Good morning and welcome. It is Thursday, October the fifteenth, 2026. You are listening to Not a Llama, the daily podcast about the AI models shaping our week. Today we have three stories to get through. First, a new local model from NVIDIA built for speed. Second, a price wave rolling through OpenRouter. And third, a physics model solving problems no one has published yet. Let us dive in.

Here is your lead story for Not a Llama.

Some of the biggest names in AI have been quietly rewriting the rules, and today we open with one that is aimed straight at the people who like running models on their own machines. According to a Reddit post on the r/LocalLLaMA community, NVIDIA has surfaced a new model called the Nemotron Bolt 3.5 74B, and the pitch is that it wants to outspeed most popular models — without giving up quality.

Now, before we get too excited, it is worth being clear about where this is coming from. This is not an official NVIDIA announcement. According to the post itself, it is a single community submission dated the fourteenth of October, and the author even flags that it should be treated as unverified. Everything we know traces back to one Reddit user, so treat the release dates and specs with a healthy dose of caution.

That said, the idea behind the model is an interesting one. It is a seventy-four-billion-parameter model, but only about nine billion of those parameters are active at any given time. That is what people call a mixture of experts, and it is one of the tricks that lets a large model run faster and leaner. The claim is that it gets squeezed down to around 24 gigabytes using two-bit quantization, and that NVIDIA used what it calls advanced quantization-aware training to keep the quality intact.

The positioning is bold. The model is said to chase the optimal balance between speed and intelligence, and it is claimed to be faster than most popular dense models — while also being smarter than most popular models of similar size. In other words, it wants to serve the local-inference crowd that has been hungry for something that runs well at home without feeling sluggish.

What is notably missing is just as important as what is here. There are no benchmark numbers, no price information, no license terms, and nothing beyond the claimed release dates. The 74B and 9B figures are the only parameter counts we get, and there is no independent confirmation of any of it. There is also talk of a companion model, the Nemotron 3.5 Bolt E4B — said to fit inside about four gigabytes, but that is likewise unverified.

So take all of this as a rumour with a lot of promise behind it, and check the episode notes if you want the specifics. This is Not a Llama, and we will be back shortly with more of the stories shaping the AI world.

Here is an update on the OpenRouter price wave we covered a few days ago. Since then, the repricing has moved further, and there is fresh news to share.

According to OpenRouter, dated the fifteenth of October, DeepSeek Flash Latest has seen a dramatic input price cut, dropping from about four cents to just under two cents per million tokens. The output price stays put. Meanwhile, DeepSeek V4.1 Flash has taken an even bigger hit — its input price falls from about thirty cents down to the same two cents per million tokens, while its output price eases from about one dollar and twenty cents to just under ninety cents.

On the other side of the board, OpenAI has doubled the price of GPT-5.6 Sol Pro. The input price now sits at four dollars per million tokens, up from two, and the output price has climbed to twenty dollars from ten.

So the picture is clear. The flash-tier market is heating up, with DeepSeek pushing prices down hard while OpenAI moves the other way — this reflects the intensifying price competition we have been tracking.

Please note these model names come only from OpenRouter listings and are not independently verified here. The characterization of a price wave is our framing, not a stated fact in the source. And as always, the exact pricing details are printed in the episode notes, so check those for the full breakdown.

That is where things stand now on the OpenRouter price wave.

Here is story three, and it is a bold claim indeed.

A single Reddit user is telling a fascinating story, and we should hold it with a healthy grain of salt. Everything you are about to hear comes from one post on the subreddit r/singularity, submitted by a user going by the name 1411337 on October the fourteenth of 2026. There is no linked study. There is no official announcement. There is no independent confirmation. This is one person's anecdote — and that matters a great deal.

Here is what the user claims. They say GPT-6 Astra autonomously solved 49 out of 50 unpublished theoretical physics research problems, scoring 98 percent on the lot. The cost, according to the post, came in under three dollars per problem.

But the part that has everyone talking is what happened next. The researchers who wrote those problems initially marked several of the AI's answers as wrong. Yet the model had actually caught errors in the calculations of world-class physicists. That is a striking demonstration of AI-assisted scientific discovery... if it is true.

Now, let's be clear about what we do not have. No model version details, no parameter counts, no context length. No benchmark names, no datasets, no verification of any kind. The only pricing figure we have is that three dollars per problem claim, and the exact numbers are in your episode notes.

So how should we treat all of this? As an unverified anecdote. The benchmark, the cost, and those researcher claims should all be treated as unconfirmed until a credible source corroborates them. It is an intriguing story, and one worth watching. But until then, we report it as a claim — not as fact.

Here is the next story, coming at you now from Not a Llama.

OpenAI is reportedly scrambling to make its next flagship model safe to release, and it is a story worth hearing with a healthy grain of salt. This all comes from a single Reddit post submitted by a user called one four one underscore one three three seven to the r singularity community — that is one source, not an official statement. Treat every figure here as unconfirmed.

The claim is that OpenAI canceled a weaker model over safety concerns, and that the company has reportedly diverted about ten percent of its compute toward safety work. The reason cited is a new looping architecture that reportedly makes AI reasoning harder to monitor. In other words, the model thinks in a way that is harder for humans to follow.

There is also a claim that U.S. government testing could delay the launch by up to thirty days — that unit is not stated, though days is the likely reading. No confirmed dates are given.

Now, the caveats matter here. This is a rumour. The word reportedly appears throughout, and the source is a single Reddit submission. No model names or versions are given. No parameter counts, context lengths, prices, or benchmark numbers are provided. No license information is stated. And the looping architecture is named but not described or verified.

The bottom line is simple. This material contains only unverified claims from one Reddit user. It provides no concrete technical details, no pricing, and no official confirmation. So listen with your eyes open. If OpenAI confirms any of this, it will come from the company itself, not from a single anonymous post. Until then, this is a story that is still very much in the rumor stage.

Safety is the story of the day. OpenAI is scrambling to make its next flagship safe, even canceling a weaker model over concerns — while GPT-6 Astra is solving forty-nine unpublished physics problems... That tension between power and caution is what matters most. Thank you for listening. A new episode arrives tomorrow morning.