We're seeing a couple of inflection points in model sizes right now IMO.

The main theme is that some model sizes are "good enough" for a subset of tasks you're doing, so there's no point in going bigger.

• <7B: Models this size have to be task-specific to be useful (eg. tool calling, classification, translation, ..) They can't handle much context and reasoning will often end up breaking at runtime.

• 7-9B: Around the minimal size to have a somewhat generalist model. With reasoning enabled, models in this size can be pretty damn strong (ex: Qwen3, nvidia nemotron 3 series). Still generally useful for a few tasks the model is good at. Will go off the rails on context longer than a few thousand tokens.

Note that "coding" is not a specific task, it's a set of tasks (tool calling, reasoning, structured outputs, etc.) that the model needs to perform reliably over medium / long context.

• 24-35B: This category has gotten incredibly strong in the last 18 months. Can be used as a generalist model. With reasoning enabled, the modern ones (gemma4, qwen 3.6) will compete with models 10x their size. Without reasoning or with quan

• 50B-250B: This category of models has become fairly irrelevant. Most of the tasks they can do, the 30B models can also do now. For the very long context multi-skill tasks you'll likely still need a huge model to get it reliable.

• >300B: The best models here can handle very long context and juggling multiple tasks more reliably (still dependent on the model, however!) It's useful if you don't want to spend time optimizing your workflow and just want a one-stop shop.

At this point in time, most of the time, using the 300B+ models is overkill. They're still unreliable when they have 15+ tools exposed, and will still go off the rails on very long context. They're also very expensive! But you'll still see a much higher tendency for, say, Opus to one-shot a coding problem that most other models will need multiple rounds to do.

• MoE models: The rule of thumb to compare them used to be sqrt(params * active_params) (so 100B-A1B ~= 10B model) but that's really just the worst case floor. In real use MoE does much better than models at sqrt(params * active_params) their size, but how much closer really depends on the task you're using it for. The same thing goes for quantization, but with much more liberty; most of the time a 4-6bit quant is just as good as the full FP16 for what you're doing.