The cost vs performance is insane for all these new models. I’ve been using Deepseek Flash 1017 and it does whole projects and asks for $0.10 on US provider with ZDR on Openrouter. I convinced a few friends to drop their Claude $20 subscription because DS Flash 1017 brings so much better value. They can still use Opus 5.5 for planning though each call will cost north of $2. Excited to try this model out too once there are US providers.
terhechte 37 minutes ago [-]
The weights have yet to be released. The previous MiniMax M3 had (I think) ~428B parameters. Since this one is called Flash and they advertise it as inference being 3 times faster, there's the possibility that this will be a ~150B size model which would (quantized) run on DGX Spark or 128GB Macs or the new AMD AI systems.
penagwin 20 minutes ago [-]
The FAQ on their website says Minimax 3.1 flash is 428B total, 23B active
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+
Minimax has been a strong player in our tests, if they managed to solve the inference speed issue(be scared, be very scared). A decent speed is at least 13 tps in consumer hardware. Let see if it passes Hugston bench this time, we really hope so.
latentsea 8 minutes ago [-]
13 TPS is painfully slow?
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.
varispeed 4 minutes ago [-]
The comparisons with models like GPT-5.6 or Opus are pointless because they vary substantially. They are often nerfed shortly after release and then performance is not consistent. One day model will be doing relatively fine, another it will be dumb as markov generator. In that regard self hosted models are better, because you know what is running your requests.
loufe 34 minutes ago [-]
Odd choice to have a mix of opus 5 and 5.5 in different graphics.
tcdent 29 minutes ago [-]
The recent DeepSeek release used Opus 5 as a reference point as well. I don't understand it. It's not like they're referencing superseded releases of other open models.
minraws 15 minutes ago [-]
I think it clearly points to where they expect themselves to be, Opus 5.5 and Astra or Sol 6.1 are models that shouldn't exist at those prices without the price competition.
And no new releases from Chinese labs likely means their models that they could compare against those models are still baking largely because they lack compute so severely they need to train models for far longer, or figure out training on Chinese hardware.
Either way it's clear open models a lagging by ~1-2 months compare to frontier, depending on what frontier means to a person. Honestly most new models are just not that much better than at edge cases, they still fail a lot for the kind of stuff I do, but those edge cases matter a lot for un-interrupted long horizon work.
I personally am starting to see a wall here in llm development but that might just be me, given that people are being forced to scale models up considerably for frontier performance.
It's scary thinking that modern models will likely be at best 2-3% better in benchmarks or only useful in 1-2 unique edge cases compared to base models for 100x cost increase in compute required, it's getting absurd.
I can't imagine how Fable models can retain any value once you have to go 100x larger, while 27-32B models start to catch up to the current frontier for coding or other narrow/focused tasks in a year or so.
tadkar 23 minutes ago [-]
Most probably the last version they had access to for benchmarking, or otherwise.
walrus01 11 minutes ago [-]
The deepswe and terminalbench4.0 scores don't seem to jive with what many opencode users' experience has been of this model while it was being tested as "space bunny". I saw a lot of anecdotes of people trying it and going back to qwen 3.8-flash-next or deepseek v4 flash because "space bunny" made some rather egregious errors.
LaurensBER 35 minutes ago [-]
Another day where the Pareto frontier is moved up and to the left!
I haven't tried this model yet but Deepseek and the GLM 5.3 series do feel like "enough" for a lot of tasks, it's pretty amazing to see that we're still getting better and cheaper models all the time.
I only wished they would focus more on wall clock time. For a hobby project I do not mind that a cheaper model takes twice as long but at work it's hard to justify using a "cheaper" model if the time per task is so much higher than the frontier models.
The amount of time that it took to complete a benchmark is almost never shown in these kind of releases.
theanonymousone 34 minutes ago [-]
> Another day where the Pareto frontier is moved up and to the left!
Would place DS 4.1 1017 roughly equivalent to GLM 5.3 which means it's targeted to price rather than direct capability. Probably why they listed DS adjacent in the comparison table when according to their results more expensive models are beaten by it.
antisthenes 28 minutes ago [-]
You do realize that there exist multiple Pareto frontiers, at least for every arbitrary combination of parameters for a given benchmark?
