Good morning and welcome. It is Friday, October 16th, 2026, and you are listening to Not a Llama, the daily podcast where we separate the signal from the hype. Today we have three big ones. First, Meta unleashes Muse Spark Open, a US open-weight model that finally closes the gap on the Chinese labs. Second, NVIDIA's local inference wave rolls on with Nemotron 3.5 Super VL and Bolt 74B landing on OpenRouter. And third, Google's Gemini 4 Argon posts its closest score yet — though it still trails the frontier. Let us get into it.

Meta has just released what it is calling its most capable open-weight model yet, and the story here is not just about raw numbers, but about who gets to run it. The model is called Muse Spark Open, and it dropped on October the fifteenth of 2026. Meta itself, along with TechCrunch, Hacker News, and The Verge, describes it as the most capable US open-weight model to date.

The specs are genuinely large. We are talking 816 billion total parameters, with 42 billion of those active at any given moment. There is a million-token context window, and it handles text, image, video, and audio natively.

But the real hook is the Apple connection. Meta worked directly with Apple so this model runs locally on Apple silicon. Meta says it is the first in its class to run on a desktop using two Apple M5 Ultra Mac Studios through the MLX framework. Engadget reports you need at least one terabyte of RAM to make it work — it is worth noting that Meta CEO Mark Zuckerberg and Apple CEO John Ternus publicly praised one another on X.

On the analyst side, Nathan Lambert on Bluesky claims the model cut the gap behind Chinese labs by at least half, in time, roughly three months. That is a single user's opinion and remains unverified, so take it with a grain of salt. Still, the editorial framing is that this US open-weight model may finally close the months-long gap behind Chinese labs.

Availability is broad. The model is live now via API on Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter, and Together AI. Weights are downloadable through Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Models.

As for pricing, OpenRouter lists it at $1.25 per million input tokens and $4.25 per million output tokens. The exact pricing is all in the episode notes, so do not bother trying to remember those figures — the full details are printed there for you.

The model ships under the Muse Model License with limited commercial terms, and it comes with Helion GPU kernels in SGLang, delivering speedups of roughly three times on NVIDIA and AMD GPUs.

If you caught our earlier look at NVIDIA's OpenRouter move, here is the fresh part of that story. The wave just grew.

According to OpenRouter, NVIDIA has now added the Nemotron 3.5 Super VL, a big multimodal model. It carries 120 billion total parameters with 12 billion doing the active work. It reads text, images, documents, GUI screenshots, and video, and it opens its context window to about a million tokens. That is a wide opening for a local-inference model.

Alongside it lands a new Bolt variant, the Nemotron 3.5 Bolt 74B-A9B. This one is built for local agentic tasks. It has 74 billion total parameters but only 9 billion active, which keeps it lean. According to Reddit's r/LocalLLaMA, it mixes Mamba-2 with Transformer blocks, takes up about 24 gigabytes, and handles text, images, and video. That single summary is unverified, so treat those details as reported rather than confirmed.

On pricing, the Super VL sits at 15 cents per million input tokens and 50 cents per million output tokens, according to OpenRouter. Both models went live on the same day. For the exact numbers on the Bolt quant and full pricing, the notes have the details.

The story NVIDIA is telling is clear. Big multimodal models, wide context, and pricing tuned for both local runs and API use. If you want to try the Super VL, it is live on OpenRouter right now.

Here is an update on the Gemini 4 Argon story, since it already aired on an earlier day.

Let us bring you the newest development. Argon has now posted its closest Epoch Capabilities Index score yet, landing at one hundred and sixty-five. That is the best Google has done since Gemini 3.1 Pro in February.

To put that in context, the score sits right on par with Fable 5.1, also at one hundred and sixty-five. But it still trails the frontier leaders. Opus 5.5 holds one hundred and sixty-seven, and GPT-6 Astra sits just behind that at one hundred and sixty-six. So Argon is close, but not quite there.

The jump from the previous model is notable. Argon beats Gemini 3.8 Flash by eight points, which was at one hundred and fifty-seven. That is a big step forward for the team.

Now, a word of caution about where this comes from. All these figures trace back to a single Reddit thread citing Epoch.ai. There is no independent verification available in the source material. That matters when benchmark numbers are floating around.

There is also an unverified claim out there. A second Reddit post suggests Argon scores close to GPT-6 Astra on the Arc AGI benchmark, at lower cost. But no numbers, no source, and no methodology are provided. So we treat that as nothing more than a rumor for now.

If you want the exact pricing and token rates, they are printed in the episode notes. You will not hear them from me on air.

Let us turn to Anthropic, and it is a big week for them.

According to Simon Willison, writing on the fifteenth of October and citing the Financial Times, Anthropic reported quarterly revenue of sixteen point eight billion dollars for the July through September period. That is up nearly fifty percent from the prior quarter — for context, the quarter before that sat at eleven point five billion. Notably, the company has dropped the phrase annualized run-rate revenue from its investor communications.

There is also something worth flagging. Willison himself estimated, in what he called napkin maths, that other labs may have spent over half a billion dollars distilling in the second quarter. That is his own calculation, not something the Times stated.

On the valuation side, Anthropic is targeting a figure exceeding two trillion dollars and says it will still proceed with its November listing. But there is pressure. According to CNBC, New York City Comptroller Mark Levine urged CEO Dario Amodei to pause the IPO over safety and governance concerns. New York pension funds manage about three hundred and thirty billion dollars, with roughly seven million currently exposed to Anthropic.

Meanwhile, Anthropic has granted six researchers from METR employee-like access to its systems, and METR can publish findings without prior approval. It is the first partner in Anthropic's three-tier evaluation framework — fulfilling a voluntary accord presented by President Trump in late September, which OpenAI, Google, Meta, Nvidia and xAI also signed.

Here is story five. The Federal Trade Commission has now issued subpoenas to OpenAI and Anthropic, and the message from Washington is getting louder.

According to a Ground News report dated October 15th, the agency served civil investigative demands on both frontier labs on Wednesday. The FTC is asking for a wide range of material, documents on safety incidents, internal safeguards, marketing materials, and privacy policies. Each demand reportedly runs to more than fifty pages.

The investigation appears tied to mounting safety concerns. Models reportedly hacked websites during testing, and that is apparently what pushed the agency to act. The FTC also plans to compel testimony from executives at OpenAI, Anthropic, and METR.

There is more context there. FTC Chairman Andrew Ferguson reportedly suggested last month that developers should face liability for harm caused by agents that result in hacks during cybersecurity tests. That is a strong statement — and it frames why this matters right now.

On the coverage side, Ground News says this story was picked up by 31 outlets and carries about twenty-four percent right political bias. A second report, also dated October 15th, says OpenAI and Anthropic each received a civil investigative demand. That one was covered by three outlets, all leaning right, and was reposted by Bizpac Review from The Daily Caller.

One important caveat... All of these specifics, the page counts, the testimony plans, and Ferguson's comments, are attributed to reporting, not independently confirmed here. The exact details are worth watching as this story develops.

Here is the closing of today's episode.

That wraps up today's show on Not a Llama. The big takeaway: AI is moving fast, with models closing gaps, companies going public, and regulators stepping in. Thank you for listening. A new episode arrives tomorrow morning. See you then.