If you're going to use OpenRouter to test reasoning levels, always make sure you are locking to the official provider instead of third party providers.
Pretrained dictionaries have never been intended to help with book sized or bigger compression. zstd automatically learns the most efficient dictionary it can within a few kilobytes. Pretrained dictionaries are only useful when you're independently compressing very small records.
Please run GLM-5.3 and GLM-5.3-Flash. I would love to see how they do. On the smaller end of things, Qwen3.8-27B and Ling-3.0-Flash would also be interesting.
In the benchmark, have you considered instructing the models to build their own SPICE simulations to test their work? Simply asking them to write and run simulations could improve performance, even without telling them what to simulate.
FunctionGemma never worked well for me (without fine tuning). Liquid has released 230M and 350M models that work far, far better in my testing: https://huggingface.co/LiquidAI/LFM2.5-230M
I really look forward to a hypothetical LFM3-230M, because LFM2.5-230M is so close to being usable, while FunctionGemma is miles away from being usable.
I fully expect Meta will release other, smaller Muse models in the near future too.
The 5090 is also supposed to be a $2000 GPU, not a $5000 one. The entire market is utterly distorted right now, which will impact cloud inference more and more over time too. They are not immune to the absurdly high RAM prices, so their prices will have to go up over time too until the RAM supply chain goes back to normal.
The best workaround is a third party app that lets you run Parakeet V3 or Whisper on your phone. There are quite a few.
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