MiniMax M3.1 Flash

1 - https://bench.killswitch-lang.org/

I am not sure. The people who actually care enough to look at benchmarks will run evals, and find out quickly. If you post silly benchmark scores too often, you will simply be ignored. You are paying with your reputation.

They genuinely try to improve usability, without spending time on marketing or benchmaxxing.

Except for v4 0731, which was a regression, and probably the reason they released v4.1 ahead of schedule.

Got it to do a code review on a couple of codebases where I knew roughly what the problems were and it underperformed even with a prod in the correct direction. Its code output is average, and it's more expensive than glm-5.3-flash, I don't see myself using it when it comes off free.

Claude sub has a 60x subsidy, so a $20 sub gives you $1200 in API.

Based on this math, Claude sub is a much better deal than Deepseek. And it's a stronger model of course.

(yes, I'm aware the analogy isn't perfect for many reasons)

This MiniMax model is clustered with Haiku 5.5 so (combined with the criticism on here that it was lacklustre while free on opencode go) I don't find it interesting, but every announcement has a chance at pushing that frontier.

I was benchmarking affordable LLMs for japanese->english translation recently and the winner was Kimi K3 - there are a ton of interesting models in the wake of the flagships.

I personally enjoy seeing how different models view problems. Take big plans from big models, then see how other agents reinterpret a stance. See which directions they go. I love the diversity of views they produce.

It's not really relevant what the numbers are. I really really enjoyed talking with MiniMax M3 a lot. And M2.7. it had a good world view, wrote the situation up incredibly fluently. It thought out loud well. These were pleasant uses for me. The realities, the world's they could see felt very grounded. Good stuff, to me.

How dare you, sir/ma'am! the LLM spent a whole 0.9 tokens creating the numbers and graphs!!

As a prospective user, none of of the benchmarks details will matter to you, but the family of models and settings they peg themselves against can give you a hint of where you might want to evaluate them within your own workflows.

As an enthusiast or lay researcher, the more interesting thing is to look at their techniques and any innovations they're exploring in training, inference, etc. Those are usually going to be details tucked below the fold and won't be summarized in a benchmark chart.

As anybody else, why do you even care about this in the first place?

This doesn't just apply to AI. You should know better than to take marketing announcements at face value, nor trust that most "influencers" or "journalists" or "commenter" repeating and discussing those announcements are doing a lot of work as they buzz over what's said.

Don't be a victim to PR spin, but also don't discount potentially interesting work just because it got channeled through a marketing team. Just look for the information you need amidst the noise or move on if there's none there.

For example, here you see they are comparing against Opus 5 and GPT-5.6 Sol, when Opus 5.5, Sonnet 5.5, GPT-6 Astra, and GPT-6.1 Sol would be the latest SOTA models from those labs to compare against.

Also because a lab's own benchmarks tend to run about 6.4% better than third party runs of the same benchmark. Source: https://whatmodel.sh/claims This can happen due to a number of factors, and doesn't necessarily indicate anything nefarious. It could just be that they run multiple times while fixing issues that aren't related to the model, and have a selection bias towards stopping when the score is on the higher side. But since you see that sometimes their own scores are lower than the independent runs, some of the variation is explained just by variance in the model solving problems, since LLMs are nondeterministic there can be variation from run to run and you don't see labs always cheating, just potentially a bit of selection bias about when they stop tweaking their benchmark setup and publish the results.

And yes, there are independent third party runs of benchmarks for just this reason. The most popular with broad coverage of a range of benchmarks is https://artificialanalysis.ai/ but there are a number of others as well.

It's never "cheaper and faster and more intelligent", it's pick 2. Sometimes (as was the case this month), it was "cheaper per token, but reasons five times as much", so your costs go up.

If you are doing coding only then official benchmarks are mostly right, but as many people are starting to do for instance marketing work with LLMs now there’s very little correlation between coding and creativity for instance.

So, I can quickly judge that this is an interesting option only if cheaper or faster than DeepSeek.

Based on Terminal Bench 4.0, I'd also guesstimate behind GPT 5.6 Sol, so of course behind the SOTA as expected.

