With them comparing to Opus 4.5, I find it hard to take some of these in good faith. Opus 4.7 is new, so I don't expect that, but Opus 4.6 has been out for quite some time.
I find it odd that none of OpenAI models was used in comparison, but used Z GLM 5.1. Is Z (GLM 5.1) really that good? It is crushing Opus 4.5 in these benchmarks, if that is true, I would have expected to read many articles on HN on how people flocked CC and Codex to use it.
Everybody's out here chasing SOTA, meanwhile I'm getting all my coding done with MiniMax M2.5 in multiple parallel sessions for $10/month and never running into limits.
The way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.
That has been a viable commercial strategy for most modern, funded businesses. Capture market share at a loss, then once name is established turn on the profit.
Nowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case.
And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with wrong, misleading and unnecessary information but on the other hand Qwen always gave me proper, practical and correct information. I’ve almost stopped using Claude and Gemini to not to waste my time anymore.
Claude code may shine developing web applications, backends and simple games but it's definitely not for me. And this is the story of my specific use case.
Ok I find it funny that people compare models and are like, opus 4.7 is SOTA and is much better etc, but I have used glm 5.1 (I assume this comes form them training on both opus and codex) for things opus couldn't do and have seen it make better code, haven't tried the qwen max series but I have seen the local 122b model do smarter more correct things based on docs than opus so yes benchmarks are one thing but reality is what the modes actually do and you should learn and have the knowledge of the real strengths that models posses. It is a tool in the end you shouldn't be saying a hammer is better then a wrench even tho both would be able to drive a nail in a piece of wood.
GLM 5.1 was the model that made me feel like the Chinese models had truly caught up. I cancelled my Claude Max subscription and genuinely have not missed it at all.
Some people seem to agree and some don't, but I think that indicates we're just down to your specific domain and usage patterns rather than the SOTA models being objectively better like they clearly used to be.
>GLM 5.1 was the model that made me feel like the Chinese models had truly caught up. I cancelled my Claude Max subscription and genuinely have not missed it at all.
GLM 5.1 is pretty good but there are some "buts".
They hiked the prices 2 times this year. I subscribed to the pro coding plan just before the last hike. At the start of the year, they had only 5 hours quota and no weekly quota. And I hit the weekly quota hard. I can't upgrade the subscription to get a higher weekly quota because they jacked up the prices a lot recently.
My $30 subscription costs now $72. Previously was $15.
Max was $49,then $80 and now $160.
I have been using GLM-5.1 with pi.dev through Ollama Cloud for my personal projects and I am very happy with this setup. I use pi.dev with Claude Sonnet/Opus 4.6 at work. Claude Code is great but the latest update has me compacting so much more frequently I could not stand it. I don't miss MCP tool calling when I am using pi.dev; it uses APIs just fine. I actually think GML-5.1 builds better websites than Claude Opus. For my personal projects I am building a full stack development platform and GLM-5.1 is doing a fantastic job.
Opus 4.6 was incredible but Opus 4.7 is genuinely frustrating to me so far. It's really sharp but can be so lazy. It's constantly telling me that we should save this for tomorrow, that it's time for bed (in the middle of the day), and very often quite sloppy and bold in its action. These adjustments are getting old. The next crop of open models seems ready to practically replace the big ones as sharp orchestrator agents.
I had to write multiple times in my prompt that it's not the model's role to change the subject or end the conversation at all.
I think that they do that to dodge conversations about controversial subjects without full-on refusing to answer. They'll give you an ok answer then tell you to go to get the walk you were talking about.
I also feel like maybe they think people are still ready to pay a lot if they feel like they're getting a lot of "high value stuff" even if the low value stuff the model refuses to do, so they basically try to stop you from doing low value stuff on Opus. I suspect that Sonnet or Haiku never tells you to go take a hike.
Not to mention, that Opus cost orders of magnitude more money.
These are VERY impressive and usage.
FAANGS love to give away money to get people addicted to their platforms, and even they, the richest companies in the world, are throttling or reducing Opus usage for paying members, because even the money we pay them doesn't cover it.
Meanwhile, these are usable on local deployments! (and that's with the limited allowance our AI overlords afford us when it comes to choices for graphics cards too!)
I don't find GLM 5.1 beating Opus personally, but I do think it is good enough to consider it part of the SOTA pack at this point. It feels like it needs more time and tokens to achieve things, but that's okay - it's so much cheaper per token.
If Qwen3.6-Max is up there as well, it will be very interesting.
While Qwen advertises large context windows, in practice the effectiveness of long-context usage seems to depend heavily on its context caching behavior. According to the official documentation, Qwen provides both implicit and explicit context caching, but these come with constraints such as short TTL (around a few minutes), prefix-based matching, and minimum token thresholds.
Because of these constraints, especially in workflows like coding agents where context grows over time, cache reuse may not scale as effectively as expected. As a result, even though the per-token price looks low, the effective cost in long sessions can feel higher due to reduced cache hit rates and repeated computation.
That said, in certain areas such as security-related tasks, I’ve personally had cases where Qwen performed better than Opus.
In my personal experience, Qwen tends to perform much better than Opus on shorter units like individual methods or functions. However, when looking at the overall coding experience, I found it works better as a function-level generator rather than as an autonomous, end-to-end coding assistant like Claude.
Is this going to be an open weights model or not? The post doesn’t make it clear. It seems the weights are not available today, but maybe that’s because it’s in preview?
I've been using glm5.1 for pretty much all my coding work, but Claude is too expensive for me. Haven't tried qwen yet though. China's coding models are now very cost-effective.
