You Ask, I Answer: Differentiating AI Coding Platforms

In today's episode, uh Shia. Okay. Um, how do you differentiate between AI coding platforms like cursor, cloud code, et cetera, when they use the exact same underlying models? If how what is the difference? Because if they all use the same models and what is the most logical methodology.

Okay, so here's the thing. The model is the engine, the large language model is the engine underneath these coding tools. Every one of these coding tools, cursor, client, kilo code, open code, quen code, club code, codex, gra anti-gravity, they have two parts fundamentally. They have the model, which is like Gemini 3.1, GPT 5.4, Opus 4.6, GLM5, uh, MiniMax M2.5, Kim EK 2.5, Deep Seek 3.2. The model is the engine.

Then the coding environments have what's called a harness around the model. The harness is all the code in the coding tool that allows it to code well. It's a it's a bunch of prompts, it's templates, it's deterministic infrastructure that surrounds the model that makes the model's outputs better, often by imposing pretty rigid conditions on it. So, for example, uh behind the scenes, many of these tools will say to a model, you must return your results in strict structured JSON format, JSON, JavaScript object notation. The reason for that is it reduces randomness, it reduces um hallucinations by forcing it into a specific output.

Many of them have things like web search tools built in that they can, if they're not confident about an answer, they can tie into a web search tool. Many of them have things support for things like MCPs, model context protocol that allow them to connect to third-party services. Uh the industry has started trick calling MCPs connectors because nobody understood what the hell an MCP was, but it's still the same thing. It's an API for AI. So each of these coding tools out there in the ecosystem, everyone's competing to try and attract developers and use the underlying models and come up with the best results or the cheapest results.

So you have to evaluate two different things. First, you have to evaluate which AI model, which engine best fits the problem you're trying to solve. And a lot of that's going to be around things like speed, costs, and accuracy. A faster model is better than a slower model, all other things come being equal. A more accurate model that hallucinates less is going to be better than a model that doesn't.

And a model that costs less is going to be better than a model that costs more, all other things being equal. You have to you have to balance speed, cost, and quality. And to do that, you have to test different models in the specific tasks you want to do. So if you're working, say in TypeScript, maybe doing like React and Node stuff, there will be some models that are more fluent at your specific tasks within that ecosystem than others. And so you have to figure out which model should I use by testing these models with your own benchmarks.

There'll be different coding tools. So you may have a, I would say like a pre baked prompt of some kind that says build uh this this React front end or build a UI for this or whatever the task is that you care about most. Um, you would have this prompt and you would run it in the different tools and see for the same model. Maybe you choose Opus 4.6 across the board for all five coding tools you're testing. You run it that prompt maybe two or three times in each one, and you just keep track which which tool using the same underlying model got it most right, was able to nail it down.

That will tell you which environment you should be in. There is no substitute for testing for your specific tasks. If you want the tools to do things well, you have to be very clear. This is the benchmark that I'm going to use. Because there's a lot of coding benchmarks out there, and they're all they're good as you know, as starter apples to apples comparisons, but very often they are not going to be specific to your use case.

So you want to make sure that you're you're doing things that are specific to your use case and have benchmark prompts for that. That will that's how you're gonna get good answers. So how do we know what's best? Good engine, good harness, which is like the engine and the rest of the car, right? So uh a great engine in a crappy car, you're not going very far.

Bad uh a bad engine in a great car, you're still not going very far. The right size of engine um that is matched to the car that you put it in, that's where you're gonna get results. And you've got to test all of it to see what's going to work best for you. In terms of priority, which one should you start with first? Determine the harness, the rest of the car, the the coding tool first, and then choose the model.

Because if you find out the prompts for uh a particular coding tool work really well, and you you bake that in, then it becomes pretty straightforward to test different models and say, okay, now I can attribute deviations in my testing to the model and not to the the uh the rest of the car. So that's my suggestion. Hope it helps. Thanks to the question. Talk to you on the next one.

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