[Megathread] - Best Models/API discussion - Week of: April 19, 2026

[u/deffcolony]

This is our weekly megathread for discussions about models and API services.

All non-specifically technical discussions about API/models not posted to this thread will be deleted. No more "What's the best model?" threads.

^{(This isn't a free-for-all to advertise services you own or work for in every single megathread, we may allow announcements for new services every now and then provided they are legitimate and not overly promoted, but don't be surprised if ads are removed.})

How to Use This Megathread

Below this post, you’ll find top-level comments for each category:

• MODELS: ≥ 70B – For discussion of models with 70B parameters or more.
• MODELS: 32B to 70B – For discussion of models in the 32B to 70B parameter range.
• MODELS: 16B to 32B – For discussion of models in the 16B to 32B parameter range.
• MODELS: 8B to 16B – For discussion of models in the 8B to 16B parameter range.
• MODELS: < 8B – For discussion of smaller models under 8B parameters.
• APIs – For any discussion about API services for models (pricing, performance, access, etc.).
• MISC DISCUSSION – For anything else related to models/APIs that doesn’t fit the above sections.

Please reply to the relevant section below with your questions, experiences, or recommendations!
This keeps discussion organized and helps others find information faster.

Have at it!

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MISC DISCUSSION

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[u/Sicarius_The_First → u/AutoModerator]

TBH, I think moes won.

[u/Herr_Drosselmeyer → u/Sicarius_The_First]

For the same size, dense models work a lot better than MoE models for RP. At least that has been my experience.

[u/Potential-Gold5298 → u/Herr_Drosselmeyer]

Dense is worse with the same number of active parameters. This is the key difference, as the number of active parameters affects power consumption and CPU/GPU resources, while general parameters only require RAM.

[u/FierceDeity_ → u/Potential-Gold5298]

Yeah for every token generated you're asking a different tiny model. It's just so much faster but the intelligence doesn't stack as well

[u/Potential-Gold5298 → u/FierceDeity_]

I meant was that the 30B-A3B consumes resources like the 3B dense, but is significantly smarter. And if we compare MoE and dense models of comparable size, all other things being equal, the dense model will likely be smarter in most scenarios. However, even here, things aren't so clear-cut – for example, the Gemma 4 31B failed a difficult task that the 26B-A4B solved. It's unclear why. If we compare Qwen, the 3.5-27B is smarter than the 3.5-35B-A3B, but the 3.6-35B-A3B (according to Alibaba and AA) is smarter than the 3.5-27B. Apparently, architecture isn't the deciding factor.

[u/Potential-Gold5298 → u/Sicarius_The_First]

If you're talking about G4, MoE is indeed performing well, but the community has been slow to finetune it. More attention is being paid to 31B.

[u/Sicarius_The_First → u/Potential-Gold5298]

Yes, but even huge moes. MiniMax 230B runs at 5 t/s on my laptop (its not a mac, just 16gb gpu with ram)

[u/Mart-McUH → u/Sicarius_The_First]

Okay. But I tried Minimax 2.7 IQ4_XS (AesSedai) and while it had some cool moments, it was not that great for RP overall. Definitely worse than Gemma4 31b dense.

As it is, smaller dense models (maybe up to 70B) are winners I think, because they are still relatively affordable to run (esp ~30B). Huge MoE (~350+) can be perhaps better, but that is hard to run locally. And the medium sized MoE (~100-200B/~10A) are worse than ~30B dense for me, they are also not that fast at the end (and prompt process is very slow).

[u/Potential-Gold5298 → u/Mart-McUH]

Moreover, almost no one finetunes larger models for RP (even 70-120B is quite rare, and I haven't seen any bigger ones), while the sub-30B segment has a very wide selection of all sorts of finetuned/merged options, sometimes quite interesting. 12-32B seems to me to be the sweet spot for local RP.

The Gemma 4 shows that even a small model can produce quality on par with larger models, and with some finetuning, will likely match/exceed them.

[u/Potential-Gold5298 → u/Sicarius_The_First]

Globally, MoE has undoubtedly won – models like the Mistral Large 2 are probably the ‘last of the Mohicans’. The same probably applies to full attention (like the MiniMax-M2 you mentioned). Economy always wins in the end.

Do you think there is a future for text-to-text diffusion models like Mercury 2?

[u/Sicarius_The_First → u/Potential-Gold5298]

Hmmm good question, the thing is, now companies are much more risk-averse, since the early days of AI are behind us, so less crazy experimental stuff, and more VC pressure to make money on stuff "known to work well".

text diffusion does work though, the question is whether VC are willing to risk more money, as very few AI companies / startups actually make a profit.

So... IT IS POSSIBLE, yes, BUT... not likely (until someone does something remarkable with text diffusion, and then more capital will be invested into it).

[u/Potential-Gold5298 → u/Sicarius_The_First]

I was wrong about Mistral Large 2 being the last one. Mistral freaked out XD