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syntaxing 11 hours ago [-]
Step models were IMO the first local model you can run on 128GB shared memory that worked well. Really excited to see how it compares to Qwen Flash Next.
Edit: bummer, didn’t know it’s 600B-A27B. No way to run that on 228GB.
>Draw a Hacker News-style comment thread. Top comment by a user named "pelican_enjoyer": "Without the pelicans I don't know what to think." Reply from "minimaxir" in a grumpy tone: "Since people keep doing it: no, you don't have to make an allusion to Simon's pelicans every time a Hacker News thread about a new LLM pops up. It's a lower-effort joke than even Reddit memes." Beside the thread, show a pelican riding a bicycle, looking smug.
minimaxir 17 hours ago [-]
Since people keep doing it: no, you don't have to make an allusion to Simon's pelicans every time a Hacker News thread about a new LLM pops up. It's a lower-effort joke than even Reddit memes.
minimaxir 14 hours ago [-]
Re: the edit where the OP added an image: I'm more bemused than offended.
hypfer 16 hours ago [-]
I'd just read it as social friction, just like I'd read your comment as that very same thing.
This is the consensus mechanism doing its job, essentially.
__
Though to be fair, the way I frame it assumes no connections between nodes and independent choices, when in reality, we have groups supporting each other.
So it's not necessarily the best mechanism, as social cohesion and other such dysfunctions might be steering away from the objectively correct solution through not necessarily rational biases.
Or rather not necessarily rational when viewed in just the specific context, but possibly rational when zooming out and considering whole-subsystem health.
minimaxir 16 hours ago [-]
Low-effort content is generally discouraged on Hacker News because it lowers the quality of the site. It's not a deep commentary on socioeconomics.
hypfer 16 hours ago [-]
Like posting the exact same personal brand-building under each new model you mean?
As said, there is no right or wrong here. Well, technically there is and it is my opinion (obviously), but if we take a step back, it's exactly what I just described and you (unfortunately) discarded through bulldozing.
The """"thought leaders"""" will have to live with the fact that some people just don't think that their work is adding all that much value. It's a bit unpleasant for the ego of course, but that's kinda the trade when making money with fluff.
tomrod 6 hours ago [-]
I love the pelicans almost as much as the penguins. One day we shall see the max positive of minimaxir ;)
behole 16 hours ago [-]
Pelican Police. Cry more.
Razengan 15 hours ago [-]
There was a better alternative, buried by dumb HN censorship:
He’ll probably respond quicker if you get his name right
robertlane0 12 hours ago [-]
Well, according to Artificial Analysis (which I'll admit I've been using as a bit of a mental crutch to avoid comparing models myself, so YMMV), it's smarter and slightly cheaper than Gemini 3.8 Flash, which has been my benchline for "cheap and smart enough", I'll give it a try on OpenCode for the week but I'm not sure I'll be compelled enough to switch from Muse Spark 1.3.
jstummbillig 15 hours ago [-]
Why is that interesting?
theturtletalks 15 hours ago [-]
Stepfun made a close to SOTA model with 3.5. They got overtaken quickly but have shown enough to be given attention when a new model releases.
It’s also an open model. Even if you only use Opus and Sol, these open models help push them and the frontier.
UncleOxidant 7 hours ago [-]
Seems like it was an under-noticed model back when it came out because there were so many new Qwen models coming out around the same time. I tried it some on my Strix Halo box, but it was right on the edge of fitting.
theturtletalks 5 hours ago [-]
3.5 Flash was decent, but I was using it on OpenRouter. What model are you running on your Strix Halo nowadays?
Iolaum 4 hours ago [-]
Qwen-3.8-flash-next @Q4
ssivark 3 hours ago [-]
Back in their 3.5/3.7 era [1], they had a small+fast model, specifically eschewing knowledge, and instead focused on "general intelligence" like managing tool calls, orchestrating sub-agents, etc. Could be a very good substrate for running a personal assistant (time will tell what's the right approach); so I'm curious to try that out and see how they've come along since then.
After some quick tests, it likely isn't. Additionally, Space Bunny Alpha had hints it was a Minimax-variant model.
james2doyle 12 hours ago [-]
Its been on ZenMux for at least 2 weeks so I doubt they are hiding
MisterMunchkin 15 hours ago [-]
I like trying new models but I wish we’d get something actually new. Like a new architecture or something. LLMs are just so sloppish. We can do better.
famouswaffles 13 hours ago [-]
Talk is cheap. You have to actually beat transformers first. All alternate architectures that have sprung up are sidegrades at best.
Artificial Analysis has some very specific biases or perspectives on what they are measuring, so I wouldn't take their benchmarks as the final word on model quality.
Composite indices are only useful for model companies which want to build one horizontal capability for N use cases, or naive users who don't want to bother with the effort of carefully pairing models with use cases. If you're a power user looking to understand and make deliberate choices, then you want pointed evaluations -- not general composite indices. You wouldn't hire the same person to do your taxes and mow your lawn, so why is it any different with LLMs? Only if you come at the problem with the folklore around "AGI" do you start making composite benchmarks.
As I mentioned in a sibling comment... Back in their 3.5/3.7 era [1], Stepfun had a small+fast model, specifically eschewing knowledge, and instead focused on "general intelligence" like managing tool calls, orchestrating sub-agents, etc. Could be a very good substrate for running a personal assistant (time will tell what's the right approach); so I'm curious to try that out and see how they've come along since then.
