Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

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.

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.

Kimi K2.6 also released today. I think it's fair to compare the two models.

Qwen appears to be much more expensive:

- Qwen: $1.3 in / $7.8 out

- Kimi: $0.95 in / $4 out

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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

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Different models have different strong suits, and benchmarks don't cover everything. But from a numbers perspective, Kimi looks much more appealing.

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.

https://www.alibabacloud.com/help/en/model-studio/context-ca... I’ve also been testing models like Opus, Codex, and Qwen, and Qwen is strong in many coding tasks. However, my main concern is how it behaves in long-running sessions.

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.

Notice the pattern that Chinese providers are now:

1. Keeping models closed source.

2. Jacking up pricing. A lot. Sometimes up to 100% increase.

The fun thing is, you can be aware of the entire range of Qwen models that are available for local running, but not at all about their cloud models.

I knew of all the 3.5’s and the one 3.6, but only now heard about the Plus.

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.

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.

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 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.

I think the benchmarks and numbers need to be easier to read. Those benchmarks are useless to the regular consumer.

A bit weird to be comparing it to Opus-4.5 when 4.7 was released...

I have the M3 Max MBP with 128 GB of memory and the 40 core GPU. What's the best local model I can run today for coding?

Very impressive!

ToKeN PrIcEs ArE gOiNg tO PluMmEt, InTelLigEnCe WiLl Be AfForDaBlE FoR EvErYOnE

I am trying since one week to subscribe Alibaba Coding Plan (to use Qwen 3.6 Plus) but it's always out of stock.

They brag about Qwen but don't let people use it.

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I tried it asked to write it an SVG with a cat holding a guitar it wrote a pic of my gradma's look alike taking a poop. Seems alibaba has it on the spot! Lolz try it for your selves for remarkable svg's and png's!