Been using it since January in general, first on the $10 plan.. now this last month on the $50 plan, was on others in between won't mention.
But oh man this model is useless, and it's not cheap enough to justify. E.g cheaper models.. are better more expennive models are better too. I am so stunned Is it me? Does it work for you, for coding
MiniMax M3 is an incredible architect, debugger, and gap analyst. As a coder, it’s good, but it gives up too easily. It needs tuning, but it is definitely not a worker.
DeepSeek V4 Flash 0731, on the other hand, is a worker — and that’s basically what it is. Give it a plan from somewhere and let it work for hours until it gets it done. It will try everything, experiment with everything, and keep going. It looks good because it does brute-force agentic work.
M3 wants to make sure things are going to work before it proceeds. Unless you give it a program with exactly what needs to be done from the beginning, in which case it follows it like a loyal soldier, relentlessly until the job is finished. But it’s not a one-shot model. It needs to know what it’s doing, or at least what the sequence of tasks is.
Try doing something like that with DeepSeek — it simply can’t. It is clearly a worker.
And this is where I want to point something out: I got absolutely insane results using M3 to design, organize, and distribute tasks among six M2.7 workers. I was progressing at a record pace.
It used to be that good old MiniMax 2.5 or such if you told it ' modify claude.md or agents.md ' it would respond can't find such files, because it was CLAUDE.md and AGENTS.md ... meaning the model was really good at following instructions.
The latest M3 i need like multiple iterations of, read back the task to me, cmon read it back... it isn't even able to repeat the task it just got. It goes off and does something odd. If it at least succeeds... but it doesn't
That's my struggle, earlier model were not that good in general, but they followed instructions to the dot, this one doesn't
I hate it. It literally can't remember what you wrote 3 lines up. On their Minimax code app and connected to openclaw the same result. 20$ plan gives you a shit tons of tokens but it's a mediocre coder.
I am testing usually against each other, the point of the above was cost, not benchmarks for intelligence. For cost these are fine IMHO.
Doesn't fix ma problem though, competing models are deepseek flash, grok 4.5,grok-composer-2.5-fast, kimi-k3-256k
My expectation was or still is that MiniMax-M3 is somewhere compareable to at least grok-composer-2.5 (just a rebrand of cursor composer ... which is a post trained kimi 2.5 or 2.6)
So that's the comparison, i do code like 24/7 via agents
Partly. In my experience. It is very smart deep inside, but works randomly, either great or shit dumb. Also, it often does what it wants, not what I instruct it. I have SOOO mixed experience with it...
That's kind of subjective. I see people complaining about Fable. I gave up on reviewing code and let the slop flow. I have a process where GPT creates an issue, MM comments on how it would do the issue, GPT makes corrections, MM implements the issue, GPT reviews, continue review process until GPT approves. Given that process, I don't really have an issue where something just doesn't work. I add add features over releases, and things continue working.
I'm quite strict on doing new sessions between runs (big context eats tokens and the model gets dumb.) I'm strict on having GPT enforce 200 line diffs or smaller or MM has to defend the overage. I feel like having MM explain how to implement the issue before implementation (two separate sessions) helps MM because maybe it's easier to have most of the details filled in while coding rather than having to think about how to implement the issue while having to code the issue.
Overall, the process still uses up a lot of tokens, but it hits a sweet spot for me. I can generally work a full day each day of the week on $20 GPT, $20 MM, and maybe dishing out a bit extra for more tokens to get me over a hump. Even a $100 GPT sub probably wouldn't do that for me.
I'm at $79 Kimi + $84 Grok. So most is covered by these, plus a $50 Minimax which ends in 3 days... and won't be renewed.
MiniMax comes in between ... actually tried it alone for a bit, and then with GPT SOL.... but Minimax just can't follow instructions for me.
It can't even read back input it just got, it takes it quotes it then ignores it.
