Artificial analysis shows this: underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, both of which you can run on a single B300 . I think Meta has a bit of catching up to do in the open-weight space.
As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases. I would really like them to start providing comparisons at low and medium reasoning levels across these models because those tend to be the ones that actually get used in production.
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).
Garmin.img thing is the interesting, but not really on my Experience.
Been running the same stuff on GLM 5.3 Flash, Deepseek v4p, Qwen 3.x model and Mimo v2.x pro model. Mimo faster yeah, but its hallucinate more and losses the thread easier. And when the context window is not limited, when usage of >200k token, is loss context. even he didnt know what im sayin.
Also, sometimes he Had a Loop over and over, for the same tool call (on my case is Grep and Read. >15x looping) until is burning a bunch of context for nothing. GLM and Deepseek doesn't do that (but the deepseek Hallucinations is huge asf too).
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.
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,
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.
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).
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?
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?
They're not using gas generators to run the other models?
Gas generators are common in new data center buildouts. I'm not a fan, because they are insanely loud, but they do prevent the data centers from weighing down the grid and raising rates for all the neighbors.
This is absurd nonsense, and needs to be quelled wherever espoused.
Dams aren't as a nuclear explosion. They don't burst into appearance, in a way where animals cannot walk away from the rising water. Birds lose nests all the time (wind knocks down trees, for example) and they merely build another. And they only need nests to rear young, and those fall out of trees so they... build another.
What happens in reality, and not in fantastical, hyperbolic renditions of the truth, is that one ecosystem replaces another. Where before was river, is now lake. And this is an entirely natural process. There have been cases of earthquakes causing river blockage, and of course the reverse!
And of course our friendly beaver blocks rivers all the time, sometimes to the extent that it can be seen from space. Rivers being blocked is a natural, normal, common practice.
But I suppose you hate all the beavers too? Do you raise fist to air, shake it in emotive angst, and rail against the horrors of natural geological processes too? Do you hunt the noble beaver, too?
How about instead of dismissing, deriding, and debasing one of the most environmentally friendly methods of power generation, you get behind it? How about instead of suppressing a hydroelectric dam, which means people will build Ng generators, or use coal, you support them.
Or would you prefer nuclear, for that stable, steady, backing, of which windmill and our friendly sol does not provide? Or would you prefer massive acres of batteries, instead?
I beg you sir, please cease this mindless chatter!
(no I don't normally talk like this, but what the hell, a but of levity may bring less conflict here)
This was intended to be absurd nonsense (or as I call it, irony).
I mean, if you think about this, burning stuff is also a part of nature, wildfires happens regularly, volcanos active right now, etc. Burning fossils like gas was(from caveman burning wood) and is still most popular way to generate energy among humans. I don't get what's so special about datacenters people find compared to any industrial facilities to fuel hateboner on most absurd nitpicks.
Arsenic is a part of nature, but it’ll still kill you. Burning the massive amount of co2 stored underground during the carboniferous period is not the same as random forest fires that burn co2 fixed a few decades ago.
Pretty sure you're not able to use many models, besides local, if you need to guarantee where the energy produced powers them. I don't align with much of what's going on in frontier AI today. And I don't actually use it as much as I had been as I've invested in local hardware. But I'm still curious and continue to test frontier models on different tasks. My limited experience with Grok is that it's boundaries are much less rigid than Anthropic and OAI, IME.
But if the planet is the only consideration here you can't use any in of these models when pointing a finger at one. They're putting them wherever they can. And I'll be one of the first one to cheer some of these systems being dismantled after the fallout / when the US has some political leaders that have a spine again.
I don't buy that - you can't distinguish. This sounds like a convenient way for people to say "my choice is better for the planet" with basically no basis.
You've moved the goalpost and declared victory my guy. I literally gave you an option, that isn't meta, and is run on renewable energy. The fact that it's hard to get clear information doesn't negate the fact that there are differences, and we should seek to find the best option rather than giving up and accepting whatever bad option is nearest or shiniest.
Let me clarify my perspective here. You put the frontier providers at the top. That's probably representative of >80% of the total, global, inference. The other ones you mention? They're likely showcasing "green AI" as a differentiator, but comparatively, they likely represent a percent of global capacity.
And you don't get to put a feel good landing page from Meta and "declare victory" (I didn't) just the same. It means nothing because there isn't any accountability. Nice try.
