Chronicles. Aug. 09 - Aug. 16 2026

Over the last week, we’ve seen some announcements from both the highest-end and lowest-end sides of open-weight models. At the same time, labs are starting to think that maaaybe, just maybe, we move too fast and we need to stop and think. So, in their free time, they are starting price wars.

Models, hi

Last week we saw a bunch of releases of very large open-weight models. Z.ai shipped GLM-5.3, which is relatively small, just 743B, DeepSeek published their 1.6T V4-Pro weights under the MIT license, and Alibaba surprised with a whopping 2.4-trillion-parameter Qwen3.8. Although they are open, doing anything meaningful with models of this size requires hardware not a lot of individuals have, which defeats their openness a little. On the other hand, they open possibilities for 3rd-party hosting and incentivize the price wars I will talk about below.

If we look at the benchmarks, we will see that those open-weight models are getting closer and closer to the capabilities of frontier models, such as GPT-5.6 Sol and Claude Fable. It is especially important, because…

OpenAI deliberately slows down

OpenAI has reportedly slowed down their next-gen model, Astra, as their preliminary evaluations indicate that it achieved the critical cybersecurity risk level. At the same time, they shipped GPT-5.6-Cyber to a list of selected partners. This model beats GPT-5.6 Sol by a large margin.

Anthropic, at the same time, released their second Risk Report, in which they raised the misalignment and chem-bio ratings. In addition, they admitted that most of their task-based evaluations have saturated.

The increase in capabilities of open-weight models didn’t leave the White House indifferent. Just 9 days after assuring that they would exclude open-weight models from their voluntary cyber testing framework, they reportedly reversed course. Given the diminishing lag between open-weight and closed models, it is a wise move. Especially considering the deluge of cybersecurity incidents we’ve seen recently.

Harness is the new attack surface

Speaking of cybersecurity, we see that more and more attacks on AI systems concentrate not on the model itself, but on its tools and environment. Black Hat researchers demonstrated how to dispatch a tool provided to the model, without calling the model once in Bedrock AgentCore, Google ADK and the Vercel AI SDK. The providers have already patched the vulnerability.

Another paper shows how to recover plaintext reasoning from encrypted traces using a less-powerful model from the same family. Having access to the reasoning traces simplifies distillation a lot, so it’s natural that frontier labs are guarding them with their own lives.

Harness is the new cost saver

The epochs are changing right before our eyes. Just yesterday large corporations bet on tokenmaxxing, but now they’ve counted the money and decided that it isn’t worth it. For example, KPMG found that 49% of 2,145 surveyed leaders have scaled back agent deployments due to excessive costs¹. Accenture attributed a large chunk of its AI spending to internal conversion of PDFs to markdown, meaning that instead of good old OCR solutions, the employees extracted scanned pages as images and used LLMs to extract the text.

Given this trend, it’s clear that firms are starting to look for ways to use AI more efficiently. And good news, harness optimization is a decent way to do just that.

For example, Databricks reported that its Smart Router cuts average task cost by over 30%. Tuning the harness and caching cuts generated tokens even further, by almost half. Writer, who post-trained their Palmyra X6 from GLM-5.2, cut about 40% of costs just by jointly training the model and the harness.

Pricing wars

While spending on AI continues to skyrocket, the pressure from Chinese models makes major labs race to the bottom. For example, the new Grok 4.6 from xAI costs significantly less than Claude Opus 5 ($2/$6 vs $5/$25), while completing long tasks in roughly half the turns.

Some of the price reduction comes from increases in efficiency. For instance, OpenAI recently managed to cut costs of GPT-5.6 Luna and Terra, up to 80% in the case of Luna.

Models, lo

We see those increases in efficiency not only in major labs, but also in open-source SLMs. My favorite this week, Cactus Needle, has just 45M parameters and weighs 14MB. It is a router model, meaning that it can do just two things: understand the intent of the user, and call a corresponding tool with the right parameters. Not a great thinker, but at least it knows its limits, and will refuse to do a task if it doesn’t have the right levers. And you can even run it on an ESP32-S3!

While interesting, I think the real sweet spot lies somewhere in the 500M-1B parameter range. You can cram in enough world knowledge to understand typical routings, and the model will still be fast and small enough to run on edge gateways and mobile phones.

I will finish here. Have a nice week, it should be interesting.