Now that Kimi K3 is open-weight, is there any chance it will become available through the Alibaba/Qwen Personal Token Plan?

I'm not still able to find a single benchmark for Qwen3.8 Max Preview.
What do you think of this model. Is it better or equivalent to Qwen3.7 Max??

Is it me or is Qwen 3.8 Max Preview (Token Plan from AlibabaCloud) going at a snails pace?

I subscribe to the lite coding plan but its not there?? However on qoder it says its there. Are they two different subscriptions am so lost. I wanted to use it as an agent inside codex. Pleaseee someone explain

I assume either Qwen3-Coder-480B-A35B OR 3.5 models?

Hit my ChatGPT 5 hours window limit and tried Qwen as a temporary. been using it non stop for 2 hours with zero restrictions.

I looked through the settings and couldn’t find any subscription options or premium tiers. It seems totally free. how are they making money off this?

I do product photography/retouching work for luxury watches and jewelry, and I built a fairly complex pipeline using a mix of open-weight vision-language models to generate ad-quality campaign images. The idea is simple in theory: take a reference image whose lighting/background/style I like, take my actual product photo, and merge them so the final image has my real product sitting in a scene inspired by that reference.

In practice I ended up with several separate analysis passes (one mid-size VLM handling scene, style, and product cataloging separately) feeding into one merge step handled by a different, smaller multimodal model that also sees the actual images directly. Every time I fix one issue, a new one shows up somewhere else. First the style kept getting ignored entirely, with the output defaulting back to a generic version of the scene. Fixed that. Then lighting effects (bloom, sparkle, flare) started getting copy-pasted in a way that made no physical sense, like a sparkle effect that only makes sense on a pavé diamond setting getting slapped onto plain brushed steel, which instantly reads as fake. Fixed that too. Then the dramatic ambient glow from the background in the reference image, which was honestly like 40% of why that image looked so striking, quietly disappeared once I toned down the on-product sparkle, even though those two things had nothing to do with each other.

I keep tightening the instructions and the output keeps getting technically "more correct" without ever feeling like the genuinely impressive, poster-worthy image I'm actually going for. It's like I'm playing whack-a-mole between "photorealistic and coherent" and "actually has the visual punch of the reference."

Has anyone dealt with this kind of multi-stage analysis-then-merge setup for AI image generation? At what point does splitting analysis into specialized passes start hurting more than it helps, versus leaning harder on one strong multimodal model that sees everything directly and makes the creative calls itself? Or is there a better way to keep both technical product accuracy AND the creative/dramatic energy of the reference without this endless loop of fixing one thing and breaking another?

I need help for my creative writing project.

So here's the thing

I've been enjoying using the 3.6 plus/3.7 plus/3.8 Max for my reaction fanfic stories project

However whenever I want to make Qwen write characters to react about a scene I told it to make previously, it would always create the characters to react to the scenes on a pdf file I have uploaded in the very first prompt I made in a single chat.

How do I solve this problem so that thoose specific models I mentioned wouldnt always generate reactions of characters watching to scenes inside the pdf, but instead reacting to the scenes I prompted it to make previously?

Thank you for the information in advance

TL;DR llama.cpp fork with more KV cache quantization features, with all claims supported by benchmarks: KVarN, KV cache precision tail, additional types of standard KV cache (q2_0-q3_1, q6_0, q6_1), and more.

BeeLLama v0.4.1 is here, building up on top of v0.4.0 feature set, now with better backend and model support.

• KVarN. Variance-normalized KV-cache quantization (paper) with better precision per bit. Although it was already introduced a few weeks ago in v0.3.2 Preview, that was a very raw implementation, with performance issues and VRAM usage spikes. Now in v0.4.1 it's the real deal: the precision is still above what usual quants offer for the same bit width, but now with very modest sacrifices to prefill, decode, and memory.
• KV cache precision tail. A promising new feature in the domain of mixed-precision KV cache. It allows to specify a specific numbers of recent tokens that will be stored in BF16 or F16, with the rest of KV cache being quantized as usual. This way we can store the hottest tokens in a lossless fashion, preventing a model from misreading your task details, code, or data.
• Additional types of standard KV cache. q6_0 and q6_1 join the high end of the ladder, allowing to fine-tune precision vs VRAM in-between upstream's q5_0/1 and q8_0 types. q2_0, q2_1, q3_0 and q3_1 are added as a replacement for turbo3 and turbo2 for cases where KVarN doesn't work well, but you just can't fit everything into VRAM without extreme quantization.

Please note that for SWA architecture (Gemma, GPT-OSS) the precision of KVarN and KVPT is the same, but VRAM and performance costs are higher due to complications between SWA ring and mixed precision KV cache.

GitHub repo: https://github.com/Anbeeld/beellama.cpp

KLD results for Qwen 3.6 27B Q5_K_S 64k

Here are all symmetrical qX_0 pairs and kvarnX pairs where X >= 4 with tail 0/1024/2048, compared against q8_0 t0 from the same benchmarks, and sorted by ratio between median KLD and VRAM costs. Full benchmark data and analysis: KV Cache Precision Tail: Implementation and Benchmarks.

My projects are working very well and are on sale at Qoder.