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.
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.
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.
> 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).
Where are you seeing this? This would be sufficient for me to stop using that provider entirely, immediately.
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.
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.
You don't need to directly compare the same reasoning levels. Today you can compare Sol on Low vs Haiku on Max, for example, but you can't look at GLM 5.3 High, only Max.
For some tasks I genuinely feel Opus 5.5 does better on low than medium or high. The trouble, as always, is figuring out which.
Edit: I realize after commenting that you're not suggesting medium is useless, but just arbitrary and differs between models. Personally I don't want to directly compare mediums between models, I just want to be able to compare across effort levels.
i run opus on medium, sonet on high, and haiku on high for implementation based how complicated is
for planing and specing and review opus is on high usually, except if if the task is contained.
did not try to run it on low, will have to try it.
Opus 5.5 on low is good if you are relatively sure a given task is straightforward and time spent gathering background information is purely wasted or possibly even detrimental. Opus 5.5 low seems great at doing the most obvious thing based on the prompt without thinking much about it.
I agree - Opus 5.5 on low is a really great model if it has some direction and the tasks aren't complex.
If the tasks are complex cranking Opus up to high or xhigh and getting it to delegate to Opus low subagents is pretty effective and much faster than running everything at high.
It shows the time to complete a task versus intelligence index, which is much more important for my use-case as I do quick back and forths usually. Someone on here suggested it to me a while ago. It also captures speed and token efficiency nicely, since both of these decrease time per task.
Yep, just benchmarked it against GLM5.3 Flash on my own local bench and it's slower in TTFT, tok/s and concurrency. It also scored lower.
Tried different reasoning levels and didn't really see any difference but GLM 5.3 flash only really works high, max or none so I couldn't really compare the lower efforts which appear to work on Spark.
this is properly bad as you can run glm on 6000pro, at (in my eyes) pretty good quant, and a q2 on a 5090. im suprised at how good 5.3 flash is at some parts. i still think ds4.1 flash is better but it is more demanding. i hope meta pushes something in a range of 300-400b with a 15-20b active parameters. i know its a crowded space. but it would be fast and probably mostly good enough.
> As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases. I would really like them to start providing comparisons at low and medium reasoning levels across these models because those tend to be the ones that actually get used in production.
The frustrating thing is they started doing this for the big labs—you can see their benchmarks for all reasoning levels of Sol 6.1 or Haiku 5.5. They just don't seem to do it for any of the open weight models.
I feel like reasoning is a useless metric, and we should instead focus on results, and time to achieved said results. That is a more useful metric overall.
> Artificial analysis shows this: underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, both of which you can run on a single B300
You don't need anywhere near a B300 to run Qwen 3.8-Flash-Next in its Q8 largest variant published as a GGUF by unsloth. It fits very comfortably in a 256GB system.
My immediate reaction is that it’s weaker on coding than other open-weight models and that coding is a primary use case for running locally (not the only one, but surely a major one). That said, it takes a lot to get full Kimi and GLM running locally.
very true. when configuring custom agents for enterprise, we almost always default to none or low reasoning knobs. The added latency from medium or higher reasoning levels compared to the improvement in response quality is a diminished return.
it’s quite suspect to claim that a model is ‘underperforming’ so resoundingly, especially after so little time since release.
there’s this objectivist approach of reducing llm quality to a single dimension, furthermore one with measurability bias.
my thesis is that the chinese models beat the american models on benchmarks, on quantitative measures, but probably do worse in subjective measures. to put an example, compare a chinese phone with an iphone, the specs are great, but the OS? the materials? the privacy posture?
so will chinese llms have great specs and benchmarks, but will miss the mark when it comes to the actual quality in practice when your input stops looking like the benchmarks it was trained on.
America number one, it’s the chinese that play catch up with clones, not the other way around
You're asking a question, which if people follow up with searching may lead them to the implied point. Just post the numbers if you think the numbers are interesting ¯\_(ツ)_/¯
Um a 512GB m5 max Mac Studio is what 25k? A B300 GPU and the computer to use it is what? Double? Triple that? Apples v oranges my friend. Apples v oranges.
As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases. I would really like them to start providing comparisons at low and medium reasoning levels across these models because those tend to be the ones that actually get used in production.