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.
- 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.
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.
Where are you seeing this? This would be sufficient for me to stop using that provider entirely, immediately.
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.
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.
Then I have to work down the list: Grok, then Muse, then if those fail I make the Chinese one do it.
Source, to preempt the inevitable musk stans: https://www.scientificamerican.com/article/spacexais-colossu...
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.
how do you still have a job?
How do you feel destroying our planet and murdering innocent creatures for your entertainment clanker?
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)
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.
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.
This is simply a purity test. Just because they’re all bad, doesn’t mean there isn’t better and worse options.
Feel free to elaborate.
Anthropic: no sustainability goals or claims
OpenAI: no sustainability goals or claims
SpaceX AI: no sustainability goals or claims
Some other non-lab inference providers:
https://regolo.ai/ Eu based, 100% renewables powered
https://greenpt.com/us/sustainability Eu based, allows you to track carbon cost of inference.
Acting like there is no difference between bad things is how you get worse things.
This also highlights my point: the norm is that the bulk of the inference and training providers have no goals.
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.
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?
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.
anyway, tldr I would not be happy if stuck with vanilla 5.3 so is flash better and should I move to that?
The full 5.3 is only really needed for super deep bugfixes/profiling passes, or ridiculous reverse-engineering in 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).
Is your Mimo do the same too??
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 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,
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.
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.
Just accept whatever the provider names as "medium" and compare the accuracy between models vs the time it takes to complete a task.
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.
did not try to run it on low, will have to try it.
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.
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.
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.
1) time budget model gets (5min / task) -- this became a real problem with opus/astra/fable at high+
2) tuning which effort level we report on (when not all)
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
They do other reasoning levels for some models.
Here is Sonnet 5.5 across the full range of efforts from low to max: https://artificialanalysis.ai/?models=claude-sonnet-5-5-low%...
Opus 5.5 low to max: https://artificialanalysis.ai/?models=claude-opus-5-5%2Cclau...
Here is Muse Spark 1.3 Max and Xhigh: https://artificialanalysis.ai/?models=muse-spark-1-3%2Cmuse-...
I'm guessing it depends how much the vendor wants to contribute. Running these benchmarks isn't cheap!
Similarly, AFAIK evals tend to be performed at native precision, while anyone who cares about speed is probably running quantized.
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.
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.