Why I refused to lock users into one model
Every other AI code builder picks a model and locks you in. You get whatever they negotiated the best deal on, and you work around its blind spots. The problem is simple: no single model is the best at everything. A model that writes clean React components might produce sloppy database schemas. A model that handles complex backend logic might generate verbose, over-engineered frontends.
I built VULK with 16 models because I wanted to solve this properly. Through OpenRouter, you get access to models from Anthropic, OpenAI, Google, Meta, DeepSeek, and others. You choose the model per conversation. This is not a gimmick -- it is a practical tool that directly affects the quality, speed, and cost of what you build.
The reason we chose OpenRouter specifically is that it gives us model diversity without vendor lock-in. When a new model drops, we can add it in hours. When a provider has an outage, users can switch to another model and keep working. The AI landscape moves too fast to bet everything on one provider.
The models and when to use each one
Let me be honest about each model's strengths and weaknesses. I have watched thousands of projects get generated across all of them.
Claude 4.5 Sonnet (Anthropic) -- This is the model I recommend for most projects. It follows complex instructions precisely, maintains consistency across large file sets, and handles multi-file architectures better than anything else. It rarely hallucinates APIs or invents libraries that do not exist. Where it shines: full-stack applications, complex business logic, backend generation with auth and database schemas. Where it struggles: it sometimes produces functional but visually plain interfaces that need a follow-up prompt for polish.
Claude 4 Opus (Anthropic) -- Maximum reasoning capability. Slower and more expensive, but superior for problems that require deep thinking -- refactoring existing code, solving subtle bugs, applications with complex state management. I use this when something is not working and I cannot figure out why. It is overkill for a simple landing page.
GPT-4o (OpenAI) -- Fast, capable, and produces genuinely good-looking output. GPT-4o's CSS instincts are better than Claude's -- it picks better spacing, typography, and color relationships by default. Best for rapid prototyping, landing pages, and UI-heavy applications where visual polish matters more than architectural perfection. The trade-off: it is slightly less precise on complex multi-file projects.
GPT-4o Mini (OpenAI) -- The budget workhorse. Good enough for simple CRUD apps, component modifications, and learning projects. Responds fast and costs a fraction of frontier models. If you are iterating on small changes, there is no reason to burn credits on a more expensive model.
Gemini 2.5 Pro (Google) -- Strong context window handling and particularly good with Flutter and Dart. If you are building a mobile app, Gemini 2.5 Pro is worth trying. It also handles long, detailed prompts well because of its extended context window.
Gemini 2.5 Flash (Google) -- Google's speed-optimized model. Very fast with reasonable quality. Good for quick edits and simple applications where turnaround time matters more than perfection.
DeepSeek V3 -- This is the one that surprises people. An open-source model that produces code quality comparable to models costing five times more. Excellent with JavaScript, Python, and standard web frameworks. If you want to maximize your credits, DeepSeek V3 is the smart choice for standard web applications.
DeepSeek R1 -- The reasoning variant. Shows its thinking process and excels at algorithm-heavy problems, complex data transformations, and debugging. Think of it as the "show your work" model.
Llama 3.3 70B (Meta) -- Capable open-source model at low cost. Good for standard CRUD applications and simple frontends. Not as strong on complex architectures, but the credits go far.
How model choice shows up in your code
The differences are not abstract. They show up concretely in what gets generated.
Architecture and structure. Claude models produce cleaner separation of concerns -- utility functions in separate files, types in dedicated type files, hooks extracted properly. GPT-4o tends toward flatter structures with more code in fewer files. Both approaches work, but Claude's output requires less refactoring for larger projects.
Error handling. Claude 4.5 Sonnet consistently adds try-catch blocks, loading states, error boundaries, and edge case handling. GPT-4o adds error handling when prompted but sometimes skips it in the first generation. DeepSeek V3 handles the happy path well but may need a follow-up for comprehensive error handling.
Visual design. GPT-4o produces more polished interfaces by default -- better spacing, typography, color harmony. Claude focuses on functionality first. If you care about pixel-perfect design on the first try, GPT-4o has the edge.
Backend code. Claude 4.5 Sonnet is the strongest here. It consistently generates proper password_hash columns (not password), parameterized SQL queries, complete CRUD with validation, and auth middleware. Other models occasionally take security shortcuts.
Cost per generation. A typical full app (initial prompt + 3 iterations) costs roughly: Claude 4.5 Sonnet 15-25 credits, GPT-4o 10-18 credits, DeepSeek V3 3-8 credits, GPT-4o Mini 2-5 credits.
My practical recommendations
Here is the decision framework I actually use:
"I need production-quality full-stack." Start with Claude 4.5 Sonnet. The initial generation has better architecture and security. The higher credit cost pays for itself in fewer iterations.
"I am prototyping fast." Use GPT-4o. It is fast, looks good, and handles iterative prompts well. Switch to Claude for the final polish if needed.
"I am building something simple or learning." Use DeepSeek V3 or GPT-4o Mini. The quality is solid for simple projects, and your credits last much longer.
"I am building a mobile app." Use Gemini 2.5 Pro for Flutter. It has strong Dart/Flutter knowledge. For React Native, Claude 4.5 Sonnet is the better choice.
"I am debugging or refactoring." Use Claude 4 Opus or DeepSeek R1. Their reasoning capabilities trace through existing code and spot issues that faster models miss.
"I am building a game." Use Claude 4.5 Sonnet for the initial generation (Three.js games have complex interconnected systems), then switch to GPT-4o for visual polish and quick iterations.
Switching models mid-project
This is a workflow most people do not realize is possible, and it is one of the most powerful features in VULK.
- Generate the initial app with Claude 4.5 Sonnet (best architecture).
- Switch to GPT-4o for rapid UI iterations (faster, better CSS).
- Switch to DeepSeek V3 for small tweaks (cost-efficient).
- Switch back to Claude for adding backend and auth (best security).
Each message is independent. The model reads the existing code and your new prompt, then generates changes. It does not need to be the same model that wrote the original code. Use expensive models where quality matters and cheap models where it does not.
What model selection is not
It is not a magic wand. A bad prompt produces bad output regardless of which model you choose. The fundamentals still apply: be specific, describe the layout, list features, specify the data model. The model determines the quality ceiling, but your prompt determines how close you get to it.
More expensive does not always mean better for your specific task. Claude 4 Opus is the most capable model available, but for a simple landing page, GPT-4o Mini produces perfectly good results at a fraction of the cost. Match the model to the task, not to your aspirations.
Why this matters long-term
The model landscape changes fast. New models launch monthly, existing ones improve, pricing shifts. VULK adds new models as they become available. The multi-model approach means you are never locked into a model that becomes outdated -- you switch to the next best option without changing platforms.
This is why I built model selection as a core feature, not an afterthought. The best model for your project today might not be the best six months from now. You should never have to switch platforms to access better AI. Try all 16 models at vulk.dev.
