Qwen team, please please please, release something to outperform and surpass the coding abilities of Opus 4.5.
Although I like the model, I don't like the leadership of that company and how close it is, how divisive they're in terms of politics.
Although I like the model, I don't like the leadership of that company and how close it is, how divisive they're in terms of politics.
Have you tested alternatives? I grabbed Open Code and a Minimax m2.1 subscription, even just the 10usd/mo one to test with.
Result? We designed a spec for a slight variation of a tool for which I wrote a spec with Claude - same problem (process supervisor tool), from scratch.
Honestly, it worked great, I have played a little further with generating code (this time golang), again, I am happy.
Beyond that, Glm4.7 should also be great.
See https://dev.to/kilocode/open-weight-models-are-getting-serio...
It is a recent case story of vibing a smaller tool with kilo code, comparing output from minimax m2.1 and Glm4.7
Honestly, just give it a whirl - no need to send money to companies/nations your disagree with with.
$20/month is a bit of an insane ask when the most valuable thing Anthropic makes is the free Claude Code CLI.
Do you even need an subscription to any service for that? Is a free tier not enough?
alias "claude-zai"="ANTHROPIC_BASE_URL=$ZAI_ANTHROPIC_BASE_URL ANTHROPIC_AUTH_TOKEN=$ZAI_ANTHROPIC_AUTH_TOKEN claude"
Someone gave me that command a while back.
What do you mean by this?
https://www.bloomberg.com/news/articles/2026-01-20/anthropic...
And that's the rub.
Many of us are not.
Being critical of favorable actions towards a rival country shouldn't be divisive, and if it is, well, I don't think the problem is in the criticism.
Also the link doesn't mention open source? From a google search, he doesn't seem to care much for it.
I prefer to have more open models. On the other hand China closes up their open models once they start to show a competitive edge.
I still have a small Claude account to do some code reviews. Opus 4.5 does good reviews but at this point GLM 4.7 usually can do the same code reviews.
If cost is an issue (for me it is, I pay out of pocket) go with GLM 4.7
Regardless of how productive those numbers may seem, that amount of code being published so quickly is concerning, to say the least. It couldn't have possibly been reviewed by a human or properly tested.
If this is the future of software development, society is cooked.
Stuff like this: https://github.com/mohsen1/claude-code-orchestrator-e2e-test...
Yes, the idea is to really, fully automate software engineering. I don't know if I am going to be successful but I'm on vacation and having fun!
if Opus 4.5/GLM 4.7 can do so much already, I can only imagine what can be done in two years. Might as well adopt to this reality and learn how leverage this advancement
I think using GitHub with issues,PRs and specially leveraging AI code reviewers like Greptile is the way to go Actually. I did an attempt here https://github.com/mohsen1/claude-orchestrator-action but I think it needs a lot more attention to get it right. Ideas in Gas Town are great and I might steal some of those. Running Claude Code in GitHub Action works with GLM 4.7 great.
Microsoft's new Agent SDK is also interesting. Unlocks multi-provider workflows so user can burn out all of their subscriptions or quickly switch providers
Also super interested in collaborating with someone to build something together if you are interested!
I use Opus 4.5 for planning, when I reach my usage limits fallback to GLM 4.7 only for implementing the plan, it still struggles, even though I configure GLM 4.7 as both smaller model and heavier model in claude code
I spent 20 minutes yesterday trying to get GLM 4.7 to understand that a simple modal on a web page (vanilla JS and HTML!) wasn't displaying when a certain button was clicked. I hooked it up to Chrome MCP in Open Code as well.
It constantly told me that it fixed the problem. In frustration, I opened Claude Code and just typed "Why won't the button with ID 'edit' work???!"
It fixed the problem in one shot. This isn't even a hard problem (and I could have just fixed it myself but I guess sunk cost fallacy).
My experience is that all of the models seem to do a decent job of writing a whole application from scratch, up to a certain point of complexity. But as soon as you ask them for non-trivial modifications and bugfixes, they _usually_ go deep into rationalized rabbit holes into nowhere.
I burned through a lot of credits to try them all and Gemini tended to work the best for the things I was doing. But as always, YMMV.
That evening, for kicks, I brought the problem to GLM 4.7 Flash (Flash!) and it one-shot the right solution.
It's not apples to apples, because when it comes down to it LLMs are statistical token extruders, and it's a lot easier to extrude the likely tokens from an isolated query than from a whole workspace that's already been messed up somewhat by said LLM. That, and data is not the plural of anecdote. But still, I'm easily amused, and this amused me. (I haven't otherwise pushed GLM 4.7 much and I don't have a strong opinion about about it.)
But seriously, given the consistent pattern of knitting ever larger carpets to sweep errors under that Claude seems to exhibit over and over instead of identifying and addressing root causes, I'm curious what the codebases of people who use it a lot look like.
This has been my consistent experience with every model prior to Opus 4.5, and every single open model I've given a go.
Hopefully we will get there in another 6 months when Opus is distilled into new open models, but I've always been shocked at some of the claims around open models, when I've been entirely unable to replicate them.
Hell, even Opus 4.5 shits the bed with semi-regularity on anything that's not completely greenfield for my usage, once I'm giving it tasks beyond some unseen complexity boundary.
China would need an architectural breakthrough to leap American labs given the huge compute disparity.
A financial jackknifing of the AI industry seems to be one very plausible outcome as these promises/expectations of the AI companies starts meeting reality.
1. Chinese researcher in China, to be more specific.
1. e.g. select any DeepSeek release, and read the accompanying paper
Your 'cope' accusation has no place here, I have no dog in the race and do not need to cope with anything.
I will rephrase my statement and continue to stand by it: "Denying the volume of original AI research being done by China - a falsifiable metric - betrays some level of cope."
You seem to agree on the fact that China has surpassed the US. As for quality, I'll say expertise is a result of execution. At some point in time during off-shoring, the US had qualitatively better machinists that China, despite manufacturing volumes. That is no longer the case today - as they say, cream floats to the top, and that holds true for a pot or an industrial-sized vat.
1: https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_populationThey need a training-multiplier breakthrough that would allow them to train SOTA models on on a fraction of the compute that the US does. And this would also have to be kept a secret and be well hidden (often multiple researchers from around the world put the pieces together on a problem at around the same time, so the breakthrough would have to be something pretty difficult to discover for the greatest minds in the field) to prevent the US from using it to multiply their model strength with their greater compute.
because i've been on youtube and insta, and believe me, no one else even compares, yet.