Comment by alexpotato
3 days ago
Been using GLM 5.3 Flash via Ollama Cloud basic subscription:
if this is the only model I had to use for the rest of my life, I would be cool with that. It's good at a wide variety of tasks including reverse engineering, design, working with ESP 32 etc.
I think Muse Spark 1.3 is better but GLM 5.3 Flash is an excellent daily driver.
It is unreal how good GLM 5.3 Flash is. There's an ~4 bpw EXL3 quant that fits on 2x DGX Sparks. I'm not sure it's worth it now ($7k/Spark) or even when I bought them ($4k/Spark) but there's something about knowing that the whole internet could go down and I could still have this model running at 50-60 tokens/s with 1M context, indefinitely. It's just so good. TensorFold hits 100+ tokens/s in aggregate with 2-3 M total context pool. It's like having Claude at home.
I'm using Flash and the full 5.3 model side-by-side, and for coding I'm hard-pressed to find a difference. Flash is slightly worse at steering in longer contexts, but writes code that's just as good as anything I get out of full 5.3. You could totally build a SaaS or video game with nothing but 5.3 Flash.
The full 5.3 is only really needed for super deep bugfixes/profiling passes, or ridiculous reverse-engineering in my experience.
Yeah, MiMo Pro is the first Chinese model I can really clearly identify as an upgrade over GLM 5.3 Flash. K3, Qwen 3.8 Pro, GLM 5.3, DS4.1 Flash all feel around equivalent to me. Oddly lots of people have derided MiMo but I have found it able to tackle slightly tougher problems. For instance, I tried GLM 5.3 Flash, DS4.1 Flash, and MiMo Pro on making a new map layer for my Garmin watch, and MiMo was the only one that found the actual best method (.img file for OSM).
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In my experience, like most models, it requires a ton of supervision. It is extermely verbose. Flash loves to create some wildly unecessary abstractions/spaghetti code (like most llms). It wants to implement things in the least effecient manner and needs to be reminded constantly not to do things a certain way.
Am I the only one experiencing accidental chinese tokens, wrong grammar, and other sloppy mistakes in almost every output when using GLM 5.3? I feel like using it through any kind of subscription service (z.ai, Opencode Go) just serves me aggressive quants.
On some days it's usable, but on most days it feels more like having to trust a pretty smart intern that may or may not get a small stroke every couple of minutes.
I've only ever gotten sudden chinese (and some other script I couldn't identify) from GPT5.6 Luna via OpenAI. It's a very interesting failure mode.
I've had a bunch of failed tool calls and weirdness through Cursor with it recently but it's so good and so much cheaper that I put up with it.
We use GLM at work and YMMV a lot depending on the provider. So i guess some have bugs on their inference pipeline, or serve too agressive quants
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I got this frequently with on GLM 5.3 Flash with GLM's actual plan, I do not get this with self hosting.
That sounds like a bug. I’ve never had this happen, using it daily since release.
Never had that happen at all and using since launch as well, but I have a very custom harness running. My local machine went down however and I used their web API to repair it and talking to that felt much stupider, I think harness really matters Alot.
I do get the really annoying LLM "that's A, not B" and "load-bearing" from time to time, those two tells stay no matter what I do even though the rest of the output is pretty amazing,
How do you feel it compares to DeepSeek 0731 if you have tried that? (Also runs well on 2x spark)
I was running DS731 until Qwen 3.8 Flash Next (so what, like a couple weeks? The pace has been torrid). I switched to Qwen for image input, then GLM 5.3 Flash. At the time, Qwen felt like a minor step down from DS731, GLM 5.3 Flash feels like a major step up from Qwen, so I'd say slightly better than DS731. I realize that's not exactly what the benchmarks say, but DS731 definitely flailed around on some of the more challenging things that GLM mostly gets for me.
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How did you setup the 2x dgx spark?
Pretty much just this, but tweaked to have I think 64 active conversations be cacheable, which drops the context pool to ~2M.
https://github.com/MiaAI-Lab/GLM-5.3-Flash-EXL3-2x-DGX-Spark...
I also had to downclock my Sparks (Asus) slightly, to 2000 Mhz. Otherwise they'd overheat and crash.
I've been using GLM 5.3 Flash as the planner and DS 4.1 as the task worker over the last month and the results have been very good, predictable, ridiculously cheap.
Why this model split? And are your work volumes so high that you prefer these cheaper models over a subscription or are you simply trying to avoid the 200/m? (which will only go up).
What reasoning level do you use for each?
Genuinely curious, why not use GPT Sol 6.1 high which has a better cost per task and intelligence ($0.32, 50) or Mimo 2.6 pro? ($0.13, 46) over GLM Max's $2.01, 45?
Sol won't do some things. Like, GPT freaked out yesterday because I needed to vector trace a logo and it said it would be a rights violation.
Then I have to work down the list: Grok, then Muse, then if those fail I make the Chinese one do it.
https://imgur.com/a/8LVmpLV
Seems like you should flip your list and save time. I'm not sure why everyone is so ideological about tools. If it works well I use it. Refusals are a non starter.
Imagine if my table saw refused to cut out certain shapes. I mean once they are internet connected some brand will probably try that...but just why put up with it at all?
