Then proceeds to talk about something in the AI news every day. Hey, did you guys hear? Open source models are cheaper and their quality is increasing! So, first, by no measure is GLM5.2 as good as Opus. Second, yes, open source models will put pressure on margins...eventually. Everyone knows that. But do you think today's AI business model is the same as tomorro…
> So, first, by no measure is GLM5.2 as good as Opus. Depends what you do. Complex tasks, poorly-defined tasks, sure. For relatively simple tasks, though, or very well-defined tasks, it's just as good and usually a lot faster. It also has a more neutral character and is somewhat less adversarial than Opus. (Opus is always "Let me push back on that..." whereas GLM is "sir, yes sir!") I use both and I appreciate both.…
I hope cheaper inference eventually means faster speeds at the lower tiers. I don't want to settle for 100 t/s, but I don't want to pay $10 per prompt either