Which model should I actually use for code review versus writing new code?
I spent three weeks running the same pull request through four different models to see which one caught the bugs my team kept missing. Claude Sonnet 4.5 found the edge cases. Gemini Flash ran faster but missed a race condition that would've broken our payment webhook. DeepSeek R1 wrote an essay about why the code was fragile but took forty seconds to do it.
Here's what works: use Claude Sonnet 4.5 for code review. It catches logic errors, spots security issues in API calls, and explains what's wrong in plain English. When you're reviewing a PR from a junior dev or auditing a function you wrote at midnight, Sonnet reads the code the way a senior engineer does. It doesn't just flag syntax; it asks whether your error handling makes sense.
For writing new features or scaffolding a module, Gemini Flash is faster and cheaper. It autocompletes boilerplate, generates CRUD endpoints, and writes the first draft of a React component in seconds. The output isn't always elegant, but it's correct enough to edit. I use Flash when I know exactly what I want and just need the code on the screen.
The mistake: using the same model for both. Review needs scrutiny. Writing needs speed. If you're paying ₹900 a month for a multi-model workspace and still running everything through one model, you're leaving time on the table.
Does Compare mode actually save time or is it just running the same prompt twice?
Compare mode runs your prompt through two or three models at once and shows the outputs side by side. The first time I used it, I thought it was a gimmick. Then I tried debugging a function that was failing silently in production, and Claude suggested I check the database connection pool while Llama 3.3 70B told me to add logging. Both were right. The bug was a pool exhaustion issue that only showed up under load, and I wouldn't have found it without the logging Llama suggested.
Compare saves time when you don't know which approach will work. Debugging is the obvious case: you're stuck, you're not sure if it's a logic error or a config problem, and running the same question through Claude and DeepSeek gives you two angles in fifteen seconds. One model might focus on the code, the other on the environment. You pick the answer that fits your setup and move on.
It's also useful when you're writing documentation and you want to see which model explains a concept more clearly. I compared Claude and Gemini Flash on a README for a Shopify app I built, and Flash wrote a better quick-start guide while Claude wrote better API reference docs. I used both.
When Compare wastes time: when you already know the answer and you're just second-guessing yourself. If you're writing a simple function and you understand the requirements, pick one model and go. Don't compare three outputs for a ten-line helper function.
Are reasoning models like DeepSeek R1 worth the extra wait for actual dev work?
DeepSeek R1 takes longer because it's a reasoning model—it thinks through the problem step by step before answering. For debugging, that's often worth it. For autocomplete, it's not.
I tested R1 on a bug where our Express.js API was returning 500 errors intermittently. Claude Sonnet gave me a list of possible causes. R1 walked through the request lifecycle, identified that the error only happened when two users hit the same endpoint within 50 milliseconds, and explained why our middleware wasn't handling concurrent writes. That explanation saved me two hours of console.log debugging.
Use R1 when the problem is complex and you need to understand why something's broken, not just what to fix. It's good for architectural decisions, performance bottlenecks, and bugs that only happen in production. It's terrible for "write me a function that validates an Indian mobile number" because you don't need reasoning; you need regex.
The wait is real. R1 can take thirty to fifty seconds for a detailed answer. If you're in flow and you need a quick fix, that's long enough to break your concentration. I keep a second tab open: R1 in one, Gemini Flash in the other. Flash for speed, R1 for depth.
How do I stop losing context when I switch between models mid-task?
You don't switch mid-task. You switch between tasks.
If you're writing a new feature, stick with one model for the whole feature. Don't write half a component in Gemini, then switch to Claude to finish it, then switch to Llama to refactor. You'll end up with three different coding styles, inconsistent variable names, and a function that works but feels like it was written by a committee.
The workflow that works: one model per type of task. Claude for PR reviews. Gemini Flash for scaffolding new code. DeepSeek R1 for debugging production issues. Llama 3.3 70B for writing tests. Each model gets a clear job, and you don't switch until the job changes.
Context stays in your head, not in the model. I keep a scratch file open with notes on what I'm building, the edge cases I'm worried about, and the dependencies I've added. When I switch from writing code to reviewing it, I paste those notes into the review prompt. The model doesn't remember the earlier conversation, but it doesn't need to if your prompt includes the context.