I've always wondered if there were niches that some models are better at. I use them for software, electrical and mechanical engineering plus other things. What are some notable subject-specific differences you found?
RussianCow 15 hours ago [-]
It's fast. The average speed is 115 tokens/sec according to OpenRouter. I haven't tested the model to see how it is in practice, but I'd certainly pay a little extra for faster inference.
Edit: Though the average latency of 1.5s isn't very low, so it might not be that fast in practice for agentic work. Also, I don't know how much thinking it does, as that's generally been the drawback to Chinese models.
celrod 14 hours ago [-]
Using Artificial Analysis
Model | Reasoning | Intelligence Index | Artificial Analysis million output tokens for the intelligence index
-|-|-|-
Step 5 | ? | 44 | 160
GLM 5.3 | Max | 45 | 210
MiMo V2.6 Pro | ? | 46 | 140
Kimi K3 | Max | 44 | 160
Qwen Max 0902 | ? | 45 | 190
DeepSeek 4.1 Flash | Max | 39 | 250
GLM 5.3-flash | Max | 42 | 180
GPT-6 Astra | Low | 46 | 10
It looks reasonable by open model standards. This doesn't capture the fact that DeepSeek and Step 5 have much higher token/s than the rest, other than MiMo Ultraspeed. MiMo V2.6 is either slow but cheap, or fast but expensive.
Just based on these numbers, it looks good.
Astra-low is one of the fastest and cheapest because it doesn't use many tokens, but I've never tried it.
I liked DeepSeek and GLM when I used them.
Edit: bummer, didn’t know it’s 600B-A27B. No way to run that on 228GB.
-------------
https://postimg.cc/c6L2MjSt
12m 0s and $0.28
>Draw a Hacker News-style comment thread. Top comment by a user named "pelican_enjoyer": "Without the pelicans I don't know what to think." Reply from "minimaxir" in a grumpy tone: "Since people keep doing it: no, you don't have to make an allusion to Simon's pelicans every time a Hacker News thread about a new LLM pops up. It's a lower-effort joke than even Reddit memes." Beside the thread, show a pelican riding a bicycle, looking smug.
This is the consensus mechanism doing its job, essentially.
__
Though to be fair, the way I frame it assumes no connections between nodes and independent choices, when in reality, we have groups supporting each other.
So it's not necessarily the best mechanism, as social cohesion and other such dysfunctions might be steering away from the objectively correct solution through not necessarily rational biases.
Or rather not necessarily rational when viewed in just the specific context, but possibly rational when zooming out and considering whole-subsystem health.
As said, there is no right or wrong here. Well, technically there is and it is my opinion (obviously), but if we take a step back, it's exactly what I just described and you (unfortunately) discarded through bulldozing.
The """"thought leaders"""" will have to live with the fact that some people just don't think that their work is adding all that much value. It's a bit unpleasant for the ego of course, but that's kinda the trade when making money with fluff.
The Assbench: https://news.ycombinator.com/item?id=49807688
It’s also an open model. Even if you only use Opus and Sol, these open models help push them and the frontier.
[1] https://news.ycombinator.com/item?id=47069179
Better luck next time.
Composite indices are only useful for model companies which want to build one horizontal capability for N use cases, or naive users who don't want to bother with the effort of carefully pairing models with use cases. If you're a power user looking to understand and make deliberate choices, then you want pointed evaluations -- not general composite indices. You wouldn't hire the same person to do your taxes and mow your lawn, so why is it any different with LLMs? Only if you come at the problem with the folklore around "AGI" do you start making composite benchmarks.
As I mentioned in a sibling comment... Back in their 3.5/3.7 era [1], Stepfun had a small+fast model, specifically eschewing knowledge, and instead focused on "general intelligence" like managing tool calls, orchestrating sub-agents, etc. Could be a very good substrate for running a personal assistant (time will tell what's the right approach); so I'm curious to try that out and see how they've come along since then.
[1] https://news.ycombinator.com/item?id=47069179
I've always wondered if there were niches that some models are better at. I use them for software, electrical and mechanical engineering plus other things. What are some notable subject-specific differences you found?
Edit: Though the average latency of 1.5s isn't very low, so it might not be that fast in practice for agentic work. Also, I don't know how much thinking it does, as that's generally been the drawback to Chinese models.
Model | Reasoning | Intelligence Index | Artificial Analysis million output tokens for the intelligence index -|-|-|- Step 5 | ? | 44 | 160 GLM 5.3 | Max | 45 | 210 MiMo V2.6 Pro | ? | 46 | 140 Kimi K3 | Max | 44 | 160 Qwen Max 0902 | ? | 45 | 190 DeepSeek 4.1 Flash | Max | 39 | 250 GLM 5.3-flash | Max | 42 | 180 GPT-6 Astra | Low | 46 | 10
It looks reasonable by open model standards. This doesn't capture the fact that DeepSeek and Step 5 have much higher token/s than the rest, other than MiMo Ultraspeed. MiMo V2.6 is either slow but cheap, or fast but expensive. Just based on these numbers, it looks good. Astra-low is one of the fastest and cheapest because it doesn't use many tokens, but I've never tried it. I liked DeepSeek and GLM when I used them.