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I know the big labs like to pretend that their models are trillion parameter. But how likely is that really to be the case when Qwen 3.6 35B A3B gets so close t

by nullbio 4mo ago

I know the big labs like to pretend that their models are trillion parameter. But how likely is that really to be the case when Qwen 3.6 35B A3B gets so close to their performance? Seems that with the best research applied, best training data, they'd be able to top the charts with a 60B model quite easily.

MisterKent 4mo ago
They want people to believe they have massive models, that is effectively their moat at this point. Because if they don't imply that size is needed for every task, they'll end up tanking their valuations. https://blog.nilesh.io/post/ai-profit-race https://blog.nilesh.io/post/ai-profit-race
redox99 4mo ago
Qwen 35B isn't even remotely close to the big models. It's just people over hyping small models. Ignore the benchmarks they are almost meaningless. If you want something comparable you need the trillion parameter open models like deepseek.
- otabdeveloper4 4mo ago
  Number of parameters doesn't make the model smarter, it just makes it know more stuff out of the box. At some point there's diminishing returns and your coding LLM performs worse because you encoded useless stuff like Pokemon combinations or languages you don't speak into its parameter space. The "smartness" of the model comes from RLHF post-training, which is orthogonal to model size. Also, if you're using an agentic harness a much better approach is to let the model control its own context. If you ever reach a point where your coding LLM needs to know about Pokemon, just give it a web search tool and let it google the Pokemons.
  - redox99 4mo ago
    That's just... not true. Just compare any open model which is trained with the same recipe but multiple sizes.
    - oneshtein 4mo ago
      You can compare models at OpenRouter site. Qwen 3.6 dense is in top 24% for coding.
    - otabdeveloper4 4mo ago
      > Just compare any open model which is trained with the same recipe but multiple sizes. That's exactly what I did.
  - deleted 4mo ago
    [deleted]

by nullbio 4mo ago

MisterKent 4mo ago

They want people to believe they have massive models, that is effectively their moat at this point. Because if they don't imply that size is needed for every task, they'll end up tanking their valuations. https://blog.nilesh.io/post/ai-profit-race https://blog.nilesh.io/post/ai-profit-race

redox99 4mo ago

Qwen 35B isn't even remotely close to the big models. It's just people over hyping small models. Ignore the benchmarks they are almost meaningless. If you want something comparable you need the trillion parameter open models like deepseek.

otabdeveloper4 4mo ago
Number of parameters doesn't make the model smarter, it just makes it know more stuff out of the box. At some point there's diminishing returns and your coding LLM performs worse because you encoded useless stuff like Pokemon combinations or languages you don't speak into its parameter space. The "smartness" of the model comes from RLHF post-training, which is orthogonal to model size. Also, if you're using an agentic harness a much better approach is to let the model control its own context. If you ever reach a point where your coding LLM needs to know about Pokemon, just give it a web search tool and let it google the Pokemons.
- redox99 4mo ago
  That's just... not true. Just compare any open model which is trained with the same recipe but multiple sizes.
  - oneshtein 4mo ago
    You can compare models at OpenRouter site. Qwen 3.6 dense is in top 24% for coding.
  - otabdeveloper4 4mo ago
    > Just compare any open model which is trained with the same recipe but multiple sizes. That's exactly what I did.
- deleted 4mo ago
  [deleted]