I go off of marketing obviously, but how is every new announcement of any vendor always on top of all others and then cheaper, while next day the next best and even cheaper model pops up? Is there anyone who independently verifies this? Its kind of pointless to comment on this announcement, much like how its pointless to comment on all the other announcements...
It's always cheaper (*than the next more expensive model) and better (*than the next worse model).
And often they also don't mention how verbose it is (Mistral), that the same text splits into more tokens than in competing models (Claude), how slow it is, that the pricing is temporary (Gemini), that the cost per task actually isn't that good despite being both cheap and capable (Deepseek).
You have to read between the lines (or wait for someone on HN to do it for you).
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.
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.
Opus 5.5 is according to AA cost per task, 23x more expensive than Deepseek with max effort (which is dumb, you shouldn't use) and 13x more expensive on xhigh.
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.
I pay API rates via OpenRouter / Fireworks, and I find it challenging to hit the $20 mark.
If I was to do anywhere near that kind of work on Anthropic, I'd find myself running out of tokens, waiting days to continue working, and leaning on their el-crap models to try to avoid running out again. This happens all the time.
This way I get to leverage DS for nearly all my work, benefit from its far superior latencies, and I still get to lean on OpenAPI / Anthropic or whatever SOTA models I want when I need them.
The only time that really falls down is if I'm doing huge amounts of real novel work, which hasn't been the case for a while.
Unfortunately, Fireworks is not keeping up with new models the last few months. When we selected them as a vendor, they were crushing it, new models were available in 1-2 days. Now they want us to "request" them in Discord, instead of making them all available.
I use OpenCode at home, this is one of the reasons.
Their Ember-1 model (Kimi K3 tuned to think less) hallucinates to high hell, I have no trust of it, nowhere to provide feedback, despite their launch post saying they would like feedback. That they loudly and proudly claim their internal devs notice no difference is a huge red flag for me. It says there is too much eyes free coding, I noticed the hallucinations and false claims of task completion immediately.
Just because anthropic charges absurd consumer API prices, doesn't mean their subscription is a 'subsidy' lmao.
I'm a heavy Claude/anthropic user, I max out my 20x max plan in 1-2 days at this point. The OpenCode Go $40 plan with DeepSeek flash is like 2x the value of the Claude max 20x plan for how much I actually get out of it, for 1/5 the price.
DeepSeek literally like 10x better value than Claude/Anthropic.
It's just pointing out how heavily subsidized subscriptions are. I often read about how much value people get from DeepSeek, but when I try to use it, I end up spending the amount I'd pay for a subscription much faster.
DeepSeek is really cheap when you compare API pricing, but it's more expensive than a subsidized Anthropic or OpenAI subscription.
> For agentic tasks, the model’s overall usage cost is 70% lower than its predecessor.
For this release, the cost is $0.15 input MToK/$0.60 output MTok/$0.015 cache read MTok, In this case the predecessor is MiniMax M3 (not Flash), which was $0.30 input MToK/$1.20 output MTok/$0.06 cache read MTok. So a 50% cut to input costs and a 50% proportional cut to cache read costs (20% of input -> 10% of input) makes the ~70% cost reduction plausible.
A 70% cost reduction is very funny in light of reports saying LLMs are becoming too expensive. That is likely due to the need to compete with DeepSeek V4.1 ($0.15 input MToK/$0.60 output MTok/$0.003 cache read MTok), which this new model still does not beat because DeepSeek's cache costs are still confusingly low, but it's pretty close.
If anything I am confused by how high cache costs still are across many models
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
The amazing part of DeepSeek's caching is that the TTL is (although not guaranteed) like 8-24 hours. Makes a big difference to how much time I can spend reviewing code, when most everyone else offers maybe 1 hour (for subscribers - often minutes for API users).
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.
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.
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.
I know, it is never enough, more, more, give me more. But the fact is that no one has broken though yet to offer a frontier like open weight over 13 tps in consumer hardware. It would be usable at 13tps for me, especially considering the benefits and advantages of privacy, infinite tokens, availability and no refusals/restrictions.
To this day Minimax and GLM are very strong coders but have the same issue, inference speed. We need a real break through, one that thinks out of the box (Tesla turbines style perhaps).
If all you're going to do is chat with an AI, you don't need a 400B parameter MoE model.
13 TPS is unusable for agentic coding, because if decode is that slow, then prefill is absolutely abysmal, and agentic work live and die by prefill speeds.
I’ve been agentic coding at home with like 15 TPS on average. I don’t mind it at all. I can give my agent a big task or a list of little ones and just… do something else. Why do I need to watch it work?