I've been using Claude Code regularly at work for several months, and I successfully used it for a small personal project (a website) not long ago. Last weekend, I explored self-hosting for the first time.
Does anyone have a similar experience of having thoroughly used CC/Codex/whatever and also have an analogous self-hosted setup that they're somewhat happy with? I'm struggling a bit.
I have 32GB of DDR5 (seems inadequate nowadays), an AMD 7800X3D, and an RTX 4090. I'm using Windows but I have WSL enabled.
I tried a few combinations of ollama, docker desktop model runner, pi-coding-agent and opencode; and for models, I think I tried a few variants each of Gemma 4, Qwen, GLM-5.1. My "baseline" RAM usage was so high from the handful of regular applications that IIRC it wasn't enough to use the best models; e.g., I couldn't run Gemma4-31B.
Things work okay in a Windows-only setup, though the agent struggled to get file paths correct. I did have some success running pi/opencode in WSL and running ollama and the model via docker desktop.
In terms of actual performance, it was painfully slow compared to the throughput I'm used to from CC, and the tooling didn't feel as good as the CC harness. Admittedly I didn't spend long enough actually using it after fiddling with setup for so long, it was at least a fun experiment.
47 comments
[ 4.2 ms ] story [ 75.9 ms ] threadI knew of all the 3.5’s and the one 3.6, but only now heard about the Plus.
And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with wrong, misleading and unnecessary information but on the other hand Qwen always gave me proper, practical and correct information. I’ve almost stopped using Claude and Gemini to not to waste my time anymore.
Claude code may shine developing web applications, backends and simple games but it's definitely not for me. And this is the story of my specific use case.
They brag about Qwen but don't let people use it.
Some people seem to agree and some don't, but I think that indicates we're just down to your specific domain and usage patterns rather than the SOTA models being objectively better like they clearly used to be.
GLM 5.1 is pretty good but there are some "buts".
They hiked the prices 2 times this year. I subscribed to the pro coding plan just before the last hike. At the start of the year, they had only 5 hours quota and no weekly quota. And I hit the weekly quota hard. I can't upgrade the subscription to get a higher weekly quota because they jacked up the prices a lot recently.
My $30 subscription costs now $72. Previously was $15. Max was $49,then $80 and now $160.
I think that they do that to dodge conversations about controversial subjects without full-on refusing to answer. They'll give you an ok answer then tell you to go to get the walk you were talking about.
I also feel like maybe they think people are still ready to pay a lot if they feel like they're getting a lot of "high value stuff" even if the low value stuff the model refuses to do, so they basically try to stop you from doing low value stuff on Opus. I suspect that Sonnet or Haiku never tells you to go take a hike.
FAANGS love to give away money to get people addicted to their platforms, and even they, the richest companies in the world, are throttling or reducing Opus usage for paying members, because even the money we pay them doesn't cover it.
Meanwhile, these are usable on local deployments! (and that's with the limited allowance our AI overlords afford us when it comes to choices for graphics cards too!)
If Qwen3.6-Max is up there as well, it will be very interesting.
1. Keeping models closed source.
2. Jacking up pricing. A lot. Sometimes up to 100% increase.
While Qwen advertises large context windows, in practice the effectiveness of long-context usage seems to depend heavily on its context caching behavior. According to the official documentation, Qwen provides both implicit and explicit context caching, but these come with constraints such as short TTL (around a few minutes), prefix-based matching, and minimum token thresholds.
Because of these constraints, especially in workflows like coding agents where context grows over time, cache reuse may not scale as effectively as expected. As a result, even though the per-token price looks low, the effective cost in long sessions can feel higher due to reduced cache hit rates and repeated computation.
That said, in certain areas such as security-related tasks, I’ve personally had cases where Qwen performed better than Opus.
In my personal experience, Qwen tends to perform much better than Opus on shorter units like individual methods or functions. However, when looking at the overall coding experience, I found it works better as a function-level generator rather than as an autonomous, end-to-end coding assistant like Claude.
Qwen appears to be much more expensive:
- Qwen: $1.3 in / $7.8 out
- Kimi: $0.95 in / $4 out
--
The announcement posts only share two overlapping benchmark results. Qwen appears to score slightly lower on SWE-Bench Pro and Terminal-Bench 2.0.
Qwen:
- Teminal-Bench 2.0: 65.4
- SWE-Bench Pro: 57.3
Kimi:
- Terminal-Bench 2.0: 66.8
- SWE-Bench Pro: 58.6
--
Different models have different strong suits, and benchmarks don't cover everything. But from a numbers perspective, Kimi looks much more appealing.
Does anyone have a similar experience of having thoroughly used CC/Codex/whatever and also have an analogous self-hosted setup that they're somewhat happy with? I'm struggling a bit.
I have 32GB of DDR5 (seems inadequate nowadays), an AMD 7800X3D, and an RTX 4090. I'm using Windows but I have WSL enabled.
I tried a few combinations of ollama, docker desktop model runner, pi-coding-agent and opencode; and for models, I think I tried a few variants each of Gemma 4, Qwen, GLM-5.1. My "baseline" RAM usage was so high from the handful of regular applications that IIRC it wasn't enough to use the best models; e.g., I couldn't run Gemma4-31B.
Things work okay in a Windows-only setup, though the agent struggled to get file paths correct. I did have some success running pi/opencode in WSL and running ollama and the model via docker desktop.
In terms of actual performance, it was painfully slow compared to the throughput I'm used to from CC, and the tooling didn't feel as good as the CC harness. Admittedly I didn't spend long enough actually using it after fiddling with setup for so long, it was at least a fun experiment.