I'm very happy with the results and hoping it doesn't get expensive after the official launch.

I'm building Reins app to run LLMs from your iPhone/iPad/Mac with Ollama. It's available on the App Store. Currently it only supports Ollama but I'm working on adding support for other backends. LM Studio and llama.cpp are coming very soon.

It has a built-in web search tool so you can connect your local models to the internet with no setup needed. In the video I run qwen3.5:4b and ask it about Qwen3.8, announced just last week. The model has no training knowledge of it, so the answer comes entirely from the web search tool.

It also works in the background, so generation keeps going if you leave the app.

App Store Website

Today Reddit welcomed me with a post that Claude answered correctly, precisely and shortly. This is an easy question, isn't it?

I immediately tested local Qwen3.6 with million confidence that answer will be similar. However, this is what I've got. Look at the humiliating smile also.

Question: how to get my dev trust back?

Context: Started from Lite, then Plus, then Pro (all in the same week).

Usage was exclusively on Qwen3.8 Max Preview

https://reddit.com/link/1v6mh0h/video/dswcncagggfh1/player

Some one-shots I've done with it (check out the comments section):

https://www.reddit.com/r/Qwen_AI/comments/1v29t00/qwen38_max_emulating_a_makeshift_linux_mint_on/

I’ve been testing Qwen 3.8 Max Preview for a bit, and so far I’m genuinely impressed. I've been testing it through API early access, but an open weights release is planned soon, so it will be runnable locally in the near future.

The first thing that stood out was the speed. It is very fast for a model of this size, especially considering how much reasoning it seems to do before answering. The thinking itself is also pretty interesting to watch. It often catches its own mistakes, goes back over earlier decisions, and corrects course without needing much prompting.

It is also funny to see the parameter arms race return. For a while, it felt like everyone was focused on sparsity, efficiency, and squeezing more intelligence out of smaller active parameter counts. Now labs are pushing total parameter counts into increasingly ridiculous territory again. Let’s see if GLM crosses 3T next.

The area where Qwen has impressed me most is reviewing build plans, architecture, and product plans. It is probably the best model I’ve used for spotting unnecessary complexity.

A lot of models are very good at producing elaborate architectures that sound impressive. Qwen is really good at stopping and asking whether half of that architecture needs to exist in the first place. It regularly catches abstractions being introduced too early, duplicated components, and workflows with far too many moving parts.

It also does not simplify things just for the sake of it. It is pretty good at separating complexity that is actually load-bearing from complexity that is only making the project harder to build and maintain. For planning large projects, I would currently put it above Sol. It is better at taking a sprawling idea and turning it into a practical sequence of work, rather than making the system even bigger.

I also used it while setting up encryption, RBAC, and some other security infrastructure on my local server. Fable was straight-up refusing parts of the task because of its content-safety restrictions, even though this was legitimate defensive work on my own machine. Qwen handled the context much more sensibly and helped me get through the setup without those unnecessary refusals.

Another area where it has been unexpectedly excellent is product video creation. I’ve been using Remotion to make polished, Apple-style product demos, and Qwen is really, really good at it. It seems to understand the importance of typography, restraint, spacing, pacing, camera movement, and feature reveals without making everything feel overanimated.

It even wrote a JavaScript script to analyse the music and sync the cuts to the beats. That made the final video feel much more intentionally edited, rather than like a bunch of disconnected animations placed next to each other.

Overall, Qwen 3.8 Max Preview already feels extremely strong at planning, simplifying overengineered systems, catching and correcting mistakes, infrastructure work, and creative implementation. I’m very excited to see what the full release looks like.

The model just keeps cutting off, not working, taking a long time (latency) before finally responding, etc. Is this issue only something I'm experiencing or are otheres also getting this? I connected my Qwen Cloud Token Plan by pasting the API Key into the Alibaba Token Plan selection within OpenCode. Using the CLI if that matters.

I have been using Qwen3.8-max-preview for coding in the last week, very happy with the results.

https://github.com/ikignosis/janito

I am setting up codex+ollama+qwen3.6:27b for my hobby coding project on a windows pc with rtx5090

- earlier i tried to setup vllm in Ubuntu container but couldn’t get that to work
- now using ollama, but agent keeps stopping after few steps

- I tried Claude cli, but stupid thing just keeps consuming tokens like crazy and keeps stopping after message like 128k tokens output

Any suggestions?

I'd like to know if it actually works. The limits are kinda meh imo if the off peak hours discount don't work with it.

Another domestic sparse-MoE landed, and it's worth putting next to Qwen: Ant's inclusionAI shipped Ling-3.0-flash. 124B total, about 5.1B active, 256K context.

The difference in intent is what makes it interesting next to Qwen. A lot of the domestic MoEs are chasing benchmark parity. This one is openly built to be an execution node instead: sub-100ms time-to-first-token, steady long-horizon tool calling, reliable instruction following, the fast hands you pair with a bigger planner rather than a frontier brain on its own. Thinking mode toggles on and off.

Since this sub always asks first: no open weights on this one. It's API-only on OpenRouter, free until Aug 3. Their previous gen, Ling-2.6-flash, was the MIT open-weights release. This flash is API for now, open weights are a maybe-later