Speed vs smarts for coding agents?

TLDR - SMARTZ!

I prefer functionality first, it can be made faster later, right? I do not want a lightning fast pile of cr*p. I'm fine waiting 5 minutes lol. I like to be able to ask specific questions and have them be handled gracefully.

I would even go so far to say that when it comes to development, a super fast lightweight model could potentially send you on all these unrelated side tangents, which results in a slower process, not a faster one. If we can be just a tiny patient, there's a better chance we start on the right foot. Why does it all have to be warp speed? I'll probably need like half a second to think or something, at some point.

What are we running towards? Are we sure we are not running in circles? 😅

To follow up those questions:

I'll never forget when I first started asking AI questions in VSCode. Side-quest-city. It would take me in loops over something dumb like the server not running or package not installed. Imagine the feeling when I realized that. I have a mistrust of - specifically - copilot in VSCode. Fast forward two years, I still hate copilot in VSCode, but I LOVE using copilot CLI. It has almost never done me wrong.

One thing that has irritated me lately is sometimes 1 and l look nearly identical. This is a trap that AI would never fall for! (True story from last week fighting with CSS classes)

I think speed is what gives the user productivity. I think it's based on how you define productivity. Is it how fast you can finish the product or is it something that you implement and learn as you go?

I tend to see productivity as working faster in a way that it reduces redundancy. For example, if I wrote a specific block of code before, instead of typing it out, I ask the AI to write it for me. That way, it saves me time.

The honest answer is: it depends on where in the workflow the agent is sitting.

For inline autocomplete and small edits speed wins completely. Any latency breaks the flow. I'd rather have a fast, slightly dumber suggestion than a brilliant one that takes 3 seconds to appear.

But for anything involving reasoning across files, architecture decisions, or debugging something non-obvious I'll wait. The cost of a fast wrong answer is higher than the cost of a slow right one. A quick confident hallucination in a complex debugging session can send you down the wrong path for 30 minutes.

So I've started thinking of it less as speed vs smarts and more as: reversibility of the task. Easy to undo? Give me speed. Hard to undo? Give me the smartest model available.

You are forgetting another important aspect: price.

when choosing which assistant or CLI to use on a project, I analyze:

• how fast it can deliver the changes
• how good I need the code to be
• how much I'm willing to pay for these changes

Claude Code is really good at complex tasks. It can handle huge codebases without hallucinations. It's fairly fast. But for some tasks, just two prompts and BAM, token limit reached, start using the API tokens instead.

Gemini is more balanced, but coding quality isn't so good as Claude and I saw it enter loops several times, for more complex tasks.

And so on. Honestly, when I can, I prefer to have better coding, even if it'll cost more.

For me it depends on the feedback loop length.

Fast model for tight loops (lint, test, iterate) where I'm watching and can course-correct in real time. Smart model for longer autonomous tasks where a wrong turn at step 3 wastes 20 minutes of downstream work.

In practice I've found the biggest quality lever isn't the model itself but the context you feed it. A fast model with good structured input (prior review feedback, real data, specific constraints) beats a smart model with a vague prompt. Speed with context > smarts without it.

My day-to-day tasks don't require too much intelligence so I'm generally optimizing for speed to get things out the door. Perhaps one day I'll have something more complex to work on.

for multi-agent workflows the calculus changes a lot. a single smart-but-slow model is fine, but once you have 5-10 agents chaining off each other, latency compounds. i've started defaulting to faster models for the "middle layer" agents that are mostly routing and state management, and reserving the heavy models for the leaf tasks that actually need reasoning depth. the question i keep asking is: does this step need intelligence or just reliable execution? most orchestration steps are the latter.

Speed matters until it doesn't. For scaffolding, boilerplate, anything I already know the answer to. fast is fine. For architecture decisions, anything that touches how the system is structured or how components talk to each other — I want the smartest model in the room, full stop.
The expensive mistakes aren't in the code that's obviously wrong. They're in the code that looks right but made a decision I didn't authorize. A faster model gets you to that mistake quicker.

It seems to me that the most important thing about AI is its ability to process long context. Speed ​​may vary. AI largely depends on the hardware it runs on, so this factor may be ambiguous.

We stopped treating this as a binary. The answer is both, routed by task type.

On a large PHP codebase, we run Opus for architecture decisions and code that touches business logic — the kind of work where a wrong assumption costs hours of debugging. But for batch operations (documentation sweeps, type annotations, renaming 200 constants), we spawn Haiku sub-agents. They're 20x cheaper, fast enough, and the work is mechanical — you don't need deep reasoning to add a final keyword to a class with no children.

The routing isn't automatic — we decide per task. But the pattern is clear: smart for judgment, fast for labor. Trying to use one model for everything either wastes money or wastes quality.