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> late last year/early this year

That's an eternity when it comes to coding models.

In my personal experience, we've had almost a step change every ~3 months this year, at least for bigger one-shot tasks. For example looking at Gemini Flash 3.0 vs 3.5 vs 3.8, it went 5% -> 30% -> 75% on DeepSWE, all since the start of the year.

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Same here. It’s the first AI provider I actually gave money to, since they offered the model for free with a Mimo code for the first month or so, and it was great.

These days, there are more intelligent models like DS4.1, but Mimo is very obedient, so I plan things with another model and give the implementation to Mimo.

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I’ve been very pleased with DS 4.1 flash. Not so much the 4.0 models, but for coding (Rust) it’s been great so far (3 solid days of work).

I’ll give Mimo a try.

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MiMo is my backup whenever DeepSeek is down, had the price bump, is slow, etc.

UltraSpeed was absolutely awesome. I miss it.

DS 4.1 Flash is amazing. Well worth the extra cost.

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I am also using 2.5 and it is giving me solid results. Its available free on Openrouter
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Could you elaborate on how you check daily? Do you swap models for certain tasks?
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I've found that mimo v2.5 works for very basic things like a python script to do one thing, but it also is very 'dumb' compared to qwen 3.8-flash-next (I think the benchmark scores for terminal and coding specific benches back this up). And definitely not in the same class as like a GLM5.2 or 5.3. It's fast but makes basic mistakes that only get caught later.
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The fact I can run Qwen 3.8 Flash Next locally, forever (on my DGX Spark-alike) is genuinely shocking to me. It’s crazy good for how small it is. Fast, too.
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I made this 3D game in a day on the same setup with Qwen Code as agent: https://games.jonathanpage.com/

And I am not a web developer! It's an extraordinary model.

(Mouse and keyboard required)

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Yeah, I'm guessing you have a variant that fits in <128GB with 262k context? I have the unsloth Q8 GGUF of it here in a setup that with full context and ton of extra llama-server "--cache-ram" sits around 200GB RAM usage on a 256GB system, it's probably the best thing I've found for a 256GB class machine. Enough headroom for a rope/yarn extension to 524288 context if I need it.
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Yep, the engrams are on NVMe (the speed penalty was lower than I expected) and it is quantised to fit.

It’s good enough that I’m considering a second spark, or selling this and buying an M5 Ultra with 256GB for it

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May I ask why you ended up there instead of just using the heavy subsidized subscription. I’m actually curious.
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Mimo has subsidized subscriptions too
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How does it compare with DS 4.1 Flash in your experience, if you ignore the cost?
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2.5 Pro or the regular 2.5?

I always found that those Mimo models to be really good at tool calling and following instructions

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> GLM 5.3 Flash gets a job done well, etc. – but when I add cost of M-token in the ROI math, Jeez! MiMo is an order of magnitude better.

API may be expensive, but I do 900m tokens (95% cached, ~0.4% output) on Z.ai's $18/mo coding plan with GLM 5.3 Flash.

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I wouldn't call that inexpensive.

For comparison, I am currently at 6.6B tokens, 95% of monthly quota on a 10$ command code plan, mostly using DeepSeek flash 4.1, or some of the free models for easier tasks.

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How fast is it compared with the other Chinese models?
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They both are in the 50-100 tok/s range. The Mimo v2.5 Pro Ultraspeed beta could reach 1000 tok/s, hoping they can do something similar for the new model, it was amazing.
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Real?
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mimo 2.5 has been a big underperformer since shortly after it's release imo. i cancelled my sub after the first month. purposefully using 2.5 right now is just handicapping yourself for no reason.
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I understand now. You used the wrong word.
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Yeah, this Brazilian dude who has been a contributor here on HN longer than your anonymous account is shilling for a Chinese model company. Makes sense.
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Are you accusing them of astroturfing? Why is it strange for someone to say something topical?
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