If by those spaces you mean reddit, then yeah I've also noticed this trend. It has became more egregious with the duality of L4 vs Qwen3 reception. L4 was blamed, mocked and everyone was posting shit about it (well, some of it was relevant since the launch was rushed and many providers had bad implementations for ~2-3 days) in stark contrast to qwen3 which also had inferencing problems (related to 3rd party tools using wrong settings) and by all accounts has overinflated their benchmark scores.
Don't take the "activity" of those places as gospel, try the models on your own stacks, with your own benchmarks for best results.
Nah not just reddit but also like 20 AI related discord servers. But a couple of things, first I do agree that no one should take anyone's opinion as gospel. I live by that and test all of my models. Second, I wasn't expressing my opinion of what model is good or not good just reflecting the broader trends I see within the communities (for better or worse). On llama 4 botched launch... On this particular matter I see this happen almost every launch. A model comes out, shit hyperparameters are used or the model organization does a poor job of communicating towards the community on best practices for how to run the model, community shits on model, rinse repeat EVERY god damned release cycle. So yeah I am quite aware of this particular phenomenon.
L4 launched without a thinking model, making it an inferior choice for coding, one of the main LLM use cases. Even in benchmarks it wasn't even competitive at coding with 3-month-old Deepseek R1.
Agreed, coding is not a strong point of L4. However the "hive mind" in some places thinks L4 is "a failure" and "useless". In reality it is an "ok" model, and most 3rd party benchmarks done after the inference lib updates were in line with whatever meta announced.