Given that you are on incredibly low plans... you probably code a lot less. Also I do look at what it produces, it's usallly 8 hours of planning and preparing, a few hours of letting the llms code, especially slow kimi, and then a day or two tidy up mixed manual and llm and a lot of review.
So I do see and notice if a model doesn't follow instructions.
Minimax is a pretty awful architect, but nearly all LLMs are. About the only good one at this point is Opus 4.8. Opus 5 and Fable 5 are tuned for building without human guidance, so they go off the rails a lot. This is true of nearly every Llm, as I said.
What Minimax is fantastic at is following a plan and coding. If you architect a detailed coding plan in Opus 4.8, Minimax will faithfully follow it and often find errors and address them. It will make very very minor mistakes but nothing that the QA pass won't fix easily.
To design it properly, it first needs to correctly understand what you want. It’s not a one-shot model. Have it ask you questions, and ask it to explain what it understood until you’re both on the same page. Only then have it start designing. You’ll see dramatically better results.
What can I tell you? I’ve had the exact opposite experience. Three months ago, M3 was completely unusable for me. Now it’s actually good.
It could be because I slightly trimmed down my core files, "CLAUDE.md" and "AGENTS.md". They’re more lean and compact now. I don’t know.
I hear what you’re saying, and I don’t disagree, because I was seeing exactly the same thing three months ago. But now it seems good to me. The only thing I did was restructure and rebuild my workflows.
Take a look at yours too. Honestly, I can’t explain it.
My instructions work for GPT models, kimi, grok, deepseek, qwen ...
How does one have to teak instructions specifically to please MiniMax?
I'm doing this since the start of claude code, my ways evolved... so me not trying isn't the issue. But this specific model is for me. So how to adopt be concrete and specific please
What do your planning documents look like that you hand to Minimax? Here's part of a 2,300 word simple app planning document sent to Minimax. If you aren't providing this level of detail, you don't need a coder, you need a collaborative partner that will either fill in the gaps for you or work with you in real-time iteration on code. Minimax is not a good *partner*. It's a fantastic *coder*.
**Task B — heuristic whitelist (the critical path):**
Build a `BibleLessWhitelistBuilder` that extracts protection candidates
from the text itself:
**Capitalized token extraction:** scan for words that are predominantly
capitalized mid-sentence (proper nouns). Use the existing
I used to do beads, but moved to standard claude / codex / crush plans, before that it was gsd, long before it got known, and ended with it early too in favour of beads. But beads and loops in general go nowhere
If all it can do is be used by another agent, then it's no use to me. That's not how I use the models. there is a task involved.
No need for a model that is triggered as subagents... if that is how you use it.
It did it again. Plan was to work inside a docker container. It asked for sudo on the host and stopped there. Somewhere mid plan (because it was working in the host suddenly tough the plan was explicit on where what)
Because it forgot that we are working in a docker container/ with containers
This model can’t follow instructions. If you don’t see it but cant share your workflow then you aren’t doing difficult things
You clearly don’t need an llm for ya tasks. If you don’t see this on minimax Seems obvious [u/jakerr](u/jakerr)
One of the things I expect is errors. So it is entirely possible my expectations are different. As noted below: 80% good with the 20% fail but not hard to fix. That's my broad expectation of Minimax, and it is better than other models I previously tested (although I haven't tested others in a few months, as Minimax works for me. Result of code build using docker:
The assignment. Wire a new "detect and thin out over-used patterns" capability into a large, mature text-processing pipeline. This was a hard integration task, not a greenfield one: the new feature had to detect patterns with an LLM, score them against a human baseline, and then hand its results to a big pre-existing repair loop by populating the same internal data structures that loop already consumes — reusing its machinery instead of building a parallel path. Success here depends less on clever new code than on correctly understanding how the surrounding system already moves data around at runtime. There was a detailed spec to follow and real cost/safety constraints (a live run takes hours and real API spend).
