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
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.
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 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.
>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.
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 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.
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.
It’s also an open model. Even if you only use Opus and Sol, these open models help push them and the frontier.
Better luck next time.
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.
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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