Which LLM API is actually worth you

Stop paying for five different subscriptions and start using a unified API proxy or a specialized provider if you're actually coding. Most developers waste money on "Pro" monthly plans when they only hit the high-token limits for three days a month, then coast on the free tier for the rest.

If you are using Cursor, you've probably noticed that the built-in Claude 3.5 Sonnet is the current gold standard for logic and refactoring. But when I move to my own IDE setup or need to run bulk scripts for codebase migrations, the "worth" of an API is measured by the latency-to-intelligence ratio.

The "Heavy Lifter": Claude 3.5 Sonnet
This is currently the only API I keep on a heavy pay-as-you-go budget. It handles complex architectural changes without "forgetting" the context of the third file you opened. Where it wins is in following strict system prompts. If I tell it to "Use early returns and avoid nested if-statements," it actually does it. GPT-4o tends to drift back into verbose patterns after a few turns.

The "Quick Fix": GPT-4o-mini
Stop using the flagship models for unit tests or boilerplate. 4o-mini is ridiculously cheap and fast enough that it feels instantaneous. I’ve scripted a local pre-commit hook that sends my diffs to 4o-mini just to check for obvious typos or missing error handling. It’s a massive productivity gain because it catches the "stupid" bugs before they even hit a human reviewer.

The "Wildcard": DeepSeek-V3
If you're looking for the best bang-for-buck, DeepSeek is dominating right now. The coding capabilities are surprisingly close to Sonnet, but the cost is a fraction. I use it primarily for generating repetitive scaffolding or when I need a second opinion on a regex that is driving me crazy.

One trick to maximize these APIs is to stop sending the entire file if you don't have to. I use a small bash script to strip out comments and unnecessary imports before sending a snippet to the API, which saves tokens and reduces the noise for the model.

# Quick way to strip comments from a JS file before sending to API
sed -i 's/\/\/.*//g' target_file.js

My Current Config Strategy:

Cursor (Claude 3.5 Sonnet): Used for the actual "thinking" and complex feature implementation.
OpenRouter (Mixed): I route everything else through OpenRouter. It lets me swap between Llama 3.1 405B and DeepSeek without changing my API keys every time a new benchmark drops.
Local Ollama (Qwen 2.5 Coder): For when I'm working on sensitive data that can't leave the machine. Qwen 2.5 Coder 7B is surprisingly capable for simple autocomplete.

The biggest gotcha is "Context Window Fatigue." Just because a model supports 200k tokens doesn't mean it's utilizing them effectively. I've found that providing a .cursorrules file or a dedicated context.md that outlines the project structure is far more effective than just dumping 20 files into the prompt.

Example .cursorrules snippet for better API responses:

- Always use TypeScript strict mode.
- Prefer functional components over class components.
- When modifying existing functions, only output the changed lines using a diff format to save tokens.
- No conversational filler; start directly with the code.

If you're just starting, don't commit to a monthly plan. Use a pay-as-you-go provider, track your usage, and you'll realize that 80% of your tasks only require the cheapest "mini" models, while only 20% actually need the expensive intelligence of Sonnet.