I've played with this a bit and it's ok. I'd place it somewhere around sonnet 4.5 level, probably below. But with this aggressive pricing you can just run 3 copies to do the same thing, choose the one that succeeded and still come out way ahead with the cost. Not as great as following instructions as Claude models and can get lost, but still "good enough".

I'm very happy with using it to just "do things". When doing in depth debugging or a massive plan is needed, I'd go with something better, but later going through the motions? It works.

Would it kill them to use the words "AI coding agent" somewhere prominent?

"MiniMax M2.1: Significantly Enhanced Multi-Language Programming, Built for Real-World Complex Tasks" could be an IDE, a UI framework, a performance library, or, or...

I think people should stop comparing to sonnet, but to opus instead since it's so far ahead on producing code I would actually want to use (gemini 3 pro tends to be lacking in generalization and wants things to be using it's own style rather than adapting).

Whatever benchmark opus is ahead in should be treated as a very important metric of proper generalization in models.

Has anyone used this in earnest with something like OpenCode? Over the past few months I’ve tested a dozen models that were claimed to be nearly as good Claude Code or Codex, but the overall experience when using them with OpenCode was close to abysmal. Not even a single one was able to do a decent code editing job on a real-world codebase.

The weights got released on huggingface now.

https://huggingface.co/MiniMaxAI/MiniMax-M2.1

> MiniMax has been continuously transforming itself in a more AI-native way. The core driving forces of this process are models, Agent scaffolding, and organization. Throughout the exploration process, we have gained increasingly deeper understanding of these three aspects. Today we are releasing updates to the model component, namely MiniMax M2.1, hoping to help more enterprises and individuals find more AI-native ways of working (and living) sooner.

This compresses to: “We are updating our model, MiniMax, to 2.1. Agent harnesses exist and Agents are getting more capable.”

A good model and agent harness, pointed at the task of writing this post, might suggest less verbosity and complexity— it comes off as fake and hype-chasing to me, even if your model is actually good. I disengage there.

I saw yall give a lightning talk recently and it was similarly hype-y. Perhaps this is a translation or cultural thing.

I still can’t figure out what it does

Very anecdotal but for me this model has very weak prompt adherence. I compared it a tiny bit to gemini flash 3.0 and simple things like "don't use markdown tables in output" was very hard to get with m2.1

Took me like 5 prompt iterations until it finally listened.

But it's very good, better than flash 3.0 in terms of code output and reasoning while being cheaper.

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> It exhibits consistent and stable results in tools such as Claude Code, Droid (Factory AI), Cline, Kilo Code, Roo Code, and BlackBox, while providing reliable support for Context Management mechanisms including Skill.md, Claude.md/agent.md/cursorrule, and Slash Commands.

One of the demos shows them using Claude Code, which is interesting. And the next sections are titled 'Digital Employee' and 'End-to-End Office Automation'. Their ambitions obviously go beyond coding. A sign of things to come...

Their site crashes my phone browser while scrolling. Is that the expected quality of output of their product?

I’ve spent a little bit of time testing Minimax M2. It’s quite good given the small size but it did make some odd mistakes and struggle with precise instructions.

I won't say it's same on the level of claude models but it's definitely good at coming up with frontend designs

I used gemini-3-pro-preview on Deepwalker [0]. It was good, then switched to gemini-3-flash, It's ok. It gets the job done. Looking for some alternatives such as GLM and Minimax. Very curious about their agentic performance. Like long running tasks with reasoning.

[0]: https://deepwalker.xyz

How is everyone monitoring the skill/utility of all these different models? I am overwhelmed by how many they are, and the challenge of monitoring their capability across so many different modalities.

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One of the cited reviews goes:

“We're excited for powerful open-source models like M2.1 […]”

Yet as far as I can tell, this model isn’t open at all. Not even open weights, nevermind open source.

That they are still training models against Objective-C is all the proof you need that it will outlive Swift.

When is someone going to vibe code Objective-C 3.0? Borrowing all of the actual good things that have happened since 2.0 is closer than you'd think thanks to LLVM and friends.