Obviously your marketing team is going to use the Pareto frontier where your model has improved the most and compares favorably against competitors.
swiftcoder 38 minutes ago [-]
Curious if anyone has put it up against DeepSeek Flash yet, see if the price/performance benchmarks hold up?
bel8 37 minutes ago [-]
Some reddit comments say it's not as good as DS 4.1 Flash. But I didn't test myself and I don't trust benchmarks.
johnnyApplePRNG 22 minutes ago [-]
Not as strong as DeepSeek V4.1 Flash 1017 on deepswe so it's already dead to me.
Unless they come in at half the price (which I think will be impossible) on inference ... they have wasted their training compute imho.
Apparently the domain is shadowbanned (https://news.ycombinator.com/from?site=minimax.io) which is why people kept submitting it (and normally they cannot be vouched), and presumably a moderator gave this one their blessing.
The solution is for @dang to unshadowban it because Minimax is now a newsworthy company.
yorwba 6 minutes ago [-]
They were a newsworthy company when their domain got shadowbanned, too... The spam detector is blind to such things.
thenthenthen 30 minutes ago [-]
Amazing haha. Why is it so quiet around hy3/hy4? Or Tripo? Or agitbot or z.ai? All incredibly capable
theanonymousone 30 minutes ago [-]
@dang maybe?
bigwheels 30 minutes ago [-]
Friendly reminder: Submissions without discussion aren't considered dupes on HN.
theanonymousone 29 minutes ago [-]
Oh, really? I didn't know this. Sorry.
Then maybe they can be merged once one gains a comment?
minimaxir 8 minutes ago [-]
There is literally no reason to merge them if they have zero discussion. Submissions to HN are not a finite resource.
himata4113 23 minutes ago [-]
Sometimes it will automatically redirect to an already submitted one, but the system is not perfect and you might end up with duplicates like this.
Rendered at 18:38:07 GMT+0000 (Coordinated Universal Time) with Vercel.
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
- DeepSeek V4 Flash: 284B / 13B active MoE
- DeepSeek V4 Pro: 1.6T / 49B active MoE
- DeepSeek V4.1 Flash: 552B / 8B input, 16B output MoE
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.
And no new releases from Chinese labs likely means their models that they could compare against those models are still baking largely because they lack compute so severely they need to train models for far longer, or figure out training on Chinese hardware.
Either way it's clear open models a lagging by ~1-2 months compare to frontier, depending on what frontier means to a person. Honestly most new models are just not that much better than at edge cases, they still fail a lot for the kind of stuff I do, but those edge cases matter a lot for un-interrupted long horizon work.
I personally am starting to see a wall here in llm development but that might just be me, given that people are being forced to scale models up considerably for frontier performance.
It's scary thinking that modern models will likely be at best 2-3% better in benchmarks or only useful in 1-2 unique edge cases compared to base models for 100x cost increase in compute required, it's getting absurd.
I can't imagine how Fable models can retain any value once you have to go 100x larger, while 27-32B models start to catch up to the current frontier for coding or other narrow/focused tasks in a year or so.
I haven't tried this model yet but Deepseek and the GLM 5.3 series do feel like "enough" for a lot of tasks, it's pretty amazing to see that we're still getting better and cheaper models all the time.
I only wished they would focus more on wall clock time. For a hobby project I do not mind that a cheaper model takes twice as long but at work it's hard to justify using a "cheaper" model if the time per task is so much higher than the frontier models.
The amount of time that it took to complete a benchmark is almost never shown in these kind of releases.
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.
Unless they come in at half the price (which I think will be impossible) on inference ... they have wasted their training compute imho.
https://news.ycombinator.com/item?id=50151561 https://news.ycombinator.com/item?id=50151576 https://news.ycombinator.com/item?id=50153196 https://news.ycombinator.com/item?id=50153612 https://news.ycombinator.com/item?id=50153725
The solution is for @dang to unshadowban it because Minimax is now a newsworthy company.
Then maybe they can be merged once one gains a comment?