I get decent results with Kimi, but I agree with your overall premise. You do need to realise that while you can save money on a lot of tasks with those models, for the hardest tasks the "sticker price" of cost per million tokens isn't what matters.

It's also worth noting that the approach given in the link also benefits Sonnet and Opus. Not just as much - they are more forgiving - but put it in a harness that allows for various verification and repair and they too end up producing much better results than the "raw" model. And it's not clear that a harness around MiniMax, Kimi, or Qwen can measure up then.

I use those models a lot, and hope to use them more as my harnesses get better at discriminating which tasks they are cost effective for, but it's not straightforward to cost optimize this.

If I cared about running everything locally, then sure, it's amazing you can get to those kinds of results at all.

I won’t use anything less than the SOTA. It tried using Opus 4.6 medium and immediately regretted it. High messes up enough.

Yup, they do quite poorly on random non-coding tasks:

https://aibenchy.com/compare/minimax-minimax-m2-7-medium/moo...

> I’d encourage devs to use MiniMax, Kimi, etc for real world tasks that require intelligence.

I use MiniMax daily, mostly for coding tasks, using pi-coding-agent mostly.

> The down sides emerge pretty fast: much higher reasoning token use, slower outputs, and degradation that is palpable.

I don't care about token use, I pay per request in my cheap coding plan. I didn't notice slower outputs, it's even faster than Anthropic. Degradation is there for long sessions with long contexts, but that also happens with Anthropic models.

> Sadly, you do get what you pay for right now. However that doesn’t prevent you from saving tons through smart model routing, being smart about reasoning budgets, and using max output tokens wisely. And optimize your apps and prompts to reduce output tokens.

Exactly. For my use case, I get 1500 API requests every 5 hours for 10€ monthly. I never hit the limit, even during the intensive coding sessions.

What I notice is, while Opus and Sonnet feel better for synthetic benchmarks, it doesn't matter in the real world. I never put so much effort into coming up with a perfect problem spec like the ones in benchmarks. I don't craft my prompts for hours expecting the LLM to one-shot a working program for me. And that's exactly what all those benchmarks are doing. And that's where Anthropic tools shine in comparison to cheaper Chinese models.

When it comes to the real world, where I put my half-baked thoughts in broken English in a prompt and execute 20 prompts in half an hour, the difference between Opus, Sonnet, and MiniMax is minimal, if at all. There, I don't want to think about costs and token savings and switching between different Anthropic models. I just use MiniMax, and that's it.

Yes, MiniMax sometimes gets stuck. Then I switch to Opus to unblock it. But the same happens if I use Opus the whole session. It gets stuck eventually, and model switch is sometimes required to get a fresh perspective on the problem.

The only difference is, using Opus or Sonnet quickly eats up my budget, while with MiniMax I have basically unlimited usage (for my coding use case) for 10€ per month.

Kimi's been one of my goto options lately and it oftentimes outperforms both Claude and GPT in debugging, finding the actual problem immediately while the other two flail around drunkenly.

It does have some kind of horrible context consistency problem though, if you ask it to rewrite something verbatim it'll inject tiny random changes everywhere and potentially break it. That's something that other SOTA models haven't done for at least two years now and is a real problem. I can't trust it to do a full rewrite, just diffs.

Minimax 2.7 is fine for most web stuff. It's slightly worse than Claude at backend, but works great for frontend.

They're all slop when the complexity is higher than a mid-tech intermediate engineer though.

yea, they are still useful. But yea not close to Claude or GPT. But works good for simple changes. I use a combo of minimax and codex

i find kimi to be very very good, minimax not so much

Agreed.

They are equivalent of frontier models 8+ months ago.

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