And often they also don't mention how verbose it is, that the same text splits into more tokens than in competing models, how slow it is, that the pricing is temporary, that the cost per task actually isn't that good despite being both cheap and capable.

The really expensive part of inference - the compute and memory traffic required to perform prompt processing and decode - is paid for roughly once per token in a multi-turn session when prompt caching is working properly.

The difference to the provider's costs between:

- Submitting 100 back-to-back requests, each adding 1k tokens to the context

- Submitting 1 100k token context request

Is negligible. Yet the consumer is going to pay a lot more in the former case for all those repeatedly submitted input tokens that you get charged the cached rate for (but which required no additional compute on the provider side).

DeepSeek V4.1 has an insanely optimized KV-cache that takes around 900MB per million tokens, making it feasible to move to disk between requests. Makes sense they're cheap

Yes the official DS provider has a low cache cost, for now (it's just distributed KV). But people are still concerned about ZDR, sovereign AI, getting more model choice in their subscription price, etc. They want more bang for their buck. So when a new model comes out, the bigger impact is really its performance across providers, not the price at a single provider.

And in about 5 years (or less), API prices will stop being a thing. We all used to pay per-minute for phone calls, texts, data. Nobody does anymore. Virtually every mobile provider is now a flat prepaid or contract fee. Carriers improved their technology (going from analog to digital), and it turned out consumers will spend more money on a packaged subscription than on pay-per-use. That's why everything in the world is a subscription now.

Your modern $70 big wireless plan isn't giving you $70 of value. Similarly, your $20 AI subscription that's a great deal right now, will be less valuable in 5 years, when hardware & models are more efficient. You'll still be able to buy a local AI rig, but it will still be very expensive, due to AI models continuing to be large, and NVidia keeping new hardware expensive, as these things together reinforce their dominance and profits, and our reliance on them.

This also follows another pre-existing trend. Why do people keep having to buy a new iPhone or laptop every other year? Because each year companies make software & data less efficient/more bloated, so people need a more powerful machine to run it, which enables them to make even more bloated software. Almost everything people do with a computer today is what they did 15-20 years ago; it's just a higher bit-rate. Despite this, people keep re-buying the same machine, so companies can keep extracting more wealth.

AI works the same way. Rather than cost to consumers going down over time, it will likely go up. Not because it can't get cheaper/more efficient - but because it increases companies' profit. This isn't just a theory, btw; the US economy's growth is now almost entirely reliant on this happening.

Which is roughly similar to deepseek 4.1 flash.

I find it interesting that we’re calling these “flash” models now, even though they aren’t “small or light” and in many cases aren’t particularly that fast.

I suppose it’s more so that the standard for “regular models” is becoming 1T+

https://agent.minimax.io/tools/m3-1-flash-preview

For me, the DeepSeek 4.1 models are currently the ultimate cost-effective model the rest have to match with, especially if you are not on a subscription plan with heavy discount.

GPT Sol 6.1 is however, for me the most efficient model, both in terms of time to finish and token usage.

They mention that space bunny was an earlier checkpoints, so it may have improved (or may it could be benchmaxxed)

I find it interesting that so many models seem to be at almost exactly 74% specifically which makes me suspicious of the benchmark. I want to say 5.6-sol, Astra, deepseek flash 4.1, opus 5 (and 5.5?), Gemini 3.8-flash and a few others all ended up there?

I use Opus 5.5 at work and it does this much less (I know, different classes). Maybe it is to be expected of the flash models.

20 t/s is about the speed you can read at, so slower than that and it always feels like it's buffering. 35 t/s is where it starts feeling decent and 50 t/s feels nice.

https://jonclegg.github.io/pacman-bakeoff/#minimax-m3.1-flas...

https://aibenchy.com/compare/minimax-minimax-m3-1-flash-high...

And that model was not top 5 even when it was released, 3 months ago.

I wouldn't expect that.

But it's probably n=1. Still.

It actually hasn't, it seems: https://news.ycombinator.com/item?id=50151775

Obviously your marketing team is going to use the Pareto frontier where your model has improved the most and compares favorably against competitors.

The solution is for @dang to unshadowban it because Minimax is now a newsworthy company.