Good morning and welcome. It is Saturday, October the seventeenth of two thousand and twenty-six, and you are listening to Not a Llama, the daily podcast about the AI models reshaping our week. Today we have three big ones. First, a powerful open-weight reasoning model from 501B just landed on Hugging Face and OpenRouter. Second, a new diffusion model is reportedly hitting nearly three thousand tokens a second on a single graphics card. And third, Elorian has just launched a model built to reason with images, not just talk about them. Let us get into it.

Today we open with fresh news from the open-weight world. Reflection has just launched Beam 1.0, a reasoning model that generates text from text input — a bit of a mouthful. It is now live on Hugging Face and OpenRouter as of October the sixteenth.

According to the developer, this is a sparse mixture of experts with about five hundred one billion total parameters, but only roughly twenty three billion active at any given moment. That design is meant to keep things fast and cheap while still handling the kind of long reasoning tasks that usually demand far more compute.

The headline feature is token-efficient reasoning. Reflection claims Beam uses about three times fewer inference-time reasoning tokens than leading open models. That is a developer claim, not independently verified, so take it with a grain of salt. Still, the idea is compelling for anyone running agentic workflows or coding tasks that stretch over many steps.

On the practical side, support for llama.cpp has already been merged, which means local runners can pick this up without waiting around. The model ships in a few precision formats, including BF16, FP8, and NVFP4, and it carries a one million token context window, developed mostly for English.

Reflection says the model targets software engineering, code generation, agentic tool use, and math and science reasoning. That is a clear bet on frontier-competitive performance without the frontier price tag.

As for cost, Beam is priced at one dollar per million input tokens and three dollars per million output tokens. The notes have the exact pricing if you want to crunch the numbers yourself. And worth noting, no third-party benchmark numbers are provided in either source — so the real-world claims are still waiting to be tested.

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A new diffusion language model is making waves in the local AI community, and it might just change how you think about running these models on your own hardware. According to a Reddit post on the r/LocalLLaMA subreddit, dated October sixteenth, twenty twenty six, a research team has unveiled something called ORL. That stands for On-Policy Residual Learning, a fresh method for diffusion language models, and they have also released a custom inference engine called dlmrt. The team says it is fully open source, having shared the models, training code, evaluation pipeline, and the engine itself.

Now for the numbers, because this is where things get interesting... The authors report a speed of 2,788 tokens per second on a single RTX 5090. That is using their LaDA 2.1 mini model combined with ORL. They also claim up to 6.9 times higher end-to-end throughput on their QwenDLM 4B model, and say it runs 1.74 times faster than DFlash speculative decoding at matched accuracy.

But the authors are quick to add caveats — these figures cover single request decoding, not aggregate throughput across many concurrent requests. They are peak results on specific benchmarks, not averages across arbitrary prompts. And these are the authors own reported numbers, with no independent verification provided.

Here is story three.

A brand new model just dropped, and it is trying to do something different. Elorian is a Palo Alto startup, and its first AI model is called Glimpse. It launched Friday, according to TechCrunch, and it is built to actually reason with images, not just talk about them.

The company claims Glimpse beats GPT-6 Astra on scientific chart reasoning and short-form video captioning. But that is Elorian's own benchmarking — not an independent test — so take it with a grain of salt.

The idea behind it is interesting though. Glimpse can crop regions, pull video frames, and even run code in the middle of its thinking. Elorian calls this a visual scratch space. The team says it trained the model on just a few hundred GPUs, and that it is substantially smaller than the big frontier systems it is being compared against.

Elorian raised a 55 million dollar seed round at a 300 million dollar valuation before ever shipping a product. Nvidia and Jeff Dean were among the backers. The CEO, Andrew Dai, is a former Google DeepMind researcher, and he shared a personal story about a model failing to figure out who was winning a board game he photographed. 

Glimpse is out as a research preview with API access. The chat interface is still waitlist only.

Here is story four, and it is a clever little idea... Imagine an AI that thinks in a secret language, keeping its heavy reasoning hidden away, and only breaking into plain words when it needs to call a tool or hand you the final answer. That is the heart of a new paper on hybrid reasoning agents, from researchers at Stanford, Together AI, UIUC, and Meta.

According to a summary on the Reddit forum r/LocalLLaMA, this approach does three things at once. It reportedly lifts test scores by about ten percent across math, science, and medicine. It uses half the tokens of a normal model. And it runs twice as fast — that last part is the real draw, because fewer tokens means much lower costs.

But there is a catch, and the forum users are quick to point it out. The whole method only works if you already trust the model behind it. On a brand new model you have never seen, you might not want to keep the reasoning hidden.

And a word of caution... All of these numbers come from a single forum post, not the paper itself. Nobody has independently confirmed the ten percent gain, the token savings, or the speed boost. The notes carry the arXiv identifier for those who want to dig deeper.

Here is story five.

China is tightening its grip on artificial intelligence, but with a curious exemption. The Ministry of Commerce and the Ministry of Science and Technology published a draft revision to its export control catalog on October the 16th, according to Reddit's r/LocalLLaMA. The draft adds forty technologies in total, and twenty-three of those are AI-related.

The AI categories cover foundation models, intelligent agents, computing clusters, and information processing. Ground News reports the measures could target models exceeding two trillion parameters, citing national security. This comes amid intensifying competition with the United States, which has restricted China's access to advanced AI chips.

But here is the twist — technologies already released through open-source or academic publications are explicitly exempted. So the models already out there in the wild stay free to export. Providing access through APIs, apps, or websites is generally not treated as an export either.

Open-sourcing, and Chinese firms building their own overseas compute centers, are eligible for streamlined automatic licensing.

Public comments stay open until November the 15th, while temporary pre-export registration takes effect immediately. Analyst Lian Jye of Omdia calls the move a response to Western sovereignty requirements. It is a careful balancing act — locking down frontier power while keeping the open-source world wide open.

Here is an update on the OpenRouter price wave, since this story aired earlier.

OpenRouter posted new pricing changes across DeepSeek, Kimi, Llama, and NVIDIA Nemotron models on October the seventeenth of twenty twenty six. Let us walk through what shifted...

DeepSeek Pro Latest saw its input price rise slightly, but its output price dropped sharply, from eight dollars to under seventy five cents per million tokens. DeepSeek Flash Latest followed a similar shape, with input creeping up and output falling from sixty cents to about fourteen and a half cents.

The headline moves came from DeepSeek V4 Pro. The August thirteenth version had both input and output prices halved, landing at sixty six cents and one dollar ninety eight cents. The earlier April version took an even steeper cut, with input falling to about twenty two cents and output to roughly forty three cents.

NVIDIA Nemotron 3.5 Super VL dropped both input and output prices. Kimi K3 edged down on both figures. And Meta moved twice, with Llama 4 Scout and the 70B Instruct model each seeing sharp reductions, the smaller model cutting input to about ten cents.

All eight changes landed on the same day. The notes carry the exact pricing if you want the full breakdown.

Today was all about reasoning. From open-weight models landing on Hugging Face to diffusion models pushing thousands of tokens a second — the story is clear: AI is getting faster and smarter at thinking things through. Thank you for spending your evening with us. A new episode arrives tomorrow morning... Until then, keep exploring.