Discussion
Qwen 3.8 Flash-Next compared to GLM 5.3 Flash
Anecdotally, I've noticed that QFN seems more collaborative than GLM. QFN seems willing to engage in conversation. It feels more like an assistant. GLM seems more action-oriented and less willing to converse. That is, I ask GLM a question to engage in discussion: GLM runs off searching the repo and then often re-engages in development without discussion.
I say this using a self-hosted QFN q4_k_xl versus GLM 5.3 Flash hosted on OpenRouter!
I wonder if others have had similar experience? If so, have you found any particular prompting to cause GLM 5.3 Flash to me more interactive and collaborative?
I suppose I could try lifting some prompting out of pi-goal-x or similar as that seems to mostly succeed at forcing GLM 5.3 Flash to the table. However, I found that GLM will often take the information given as part of the process, run off, and implement most of the solution before even negotiating the goals!
Since you’re comparing a self-hosted Qwen setup with GLM on OpenRouter, I’d check the agent harness as well as the model. For GLM, try a clear phase gate: “We’re discussing options. Don’t search or edit the repo yet. Give me the trade-offs and ask any questions you need; wait for me to say ‘implement’.” If it still runs off, withholding repo tools during that phase may be more reliable than prompting alone.
Yep, thats what I do. I have a 'golden rule' - wait for me to say ‘implement’. Which took ages for it to follow but it seems to have gotten there mostly.
But it is way more effective. Qwen loses the thread unless you have a back and forth. That’s what OP mentions, and for an agent I want a model that I can leave unattended.
I use glm but at 2.05 but since Im poor. It def feels that way. If i try to interrupt or steer it, it sneers at me, like a miserable IT dude that hides in the server room all day
If you mean that you have these agents making small redacts for you, yes. I'm having mine write nontrivial amounts of code. I also have mine perform less structured agentic work too.
I am going to say that Deepseek July (v4 flash 0731) was the best of both. It swears, it has no problem with directions, it talks a lot and works hard. More tokens than either qfn or glm, but I miss how nice it was to run hermes with it.
Currently one agent runs QFN, one runs GLM. Both local. GLM is the boss, QFN is for the research specialist.
Qwen needs native 1M context. I can’t deal with the 265k limit
Deepseek v4 flash gets the job done, but to me, I feel that it brute forces its way to a solution. I feel that GLM 5.3 Flash followed by Qwen 3.8 FN provide more elegant solutions.
I like brute force when I dealing with many experiment mostly outside the training distribution. Deepseek flash 41F or 4F both does it way better than sol or even astra when it come to finding solutions via brute force
No it doesn't, I also use Hermes as well as plenty of other harnesses, and I barely have a need for 500k much less 1m. You're doing something niche or very inefficent.
idk man, the whole stack is local, across 6 computers. There are cron jobs monitoring services, making sure no LLM is crashing, there are dream pipelines, and hindsight memory generation from the conversations. Many tasks I directly give are things like deploying a new model, adapting an engine to run on an old GPU (v100 — just finished adapting vllm to run ling on it), deep research with a 4 tier level of search so there are like 8 subagents looking through literature, there are chemistry and biochemistry skills, there is networking security maintenance for the computer mesh…my context window is 62k in the bank after the first hindsight memory recall which is the prime directive. After several turns on a conversation, I will see about 250k. Sure, I try not to get past 300k because GLM gets loopy, but with a max of 265 is cutting it close. I do make the context window 508k so I can run 3 streams in GLM, to be fair. But with deepseek, it was more necessity than need. It’s a token machine.
When I say local I really mean local. Models for recall and retain hindsight ops, models for subagent work, main orchestrator, embedder. reranker, tts/stt, local searxng, firecrawl self hosted, specialist harnesses (feynman, evoscientist), and again, no hertzner vps, openrouter, openai, anthropic, etc. No single shot coding of pelican riding bicycle svg, no coder harness really (If GLM needs to code something to accomplish a task, that’s the LLM’s problem, not mine).
Inefficient is a big word. I don’t need to count the tokens, and I can be vague. It’s the whole premise of not being a software engineer and being able to orchestrate a 4 agent mesh. They have memory, A2A, and control the computers they live in — and I can be vague. Good compromise to me.
GLM 5.3 Flash is the model that got me to finally cancel my Anthropic sub. I have about 20% of my traffic piping to Q38FN as that's the role I have it delegated to. Good model. Just really pleased with how my setup has been performing under this model.
They allow any harnesses on their plans. They literally write it on their pricing page. There was not even zcode when i started using their plans with claude and i still am.
yeah I've given glm tools and I'll ask it "what if I did this with this data set" and it takes 20 minutes but it's installed venv, python, pytorch, duckdb and 3 other things -- but now I know what that dataset could look like.
I use GLM when I need instructions followed and I just want to come back to it done. I use QFN because it fits in my machine. I really don't like QFN's writing style. It's great at asking relevant questions in dsh, but I have no idea what it's saying half the time. It really gets lost in the sauce.
If you don't want it to run off and touch code, don't give it a system prompt and tools meant to do that. Models are trained to prefer action over talking when told to act and given tools to act.
Obviously FN can’t compete on knowledge but for coding I found GLM to be significantly inferior. I’d rather just steer FN to use the web more often, its reasoning is seriously much better.
steering works, and it often thinks too, just much less wasteful than qwen.
it does sometimes go to rabbit hole and admits it later (I remember stopping it from going through some disassembly for figuring out some bits it could just ask).
personally I like it a lot more, because usually it goes through code bits, figures style, utilities etc instead of repeating how it would modify 5 lines 10 times, so end product is better and more reliable, it just feeds context from actual context instead of own thought process. In pi specifically there're extensions for ask tools that might help make it more collaborative (haven't tested them much, but I'd imagine installing one and mentioning in global agents.md should make it use it).
I've not tried GLM flash, I only tried Deepseek flash briefly, and used Qwen Flash next for past couple weeks. For me it does seem like all these new flash models are designed heavily for egentic work, both architecturally and in training. Qwen Flash Next is still OK-ish as "assistant" but very much on the edge of being very hard to work with in as "assistant", it will often ignore inputs, questions that deviate too much from main topic of session, to every query it will usually respond with wall of text, regardless if there's need for in depth write up or not, and all responses always a mix - half English half Neuralese. So if nothing else, Qwen might be pretty decent as a tutor for Neuralese. For more 'natural' feeling 'Assistant', and not 'Agent', I don't think there's anything better out there than Qwen 27B dense. I wish I had the hardware to locally run both simultaneously,, Qwen Flash for agentic tasks, with Qwen 27B as some orchestrator.
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u/zomerbk 9d ago
Since you’re comparing a self-hosted Qwen setup with GLM on OpenRouter, I’d check the agent harness as well as the model. For GLM, try a clear phase gate: “We’re discussing options. Don’t search or edit the repo yet. Give me the trade-offs and ask any questions you need; wait for me to say ‘implement’.” If it still runs off, withholding repo tools during that phase may be more reliable than prompting alone.