The exception: if a model gives you a bad answer, switch immediately. Don't keep rephrasing the same prompt hoping for a better result. Copy the conversation, paste it into a different model, and say "this is what I tried, it didn't work, here's the error." Fresh eyes, even if they're silicon.
Should I use Auto routing or pick the model myself every time?
Auto routing sends your prompt to whichever model the system thinks will answer it best. It's fast, it works, and it's wrong often enough that I don't trust it for code.
The problem: Auto doesn't know what you're optimizing for. If you're writing a quick script to parse a CSV and you need an answer in five seconds, Auto might send you to DeepSeek R1 because it thinks the task is complex. You wait forty seconds for a thoughtful explanation when Gemini Flash would've given you the code in three.
Pick the model yourself when you know what you need. Code review? Claude. Boilerplate? Flash. Debugging? Compare Claude and R1. Auto is useful when you're asking a general question and you genuinely don't care which model answers, but most dev work isn't general.
I use Auto for documentation and comments. If I need a docstring for a function or a commit message for a refactor, I don't care whether Claude or Gemini writes it. The output's the same, and Auto picks whichever one's faster at that moment. For everything else, I choose.
The time you save by not thinking about which model to use is smaller than the time you lose when Auto picks wrong. It takes two seconds to click Claude instead of Auto. It takes two minutes to rewrite a bad answer.
How much does this model-per-task setup actually cost in rupees per month?
Kryotta's multi-model plan costs ₹900 a month. You get Claude Sonnet 4.5, Gemini Pro and Flash, Llama 3.3 70B, Mistral Large, DeepSeek V3 and R1, and the GPT-OSS models. You also get Compare mode, which is the feature that makes the whole workflow work.
For comparison, ChatGPT Plus costs around ₹1,650 a month (US$20 converted), and you get one model. GitHub Copilot is ₹830 a month and it's good for autocomplete but useless for debugging or code review. Cursor is ₹1,650 and locks you into VS Code.
The math matters if you're a freelancer in Pune or Indore billing clients ₹40,000 a month. If a multi-model setup saves you three hours a month by catching bugs faster or writing boilerplate you don't have to debug later, it's paid for itself twice over. If you're at a startup and your team of four developers is sharing one ChatGPT account because ₹1,650 per seat feels steep, ₹900 for five models is a better deal.
The hidden cost is learning which model does what. Budget a week of experimenting. Run the same tasks through different models, keep notes on which ones give you useful answers, and build your own mental map. After that, the workflow becomes automatic and you stop thinking about it.
Questions people ask
Can I use different models for frontend and backend code, or does that create inconsistency?
You can, and it won't create inconsistency if you're consistent about how you prompt. The model doesn't matter as much as the instructions you give it. If you tell Claude to use async/await and Gemini to use Promises, you'll get inconsistent code. If you tell both to follow your project's style guide, they'll both follow it. The real risk is switching models mid-feature, not using different models for different parts of the stack.
Is there a model that's better for writing SQL queries or working with databases?
Claude Sonnet 4.5 writes better SQL than the others. It handles joins, subqueries, and window functions without getting confused, and it explains what the query does in plain English. Gemini Flash is faster but makes mistakes with complex aggregations. If you're writing a query that touches three tables and includes a CASE statement, use Claude.
What if I'm working on a project that uses a framework the model doesn't know well—should I stick to one model or try several?
Try several, but do it in Compare mode so you're not wasting time on sequential attempts. Newer frameworks (say, SvelteKit or Qwik) aren't in every model's training data, but one of them usually has better coverage. I've found that Gemini Flash tends to know newer JavaScript frameworks, while Claude knows Python frameworks better. Run a test prompt, see which one gives you working code, and use that model for the rest of the project.
Does switching between models slow me down more than just learning one really well?
Only if you're switching randomly. If you assign each model a clear role and stick to it, the workflow becomes automatic. It's like using different tools: you don't use a screwdriver for every job just because you're good with a screwdriver. The slowdown happens when you're indecisive, not when you're deliberate.
The model-per-task workflow isn't about using more AI. It's about using the right AI for the job, which usually means using less of the wrong one. If you're still running everything through one model because it's easier than deciding, you're optimizing for the wrong thing. Try Kryotta for a week, assign each model a job, and see whether your PRs get cleaner and your debugging gets faster. Most developers notice the difference in three days.
