The best AI tools for developers: compare models before shipping code

Chat models and coding agents are different tools

Worth separating up front, because the two get conflated. An agentic coding tool lives in your editor or terminal, reads your repository, and writes files. A chat workspace is where you think: you paste a stack trace, argue about an approach, review a diff, understand a library you have never used, and draft the design doc.

Most developers end up using both, and the chat side is where model choice matters most, because you are reading the reasoning rather than the diff. That is also where paying for three separate subscriptions to compare three models stops making sense.

What you are doing	Model tendency	Notes
Hard reasoning: concurrency, a subtle race, an architectural trade-off	Claude and GPT differ meaningfully	Ask both. This is the case where a second opinion pays for itself
Implementation speed on well-trodden ground	GPT	Fast, idiomatic, good at boilerplate and conversions
Reading a large unfamiliar codebase or a long spec	Gemini	Largest context window, so more of the system fits at once
Explaining an error or a concept	Whichever framing lands	Different models explain differently, and that is the point
Strict structured output: config, JSON, schema	GPT	Most reliable at obeying a format exactly

Debugging prompts that beat pasting the stack trace

Pasting an error and asking what is wrong produces a guess. The guess is often right, and when it is wrong you lose twenty minutes chasing a plausible fix for a problem you do not have. These prompts change the shape of the answer.

Prompt: hypotheses before fixes

Here is the error, the code, and what I have already ruled out. Do not give me a fix yet. List the four most likely causes ranked by probability, and for each, the single cheapest check that would confirm or eliminate it. Error: [paste]. Code: [paste]. Already ruled out: [list].

Prompt: the bug that only happens sometimes

This fails intermittently, roughly [frequency], under [conditions]. Here is the relevant code and what I know about the environment. Enumerate the categories of intermittent failure that could produce this specific symptom (timing, ordering, resource exhaustion, external dependency, state leakage between runs, clock or timezone, caching). For each, say what evidence in what I have given supports or contradicts it, and what I should log to distinguish them.

Prompt: explain the fix before I take it

Explain why this fix works, what it does not fix, and what it could break. If the underlying cause is somewhere else and this is a symptom patch, say so directly.

That last prompt catches the most expensive class of AI assistance: a change that makes the symptom disappear while the actual defect stays in the codebase.

Two models on the same problem, which is not a gimmick

When the answer is obvious, one model is enough. The technique earns its keep on the problems where you are not sure, and it works because the models fail differently rather than identically.

The useful pattern is not to ask both and pick the one you like. It is to ask one, then hand its answer to the other:

Prompt: adversarial review of an answer

Another engineer proposed this solution to this problem. Find what is wrong with it: correctness under edge cases, concurrency, error handling, performance at [scale], or a simpler approach that was missed. If it is actually sound, say so plainly rather than inventing objections. Problem: [paste]. Proposed solution: [paste].

Two outcomes, both useful. Either the second model finds a real hole, which you now know before merging, or it agrees, which is genuine evidence given it had every incentive to disagree. Compare that to iterating with the same model, which tends to agree with itself.

The same pattern applies to design decisions:

Prompt: argue the other side

I am choosing [approach A] over [approach B] for [context and constraints]. Make the strongest case for B. What would have to be true about our constraints for B to be the correct choice, and is any of it true here?

Whizi's side-by-side comparison exists exactly for this, and it is documented in compare models side by side.

Code review and reading unfamiliar code

Prompt: review a diff like a demanding reviewer

Review this diff. Categories, in order: correctness bugs, security issues, unhandled failure modes, race conditions, then style. For each finding give severity, the specific line, and why it matters here rather than in general. Do not comment on formatting. If the diff is fine, say so. Context: this codebase uses [stack and conventions]. Diff: [paste].

Prompt: understand a codebase you just inherited

Here are the main source files. Produce: the entry points, the data flow from request to response, the state that is shared and where it is mutated, the external dependencies and what happens when each is unavailable, and the three parts most likely to contain bugs based on complexity and coupling. Say explicitly what you cannot determine from what I gave you.

That final instruction matters more than it looks. Models will happily describe the behaviour of a file you did not paste, inferred from its name. Forcing an explicit list of unknowns tells you what to go read.

Prompt: write the test you would not have thought of

Write test cases for this function, focusing on inputs I probably have not considered: boundaries, empty and null, unicode, very large values, concurrent calls, and any implicit assumption in the implementation. For each test, state the assumption it is checking. Function: [paste].

The failure modes that actually cost time

Invented APIs. Models confidently produce method names, parameters, and configuration keys that do not exist, especially for libraries that changed recently or are less common. The signature will look right. Check the actual documentation before you build on top of anything unfamiliar.

Confidently wrong fixes. There is no signal in the tone. A fix that dissolves your problem and a fix that introduces a subtle new one are delivered with identical confidence. Always ask what the change could break.

Outdated patterns. Training data skews toward the volume of code written about a framework, which is often the previous major version. If the answer feels like it is from a few years ago, it probably is. Say which version you are on, in the prompt.

Silent scope creep. Ask for a fix and you often get a refactor. Add change as little as possible, and list every line you changed and why to keep the diff reviewable.

Security theatre. A model can name the vulnerability classes in your code, which is genuinely useful for a first pass, but it is not an audit. It does not know your threat model, your deployment, or your data sensitivity.

Where this fits with the rest of your tooling

It does not replace your editor integration or your agentic coding tool. It replaces the three browser tabs where you were comparing answers, plus the two subscriptions that were needed to have those tabs open at once.

The practical setup most developers land on: one default model for quick questions, a second one you switch to when the first answer is unconvincing, and Gemini when you need to put a large amount of code or a long specification in front of a model at once. All in one thread, so the context you already established carries across the switch instead of being re-pasted.

For deeper coverage see AI for coding, the coding-focused alternatives comparison, and the Claude coding prompt pack. The mechanics of running that setup inside Whizi are in write and debug code with multiple models.