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.
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.
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.
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.
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?
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.
Indeed - Even with llama.cpp you can store n-grams to disk and use it. I m using unsloth's llama fork atm but a new llama release is out that may be better - hadn't had time to test it yet.
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.
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.
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.
Earlier this year, Step 3.5 Flash and then Step 3.7 Flash were easily my favourite local models. Step 5 is far too large to run on my DGX Spark-like, but I’m excited to try it out. It’s benchmarks look promising, and I’m hoping it’s a lot faster than GLM is
can I be frank? It's a pile of rust. Nowhere near Muse 1.3, weaker than Ling 3.1, probably just a bit over Nemotron 3.0 Ultra, and that's it. Totally underwhelming - broken prompt responses, garbled repo state, derailed trajectories, hallunies, you name it. Maybe good for some trivial stuff, but that's it. Terribly slow on top of that, quite pricey after the free trial ends, not suitable for local use, and not good enough for cloud. We already have stronger free models, we have faster models, smarter models, cheaper models...
Also, benchmarks are obviously benchmaxxed. Once again - on high effort it feels basically just a notch above Nemo. Get a real task, launch it, and cry in terror.
AND I'm not even comparing it to the flagships - y'know we just got Muse Wonder literally hours ago?!
37 comments
[ 6.4 ms ] story [ 29.7 ms ] threadThis 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.
The Assbench: https://news.ycombinator.com/item?id=49807688
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.
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?
It’s also an open model. Even if you only use Opus and Sol, these open models help push them and the frontier.
It is fully possible to run the 180B Owen model on strix halo using strata and even get reasonable speed out of it.
https://github.com/Niko1221/Strata
Edit: bummer, didn’t know it’s 600B-A27B. No way to run that on 228GB.
How am I supposed to know if it's even worth my time?
AND I'm not even comparing it to the flagships - y'know we just got Muse Wonder literally hours ago?!
tl;dr underwhelming