wkcheng

It would be super useful to have more specific analyses of what areas the model is good at. I'd like to know when I should reach for this model instead of Anthropic's or OpenAI's.

For example, Opus 5.5 is better at UI design than Sol. Anyone who has used both models knows that, but it's not something you can look up anywhere.

I'm sure that there's a need for this kind of benchmarking, not just for coding.

The benchmarks are great for sure.

Now if only they could fix their coding harness so that it's usable for day-to-day use. I paid them for a month to try out Muse Code, but their sandbox never worked across Mac or Linux (both very stock installs, nothing special).

Maybe I'm just not looking in the right place, but I cannot find what the API shape looks like. I've even deployed this model via Foundry and it doesn't say what to POST or what to expect back.

I don't see any API documentation for this yet. How can someone actually try it? Did they rush this out for hype?

When you are running the model locally and trying to do other tasks at the same time, Whisper Large feels too clunky on normal hardware.

I like Parakeet because it's good enough, relatively light, and fairly fast. I'm using this for things like meeting transcription and dictation. Since I'm sending most of the text to an LLM to clean up afterwards, it works well enough.

I'm hoping for something the size of Parakeet (or smaller) but better quality. It feels like with all of the advances in making smaller models better in the LLM space, someone should be able to come up with a lightweight and better quality speech to text model.

How does this compare with Parakeet? I've been using that locally in my projects on an M-series macbook and it's been working great. It's fast and accurate enough for my use cases (meeting transcription, audio transcription for demo videos, etc.)

This definitely seems lighter and faster. How does accuracy compare?

I agree, and I haven't seen other people mention this! The benchmarks for GPT 6 Sol are great, but realistically it does not seem better than 5.6 Sol. 6-Sol is noticeably worse for code reviews (worse than Deepseek 4.1 flash), has implementation issues (requires more rounds of code reviews and fixes to get to a serviceable state). Opus 5.5 is much much better.

I've implemented multiple features side by side with Opus 5.5 and 6 Sol, and the Opus 5.5 results always have fewer high severity bugs and require fewer rounds of fixes to get it over the finish line.

If 6.1 Sol has actually matched Opus 5.5, I'd be very happy. However, benchmarks and real usage don't seem to agree in my own tests. So we'll have to see.

That makes sense. I'm interested in seeing where Haiku 5.5 comes in then when it gets released. It feels like the low intelligence / fast niche will be covered there.

The cost / performance chart shows that in almost all configurations, it looks worse than Opus. Why would you use Sonnet 5.5 on xhigh if you would get better results (higher score, cheaper cost) on Opus 5.5 high?

Is there a good use case? This isn't like Luna where it's much cheaper/effective just to use Luna in certain situations.

Are people using MiMo models as their daily drivers in a company setting? If so, how? I know they're available over OpenCode and directly from Xiaomi, but those are not great options. OpenCode Go straight up doesn't give any guarantees about training on your data, and Xiaomi says that they won't but it's unclear.

With some models you can find hosting companies based in the EU or US, but then you don't know how they're quantizing the models, so you're not sure about the actual output quality.

How are people actually using this? Or are people just experimenting with side projects?

How do people actually use this? Do they use it through some sort of subscription plan, or via OpenRouter?

If the benchmarks are real and reflect actual use, then this is an insane model. This 300B model outperforms the previous DS4 Pro preview model (1.8T params), and it looks like it outperforms GPT 5.6 Luna too. And it's still cheaper than Luna, even with the price decrease.

Crazy.

Just use it through something like Azure. They host the entire model and serve it from the US. I'm sure that there are other providers like this.

We use it that way and it works great.

Use it through Azure! Azure hosts DeepseekV4-Pro and DeepseekV4-Flash themselves. We're using it and it works great.

You don't get the discount that Deepseek is providing, but it's still a cheap model (v4-pro is cheaper than sonnet)

This is nice! I tried it for a bit and it was indeed quite fast. Are you looking for contributors, or are you building this as a personal tool? I ran into some issues when attempting to use different models, though: gpt-5.5 on Azure doesn't work, even with the OpenAI compatible endpoint, because "max_tokens" has been replaced with "max_completion_tokens". And it doesn't appear possible to pass through custom headers, so I wasn't able to specify reasoning_effort for deepseek models.