Good morning and welcome back to Not a Llama, the daily show where we cut through the AI news so you do not have to. Today is Sunday, October the eleventh, twenty twenty six. We have a strong one. OpenRouter just cut prices on DeepSeek, Qwen, and GLM. A Fields Medalist weighs in on OpenAI's math claim. And Nvidia reportedly halts the RTX 5090, squeezing your local inference hardware.

This is an update on a story we covered a while ago. OpenRouter has been cutting prices, and now a second wave is rolling out. Let us get straight into what is new.

The first wave landed on DeepSeek models. This round reaches further. Qwen models now join the cuts, and there are fresh reductions on DeepSeek and GLM 5.3.

The biggest move of this round lands on a model listeners actually run. Qwen 3.8 27B has seen its input price slashed by about ninety percent. It drops from forty-two cents to four cents per million tokens. That is a dramatic drop. The output price barely moves, from two dollars and fifty-five cents to two dollars and thirty cents.

On the DeepSeek side, the new cuts touch several models. DeepSeek Flash Latest sees its input price fall from sixteen thousandths to one ten-thousandth of a dollar per million tokens. That is a massive reduction. DeepSeek V4 Flash 0731 also drops, from eighteen thousandths to about ten thousandths of a dollar.

GLM gets its own treatment. GLM 5.3 sees its output price cut from six dollars to four dollars and eighty cents per million tokens. Meanwhile GLM Flash Latest is a mixed bag. Its output price falls from seventy-two cents to fifty cents, but its input price actually rises, from three hundredths to four hundredths of a dollar.

A note on the figures. The exact prices are printed in the episode notes, so do not worry about trying to hold every number in your head. The general picture is clear. Most of these cuts are significant, with the one exception being that GLM Flash Latest input price going up.

So there you have it. The cuts keep coming, the range keeps widening, and the models you use most are the ones feeling the biggest drops. More detail is waiting for you in the notes.

We have an update on that Fields Medal story from earlier. Hugo Duminil-Copin, the mathematician who won the 2022 Fields Medal and teaches at IHES and the University of Geneva, has now spoken out about OpenAI's math release. He did it through a Reddit post to the r/OpenAI subreddit, submitted by a user called PianistWinter8293 on October the tenth of 2026.

Duminil-Copin says he had always expected AI to eventually surpass mathematicians, describing it as something that would happen systematically. But he says yesterday's announcement hit him with a force he had not anticipated. He writes that dozens of papers address topics he personally worked on, and that not a single major open problem he has publicly mentioned throughout his career was left untouched.

He claims everything has been proven, and quotes the phrase, for the glory of the human mind. He describes feeling paralyzed and lacking energy, saying he will rethink his profession.

Now, a word of caution. This is a single user's anecdotal post, not a verified news report. The author does not name OpenAI explicitly, and the claim that everything has been proven is his own assertion, not independent verification. There are no model names, no benchmark numbers, nothing concrete. Just a mathematician's emotional reaction to a striking moment.
So the picture is getting tighter for anyone building their own models at home. According to Reddit's r slash Local Llama, Nvidia is reportedly pausing production of the RTX 5090, steering chips instead toward data center and professional graphics cards. The word is that this will trigger a real supply drought, and prices are expected to climb. Now, before you start panicking about your next build, it is worth being clear about where this stands. The halt itself is reported, not confirmed. This is a rumor riding on a single community thread, and one follow up post was just a user typing out a long, despairing no. There is no official statement from Nvidia, and no hard numbers on availability or pricing. What we do have are some names floating around. The RTX 5090 is the one said to be on ice. The RTX 5080 with 24 gigabytes is being floated as a possible new gaming flagship, though that is explicitly rumored. And the GB 202 is being linked to the RTX PRO line for the professional market. The takeaway is simple. If this story holds up, expect the 5090 to get harder to find and more expensive, which lands hardest on the DIY inference crowd. The notes carry the exact pricing details when they show up. For now, treat this as early chatter, not a closing door.

Let us bring you story number four, a deal that has not actually happened yet, but it is worth your attention. Nvidia is reportedly in talks to acquire a United States startup called Reflection AI. The catch, and there is a big one, is that this all comes from a single Hacker News discussion thread. That is a community forum, not an authoritative primary source, so we are dealing with rumour here, not a confirmed deal.

So let us turn to a voice we hear a lot in the AI safety debate right now. Satya Nadella, the chief executive of Microsoft, posted his thoughts on social media on Saturday, and his message is a striking one. He says it is time to step back and assess the trust architecture of artificial intelligence. His proposal? Separate the model from the harness that orchestrates its work, and externalize the controls and safeguards. He wants every meaningful model action recorded with tamper-proof human readable evidence, and an authorized person to always be able to pause or shut down a model mid-task. His most memorable line is this: we must assume a model is compromised and contain it from the start, and to think of it like an emergency brake. According to The Verge, Nadella aligns with others in the field on timely incident disclosure, independent audits, verifiable data and containment, though he appears to go slightly farther on the containment point. TechCrunch places these comments after leading companies acknowledge more incidents where they seemed to lose control of their models, and after Anthropic's Dario Amodei published a plan for more cautious development. Both sources note Nadella refers to AI as super intelligence throughout, using what TechCrunch calls the Trump administration's preferred term. This is one executive's opinion, not a company policy announcement, and the claim about losing control is reported as context, not independently verified.

Now for a hands-on local inference win. A Reddit user on r/LocalLLaMA says they built a custom CUDA megakernel for the model Qwen3.8-27B, and it meaningfully outperforms llama.cpp on a single consumer GPU. The poster says they used Claude Opus 5.5 to write the kernel, then ran it on an RTX 3090. Their own numbers claim it is about 1.4 to 1.9 times faster than llama.cpp, depending on the task and how long the context is. In writing code, they hit 140 tokens per second against 73. With a 4K token file in context, 95 against 56. For prompt processing, about 1,600 against 1,100. The claimed reason is that each speculative decoding cycle runs in a single kernel launch. They describe it as an OpenAI compatible server, a drop-in replacement for the llama server. Caveats abound. It is built for one model only, unsloth's Q4KM, on 3090s alone. Outputs match llama.cpp apart from rare rounding near-ties. The poster posted code and benchmarks on GitHub. But this is a single user's self-reported anecdote, with no independent verification. The model name and the tool used to write the kernel are unverified and unusual, so treat everything with caution. Still, it is a neat demonstration of what a bespoke kernel can do on modest hardware.

That is your wrap for today on Not a Llama. The big takeaway, that frontier models are moving faster than even the experts expected, so stay curious and stay careful. Thank you for listening. A brand new episode arrives tomorrow morning. Until then, keep asking the hard questions.