IntelliJ: https://github.com/JetBrains/intellij-community

PyCharm: https://github.com/JetBrains/intellij-community/tree/master/...

Android Studio: https://android.googlesource.com/platform/tools/adt/idea/+/r...

Yes, they might offer extended proprietary editions/plugins in addition, but the IDEs themselves are open source.

In the unlikely case that this feature exists thanks to Microsoft, I would like to say that is great, because it is much more user friendly than only having tiny labels. But since I’ve never seen this feature work before, it seems to me that it must be broken, if it exists at all.

Labels would be nice, I guess, but their absence is hardly a dealbreaker.

I see that you tried to justify this lower in the thread, but no… it completely invalidates your benchmark. You are not testing the model. You are conflating one specific model host and model performance, and then claiming you are benchmarking the model. All major models are hosted by multiple different services.

In the real world, clients will just retry if there is a server error, and that will not impact response quality at all, and the workflow the model is being used in will not fail. If a workflow is so poorly coded that it doesn’t even have retry logic, then that workflow is doomed no matter which host you use. But again, reliability of the host is separate from the model.

You can make your benchmark valid by having separate leaderboards for model quality and host reliability. I’m not saying to throw the whole thing away. But the current claim is not valid.

And you’re also making an unsourced claim that everyone else has already determined this model sucks? Nah. The first result from Artificial Analysis shows good things: https://x.com/ArtificialAnlys/status/2047547434809880611

But I am still waiting to see the results from the full suite of AA benchmarks.

"Kimi-K2.6 adopts the same native int4 quantization method as Kimi-K2-Thinking."

> Import from anywhere. Start from a text prompt, upload images and documents (DOCX, PPTX, XLSX), or point Claude at your codebase. You can also use the web capture tool to grab elements directly from your website so prototypes look like the real product.

My own experiences with Gemma 4 have been quite mediocre: https://www.reddit.com/r/LocalLLaMA/comments/1sn3izh/comment...

I would almost be tempted to call it benchmaxed if that term weren’t such a joke at this point. It is a deeply unserious term these days.

Gemma 4 is worse than its benchmarks show in terms of agentic workflows. The Qwen3.x models are much better; not benchmaxed. I have tested this extensively for my own workflows. Google really needs to release Gemma 4.1 ASAP. I really hope they’re not planning to just wait another calendar year like they did for Gemma 3 -> 4 with no intermediate updates.

And the lead author on the paper replied to that tweet to say that the scores would need to be greater than 80 to show actual contamination: https://x.com/MiZawalski/status/2043990236317851944?s=20

Even Qwen3.5 35B A3B benchmarks roughly on par with Haiku 4.5, so Qwen3.6 should be a noticeable step up.

https://artificialanalysis.ai/models?models=gpt-oss-120b%2Cg...

No, these benchmarks are not perfect, but short of trying it yourself, this is the best we've got.

Compared to the frontier coding models like Opus 4.7 and GPT 5.4, Qwen3.6 35B A3B is not going to feel smart at all, but for something that can run quickly at home... it is impressive how far this stuff has come.

The article also seems to brush over the discovery phase, which seems very important. If it were as easy as they say, then the models should have been let loose and we would see if they actually found these bugs, and how many false positives they marked as critical. Instead, they pointed the models at the flawed code directly.

> Audio supports a maximum length of 30 seconds.

[0]: https://huggingface.co/google/gemma-4-26B-A4B-it#getting-sta...

For the sake of argument, even if we take that old rule of thumb at face value, you can see how the MoE still wins:

- (DGX Spark) 273GB/s of memory bandwidth with 3B active parameters at Q4 = 273 / 1.5 = 182 tokens per second as the theoretical maximum.

- (RTX 3090) 936GB/s with 24B parameters at Q4 = 936 / 12 = 78 tokens per second. Or 39 tokens per second if you wanted to run at Q8 to maximize the memory usage on the 24GB card.

The "slow" DGX Spark is now more than twice as fast as the RTX 3090, thanks to an appropriate MoE architecture. Even with two RTX 3090s, you would still be slower. All else being equal, I would take 182 tokens per second over 78 any day of the week. Yes, an RTX 5090 would close that gap significantly, but you mentioned RTX 3090s, and I also have an RTX 3090-based AI desktop.

(The above calculation is dramatically oversimplified, but the end result holds, even if the absolute numbers would probably be less for both scenarios. Token generation is fundamentally bandwidth limited with current autoregressive models. Diffusion LLMs could change that.)

The mid-size frontier models are rumored to be extremely sparse like that, but 10x larger on both total and active. No one has ever released an open model that sparse for us to try out.

As I said, I wanted to see what it is possible for Google to achieve.

> Qwen 3.5 uses 122B-A10B and still is neck and neck with the 27B dense model.

From what I've seen, having used both, I would anecdotally report that the 122B model is better in ways that aren't reflected in benchmarks, with more inherent knowledge and more adaptability. But, I agree those two models are quite close, and that's why I want to see greater sparsity and greater total parameters: to push the limits and see what happens, for science.

In a few days, I imagine that Gemma 4 support should be in better shape.