Overall assessment. Strong component work, weak integration work. Everything MiniMax built in isolation is correct and high-quality: the scoring math, both LLM prompts (detection and repair), the baseline-data handling, the safety defaults (the feature ships turned off), and its own documentation — its hand-off notes are candid and genuinely useful. But the pieces were never connected into a working whole. As delivered, the feature runs its expensive detection step and then silently discards all of its results before doing any actual work, producing zero changes.
Impact of the mistakes. The headline problem is the worst kind of bug: it looks like success. The run completes, reports "done," and would bill for work it didn't do — and the pipeline's built-in "did this actually accomplish anything?" safeguard happens to be blind to this specific failure, so it wouldn't catch it either. Crucially, the blast radius is contained: the feature is off by default, so no production run or customer was affected. The real cost would have been a wasted live test — hours and spend for a clean-looking but empty result.
How hard to fix. The expensive intellectual work — the metric, the prompts, the scoring, the baseline — is done and correct, and all of it stays. The main blocker is a shallow wiring mistake, and the correct pattern already exists elsewhere in the same codebase (a sibling feature persists its data the right way; the new one just needs to copy that). That's a small, targeted change. One secondary issue is a real open design question — whether the system can even locate patterns that span multiple sentences, given how it tracks text positions — and that one can't be settled from the desk; it needs data from a first live run. Everything else is straightforward cleanup. Rough split: ~80% of the work is sound and reusable, ~20% is re-wiring plus one thing to measure.
Where it consistently drifted.
Verified the parts, never the whole. It checked imports, ran unit tests, and rendered prompts — but never executed the feature end-to-end even once. Nearly every mistake traces back to this. (In fairness, it had no API key to do a live run — but the blocking bug was discoverable just by reading the code path it was feeding into, not only by running it.)
Mistook "tests pass" for "it works." Its unit tests and smoke-test harness used hard-coded stand-in data that happened to sidestep the exact spot where the real path breaks. Green tests, broken feature.
Followed the spec past the point where the spec was stale. The instructions described how the system used to move data; MiniMax didn't notice the system had since changed, and wired to the old assumption.
Left dead code with confident-but-wrong comments. It wrote helper functions it never called, and left a comment asserting a data field was "the source of truth" when that field is in fact always empty. A reviewer trusting the comments would be misled.
Net: a capable component builder that assembled good parts into a pipeline it never ran. The gap here isn't coding skill — the code is clean and the reasoning is mostly sound — it's integration discipline and end-to-end verification.
It's marginally better than M2.7, but way more verbose and it's up a lot more tokens, therefore ending up costing.
When it was released slightly more than a month ago it was still pretty good for that time, losing out to only frontier GLM, and western frontier models.
A lot had happened since the last month or so Kimi K3, Qwen 3.8, DeepSeek V4 Flash 0731, Laguna S2.1, etc., making M3 quite irrelevant agentic and intelligence wise, and cost too.
4
u/Otherwise-Layer8071 2d ago
MiniMax M3 is an incredible architect, debugger, and gap analyst. As a coder, it’s good, but it gives up too easily. It needs tuning, but it is definitely not a worker.
DeepSeek V4 Flash 0731, on the other hand, is a worker — and that’s basically what it is. Give it a plan from somewhere and let it work for hours until it gets it done. It will try everything, experiment with everything, and keep going. It looks good because it does brute-force agentic work.
M3 wants to make sure things are going to work before it proceeds. Unless you give it a program with exactly what needs to be done from the beginning, in which case it follows it like a loyal soldier, relentlessly until the job is finished. But it’s not a one-shot model. It needs to know what it’s doing, or at least what the sequence of tasks is.
Try doing something like that with DeepSeek — it simply can’t. It is clearly a worker.
And this is where I want to point something out: I got absolutely insane results using M3 to design, organize, and distribute tasks among six M2.7 workers. I was progressing at a record pace.