This model was released as a "stealth model" in OpenCode Go for a while as "Space Bunny" (they mention this in the blog). So people have been using without knowing what the model was exactly, but it failed to impress anyone as far as I can tell. This is despite the model being served for free (with some asterisks). GLM 5.3 Flash and DeepSeek V4.1 Flash, even before last week's update, seemed to be much preferred. Stealth releases happen often with OpenCode subscriptions, and GLM 5.3 Flash (stealth name "Ox Alpha") impressed a lot for example.

The perverse incentives around benchmarks are now so bad that benchmark scores are effectively meaningless. Models can score highly and still be crap to use. It doesn't matter whether it's Minimax or Google, or anyone really.

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.

https://artificialanalysis.ai/models/comparisons/mimo-v2-6-p...

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.

2. Add "Flash" to the end

3. ?

4. Profit

H3, on the other hand, is phenomenal

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

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.

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.

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.

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.