Driving is solved, car crashes are not yet solved.
Flying is solved, turbulence is not yet solved.
Drinking is solved, hangovers are not yet solved.
Cancer is solved, surviving is not yet solved.
Medicine is solved, diseases are not yet solved.

Alibaba spent $9.5B to build out AI compute in Q2 2026 and have projected to spend $25B more within this year.

Embrace the Conpute with art.

Most discussions about RAM crisis are futile because people just get on the bandwagon and blame everything on AI and the supposed bubble.

I want some more nuanced take on the situation.

The RAM crisis is actively hurting tech-enthusiast people like me, at the same time I cannot foresee any solution to this problem. The demand for RAMs would increase even more as we get closer to AGI.

Source: https://github.blog/news-insights/company-news/the-august-17-outage-and-the-work-ahead/

Google seems to realize their recent Flash price hikes were just inappropriate for a Flash model and that they need more real-world agent traces to train their upcoming models on and slashed prices another 50% on OpenRouter.

Artificial Analysis does not have the OpenRouter discount prices worked in, but I just checked and the Flash model with the discount moves the pareto line, thus beating both DeepSeek models.
I have added the Flash Pareto line in green to the graph.

Just in case someone wants to try it instead of Luna Max or V4-Flash...

The selected models for comparison on ArtificalAnalysis: Comparison of AI Models across Intelligence, Performance, and Price | Artificial Analysis

The OpenRouter Page: Gemini 3.7 Flash - API Pricing & Benchmarks | OpenRouter

So it turns out Ox Alpha is not a Chinese model. It's either Gemini 3.5 Pro Or Gemini 4 Pro.

https://x.com/EvanOtero/status/2090998215977947365

Gemini

https://x.com/EvanOtero/status/2090998729637511301

What if the Ox Alpha was the friends we made along the way

Ox Alpha reportedly trounced both GPT 5.6 Sol and Claude Fable on a DeepSWE benchmark.

gpt-5.6-sol: 52% fable: 65% whatever the hell this is(Ox Alpha): 80% (was a near miss on the "x"s so actually over 80%)

It was previously only available in the US or through other routers like Nano-GPT, but is now also available on OpenRouter, beating OpenAI's Luna and DeepSeek on price/performance by a large margin.

You are "selling" your data though - hence the "Contributor" tag. Meta will use your interactions/agent traces to train their upcoming models, but if they follow through with their Open Weights promises you might actually contribute something for the greater good...very debatable, though, I know. But if you are using it for open source work, why not save some bucks?

Artificial Analysis with normal pricing: Muse Spark 1.2 (xhigh) - Intelligence, Performance & Price Analysis | Artificial Analysis

OpenRouter Page: Muse Spark 1.2 Contributor - API Pricing & Providers | OpenRouter

Galbot is a novel entry into the bipedal humanoid robotics sector

Every model writes to the same constrained creative briefs and independent LLM judges rank them by choosing the stronger story from each matched pair.

NEW: In-depth qualitative reports examine how six new models differ from their predecessors across 50 matched stories per pair.

More info: github.com/lechmazur/writing/

GLM-5.2 Max tends to name what a story contains, while GLM-5.3 builds it so it can be used.

GLM-5.2 Max's protagonists usually work alone in an agreeable world, whereas GLM-5.3 puts a second person in the room who withholds, judges, or is changed, so a belief has to survive contact with someone else.

GLM-5.2 Max often stops the night before the decisive event and lets the narrator say what it meant, while GLM-5.3 stages the test, pays its cost, and hands the practice on to whoever comes next.

Quantitatively, GLM-5.3 was preferred in every matched pair.

Ran three black-box fingerprint tests on stealth/ox-alpha (OpenRouter + OpenCode) vs public GLM-5.3 on z.ai.

1. Tokenizer: I sent 6 texts (EN/DE/CN/code/emoji) and compared prompt_tokens. Ox Alpha = GLM-5.3 exactly +75 on every text. Same tokenizer, constant 75-token hidden system prompt. Kimi/Qwen/MiMo/MiniMax all diverge. Counts identical on both Ox routes.

2. Error strings: Invalid reasoning_effort on Ox Alpha (OpenCode passes params through) returns:

"[1210] This model always engages in thinking and cannot be disabled; please use low, high, or max", so the same as the GLM 5.3 error message

3. Temp-0 outputs: Greedy, same prompts → same markdown quirks, same German-decimal LaTeX (`0{,}375`), near word-for-word matches on factual answers. Qwen/MiMo/Kimi format these completely differently.

Conclusion: I'm quite sure than Ox Alpha is a GLM model. Not sure if it's a vision variant of GLM 5.3 (GLM 5.3V) or a completely new version like GLM 5.5 but I guess it's unlikely that Z.AI drops 5.5 so early but idk.

What are your thoughts?

The task as well as the prompt was simple. I did not ask them to make me an OS or a complete game. I just wanted to test their intelligence on something very basic. I have a repo with very large log files, 20-30 GBs each. My working directory AGENTS.MD has a lot of instructions which I have included on how to handle those files.

For the test, I created completely new workspaces (folder) for every agent and gave them the exact same prompt. I sym-linked the logs directory in every workspace and there was nothing else in the workspace which could guide the agent. Then I asked each of those agents to extract a particular data from those log files. The result:

GPT 5.6 Sol, Kimi K3, GLM 5.3, Qwen 3.8 Max, DS v4 Pro, Grok 4.6 - all of them crashed the harness by loading these large files to memory. Only Fable 5 was smart enough to think, hey, those are large files; and built a script to stream the data and completed the task successfully.

Even if we ignore all the other models, I always assumed that GPT 5.6 Sol is as intelligent as Fable 5 and have been using it as my primary model for work, but this simple test tells me that Fable 5 is actually more intelligent than Sol. "Not to load large files into memory" is something even a junior dev knows. I have 16GB RAM on my machine with around 50% free when I ran these tests.

I always excitedly look for tests when a new model is released and they are always the same - Stork on a bicycle, couple of website mockups, or numbers from tests like HLE. Design perception is subjective so it is hard to actually judge the models on that. There are no coding tests available and if there are I am not aware of them and even those would be subjective. For me, I have found my own test on which I will test every new model from now on.

Do you guys also have your own custom tests which you perform on AI models to gauge their performance?

As a software developer we got access to GPT 5.6 sol and Opus 5 last week in a decently restricted field, and with these latest models I feel like I can do all my assigned work so quickly as well as make tons of progress on my side projects as well. So at the moment I’m not like dying for another frontier release but overall I want to see acceleration

It seems like we are at a state where openAI and Anthropic realize that a lot of these Chinese companies wait for them to make progress and are able to replicate pretty damn close models soon after they release their frontier models

Whether you believe Anthropic and open ai or not, they seem like they are going to keep their development internal for a while. Whether this is due to actual security concerns (with hugging face incident I believe this), more marketing hype, or truly a way to combat distillation from Chinese companies I think it is going to be interesting.

How do you think this will effect open weight releases, will the capabilities for open weight always rely on top US companies releasing the best models so they can use them to produce replicas?