I've been using M2.7 through the Alibaba coding plan for a bit now, and am quite impressed with it's coding ability, and even more impressed when I see how small it is. Fascinating really, makes me wonder how big the frontier models are.
What's people's experience of using MiniMax for coding?
I had a really bad time with it. I use (real) Claude Code for work so I know what a good model feels like. MiniMax's token plan is nice but the quality is really far from Claude models.
I needed to constantly "remind" it to get things done. Even for a four sentence prompt in a session that is well below the context window, MiniMax would ignore half of it. This happens all the time. (This is Claude Code + MiniMax API, set up using official instructions)
Basically, if I say get A, B and C done, it will only do A and B. I say, you still need to do C, so it does C but reverts the code for A.
Things that Claude can usually one shot takes 5 iterations with MiniMax.
I ended up switching to Claude to get one of my personal projects done.
"Helped build itself" is a bit of a stretch here, it makes it sound as if the model was doing lasting self-improvements.
What the article describes is that the model was able to tweak to its own deployment harness (memory, skills, experimental loop etc) to improve performance on benchmarks. While impressive, it's not doing any modifications to its own weights by e.g. modifying the training code.
In addition to this conversation already having been started at https://news.ycombinator.com/item?id=47735348 yesterday, MiniMax M2.7 is not open source. The open weights have been released, which is definitely good and follows some of the spirit of open source, but isn't the same thing.
In my experience, even the MiniMax M2.5 is a very capable model with decent capabilities and with some hand holding, can do good investigation into an issue deep down multiple layers of a software stack given you keep asking right questions.
I am pretty sure MiniMax M2.7 would be much better.
36 comments
> Non-commercial use permitted based on MIT-style terms; commercial use requires prior written authorization.
And calling the non-commercial usage "MIT-style terms" is a stretch - they come with a bunch of extra restrictions about prohibited uses.
It's open weights, not open source.
https://huggingface.co/unsloth/MiniMax-M2.7-GGUF
I've been using M2.7 through the Alibaba coding plan for a bit now, and am quite impressed with it's coding ability, and even more impressed when I see how small it is. Fascinating really, makes me wonder how big the frontier models are.
I had a really bad time with it. I use (real) Claude Code for work so I know what a good model feels like. MiniMax's token plan is nice but the quality is really far from Claude models.
I needed to constantly "remind" it to get things done. Even for a four sentence prompt in a session that is well below the context window, MiniMax would ignore half of it. This happens all the time. (This is Claude Code + MiniMax API, set up using official instructions)
Basically, if I say get A, B and C done, it will only do A and B. I say, you still need to do C, so it does C but reverts the code for A.
Things that Claude can usually one shot takes 5 iterations with MiniMax.
I ended up switching to Claude to get one of my personal projects done.
What the article describes is that the model was able to tweak to its own deployment harness (memory, skills, experimental loop etc) to improve performance on benchmarks. While impressive, it's not doing any modifications to its own weights by e.g. modifying the training code.
I am pretty sure MiniMax M2.7 would be much better.
> That is not a benchmark result. That is a different way of thinking about how AI models get built.
tiresome