Not a fan of Elon but latest Grok gets shit done and isn't a narc model from Anthropic/OAI.
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Not OP, but I've been trying out Sol 6.1 medium with hermes agent, and I have not been able to match usage costs to what I've been getting with glm 5.3 flash high.
Really don't know why, since I've offloaded thinking to the LLMs in this area of my life. Though even at medium, 6.1-Sol is noticeably better at most tasks I throw at it.
My experience has been that the "cost per task" estimate wildly underestimates the cost of top models on most real work, because the tasks (as I understand it) tend to be isolated bits of problem solving or code generation, which rewards writing code that works the first time and not generating too many reasoning tokens. Sol is great for both. However, a lot of real work involves tracking down bugs, working through tests, examining many parts of a codebase, refactoring... even with subagents and good tool use this tends to bloat context before the problem gets solved. Deepseek's absurdly low cache price really shines in these instances, but even GLM 5.3 Flash easily beats the top models on price in real world usage. Maybe that's different if you factor in subscriptions.
I'm OP and I have the Ollama basic $20/mo plan.
Been very happy with it although I will say Muse Spark 1.3 being free on OpenCode is my main model these days. If I run out of free credits on OC (which almost never happens) then I drop into GLM Flash 5.3.
Because it's not as good IRL. I find that DS Flash does just as good of a job on most things.
If you liked GLM 5.3, you would appreciate Deepseek V4 Flash a lot. In my personal opinion and use-cases, it outperforms GLM 5.3.
V4 isn't the newest DeepSeek, and GLM 5.3 non-Flash isn't the same ballpark
- DeepSeek V4.1 Flash has marginally better coding, and smaller experts (8B-In/16B-Out) with more compressed attention and KV cache, so it's faster but will have less expertise per task. It's a 552B model, so while it theoretically may contain more trained data it also requires more RAM (the experts are smaller to compensate for speed loss) making it harder to serve without the distributed KV cache DeepSeek hosts.
- GLM 5.3 Flash has marginally better agentic work, and has video input (DS doesn't), and its experts are 18B so there's more intelligence per task. It's a 320B model so it requires less RAM which will make it cheaper to serve. Considering it's a much smaller model, the higher intelligence indicates their architecture/training is better.
- GLM 5.3 (non-Flash) is far more intelligent than both; it's a 744B model with 40B experts, but it's text-only.
Model sizes aren't directly comparable, and they aren't even that relevant. I care about result quality, cost, and speed, not size. I switched to deepseek because it was giving me better coding results than GLM, and for far less cost. GLM is still better at planning.
In OpenCode, it shows up as "Deepseek V4 Flash Latest", so I assume it is the v4.1, so that's where the confusion comes from.
However, I disagree on the technical specifications. For most projects, a single prompt will get me what I want with good quality with Deepseek vs GLM 5.3. This is from real world experience with JS and Elixir. Perhaps for other tasks GLM 5.3 might be good. But, my biggest annoyance with most non-flash model providers - including GLM is they will dumb down the models based on load (like switching a non-flash model to flash model). That sucks because you expect a certain level of intelligence from them. Not a coin toss on "here's me hoping it works" and you expect a certain model only when you explicitly specify it by name.
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I'm the weirdo who is clinging onto GLM 5.2. 5.3 seems to have picked up the bad writing from Opus 5.
GLM 5.2 was the first open model that I was happy to use as my main. It's a fantastic model. 5.3 cost a lot more per task, was slower and didn't get that much better results for me, so I tried 5.3 Flash for a bit, it was ok, but didn't feel as magical as 5.2 and I have now landed on DeepSeek 4.1 Flash as my main.
It's not just you-- the claudisms in 5.3 are pathetic and drive me crazy, I was very sad to see it go in that direction and consider it a big step down in that respect.
BUT OTOH the performance of 5.3-flash are impossible to argue with-- it has the same pathetic claude behavior full of "Honest" and "lever" and "load bearing" tripe... but it's scortchingly fast on hardware that is pretty slow running 5.2/5.3.
As a result I use 5.3-flash a lot, in spite of feeling kind of gross about its language. I don't enjoy using it the way I enjoyed GLM-5.2, but its pretty effective.
I don't think that's likely, given the close release dates of both? I wonder if 5.3 just choked on too much RL slop, independently of Opus 5.
Stupid question probably because I’ve gotten out of sync with the new releases but is Flash better or worse than vanilla 5.3? I’ve been using 5.3 extensively (and maybe ironically have used flash only for viewing images which I have as a skill in Pi because 5.3 vanilla seemingly doesn’t support vision). I find it generally competent but still very far behind opus 5.5. I’ve been trying to avoid Anthropic but the lift of opus vs GLM is enough that I end up coming back to it. I feel like currently there’s no comparison.
anyway, tldr I would not be happy if stuck with vanilla 5.3 so is flash better and should I move to that?
GLM 5.3 Flash is better if you need native multi-modal capabilities, higher speed, and lower costs, but worse than vanilla 5.3 in raw reasoning and for complex programming.
Have you compared to deepseek v4.1 flash? It’s so good, at a ridiculous price