It doesn’t. Decode is by far the most dominant and expensive part of agentic tasks and once prefill hits a certain achievable speed decode dominates the time in ant agentic flows with reasoning. There is a reason input is so much cheaper :)
I went to check and Opus 5.5 wasn't benchmarked on DeepSWE1.1, I can't keep track of which are in vogue at the moment but maybe it's thought to be saturated at this point, as they only have older GPT models as well
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.
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.
These models exist because better models exist, these models can't exist without better models existing, you will never beat the frontier with smaller model sizes. Because all these models are trained again larger models, generally a teacher model of sorts.
Lots of synthetic data, distilled data etc.
The problem is if we can pack a Fable in Qwen 4 27B who will pay subscription prices?
Synthetic data, distilled data, etc., does not necessarily mean data coming from outside of an organization. When we see performance increases with models like Haiku, it is because they are able to leverage the knowledge of larger architectures.
I fully expect that we will see increased performance from smaller architectures (to a point) simply because they are refined using larger ones.
> The problem is if we can pack a Fable in Qwen 4 27B who will pay subscription prices?
This is available to everyone developing these systems. Prices per relative unit of intelligence will continue to decrease. And market economics will continue to drive the cost down.
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.
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.
MiniMax models have been pretty disappointing. M2.5 was good, M2.7 was meh and came with a terrible license, 3 meh. Maybe this will be better, but I have my doubts. When I say good vs meh vs bad, this is all relative of course. If M3.1 came out a few months ago it would be groundbreaking. Things just move too quick.
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.
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.
> [Space Bunny] was an early version of MiniMax M3.1 Flash.
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?
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.
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 (Mistral), that the same text splits into more tokens than in competing models (Claude), how slow it is, that the pricing is temporary (Gemini), that the cost per task actually isn't that good despite being both cheap and capable (Deepseek).
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.
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.
If I was to do anywhere near that kind of work on Anthropic, I'd find myself running out of tokens, waiting days to continue working, and leaning on their el-crap models to try to avoid running out again. This happens all the time.
This way I get to leverage DS for nearly all my work, benefit from its far superior latencies, and I still get to lean on OpenAPI / Anthropic or whatever SOTA models I want when I need them.
The only time that really falls down is if I'm doing huge amounts of real novel work, which hasn't been the case for a while.
I use OpenCode at home, this is one of the reasons.
Their Ember-1 model (Kimi K3 tuned to think less) hallucinates to high hell, I have no trust of it, nowhere to provide feedback, despite their launch post saying they would like feedback. That they loudly and proudly claim their internal devs notice no difference is a huge red flag for me. It says there is too much eyes free coding, I noticed the hallucinations and false claims of task completion immediately.
I'm a heavy Claude/anthropic user, I max out my 20x max plan in 1-2 days at this point. The OpenCode Go $40 plan with DeepSeek flash is like 2x the value of the Claude max 20x plan for how much I actually get out of it, for 1/5 the price.
DeepSeek literally like 10x better value than Claude/Anthropic.
DeepSeek is really cheap when you compare API pricing, but it's more expensive than a subsidized Anthropic or OpenAI subscription.
For this release, the cost is $0.15 input MToK/$0.60 output MTok/$0.015 cache read MTok, In this case the predecessor is MiniMax M3 (not Flash), which was $0.30 input MToK/$1.20 output MTok/$0.06 cache read MTok. So a 50% cut to input costs and a 50% proportional cut to cache read costs (20% of input -> 10% of input) makes the ~70% cost reduction plausible.
A 70% cost reduction is very funny in light of reports saying LLMs are becoming too expensive. That is likely due to the need to compete with DeepSeek V4.1 ($0.15 input MToK/$0.60 output MTok/$0.003 cache read MTok), which this new model still does not beat because DeepSeek's cache costs are still confusingly low, but it's pretty close.
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
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
https://artificialanalysis.ai/models/comparisons/mimo-v2-6-p...
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.
13 TPS is unusable for agentic coding, because if decode is that slow, then prefill is absolutely abysmal, and agentic work live and die by prefill speeds.
Why wouldn't I want the smartest possible model I can get? I only "chat" about smart person stuff.
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.
Lots of synthetic data, distilled data etc.
The problem is if we can pack a Fable in Qwen 4 27B who will pay subscription prices?
I fully expect that we will see increased performance from smaller architectures (to a point) simply because they are refined using larger ones.
> The problem is if we can pack a Fable in Qwen 4 27B who will pay subscription prices?
This is available to everyone developing these systems. Prices per relative unit of intelligence will continue to decrease. And market economics will continue to drive the cost down.
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.
H3, on the other hand, is phenomenal
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?
Unless they come in at half the price (which I think will be impossible) on inference ... they have wasted their training compute imho.
Ask HN: Any guesses on which model/provider is Space Bunny Alpha on OpenRouter? - https://news.ycombinator.com/item?id=49898971 - Sept 2026 (1 comment)
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?