It's a different type of model. In my admittedly judgemental observation, people that aren't the type to configure fully automated harnesses with good tools and skills and verifiers for their infrastructure and are way more interventionist in the way their agent works tend to like grok 4.5 more as the main agent. It's much faster and writes more simple and normal code that aligns a bit more with human written code. As an example, instead of sandboxing and simply verifying output artifacts they manually read and approve edits, suggest different code patterns, and manually approved shell commands. On the flip side it's not as good as fable when you need a relatively complex multi step thing done.

But in my experience, the overall productivity ends up similar, give you are willing to work with it in that way.

The grok build TUI harness is excellent and I really enjoyed using it.

For debugging and such I found it pretty much the same as other models.

Fable's taste in software abstraction and project planning in greenfield setups[1] is unmatched in my experience. Sol is OK. My primary use is launching tens of experiments that have to smartly use a limited pool of GPUs.

I use fable to start off the experiments, decide checkpoints, gpu alloc, where to sacrifice precision for performance, and then grok4.5 to iterate, tune, debug, eval, etc, within the abstraction and setup that fable initiated. I have fable write simple scripts that are then wrapped in skills for grok to use. Speed for that loop is extremely important for me, since I also apply human judgement there and I don't like waiting for model output.

I have tried Deepseek and such for the inner agent, but I desperately need multi-modal. Otherwise it's OK, but it tends to use tools less and rambles on and tries to reason with limited information and gets things wrong. Probably a relative la k of tool use posttraining. Gemini flash limits in google ai pro are too low for me to use to compare.

I use anthropic and openais models through grants and so can't compare subscription plan token budgets, but supergrok's budgets are satisfactory.

[1] aside, I have not yet met a model that continues off of a human codebase and actually follows the patterns reliably long term. Eventually it's all slop.

It's much faster, so if you're not doing something cutting-edge, or you're doing the planning yourself and just using the LLM for implementation, the speed benefit outweighs the extra smarts of Fable/Opus5/Sol.