Cursor Agent Mode Tutorial: Building a REST API with GPT-5.6 Luna

If you're using Cursor Agent Mode to build software, the model you choose can make or break your workflow. Agent mode hands over autonomous coding tasks to an LLM: it edits files, runs commands, and iterates until the job is done. That autonomy means every session makes dozens, sometimes hundreds, of model calls. Token cost and response latency quickly add up. In this tutorial, you'll build a REST API with Cursor Agent Mode using GPT-5.6 Luna, a cost-efficient model available through OneMux. We'll cover why Luna is a strong pick for agentic coding, how to configure it, and walk through a concrete API build.

Why GPT-5.6 Luna Is a Smart Choice for Cursor Agents

GPT-5.6 Luna is part of OpenAI's GPT-5.6 family, which also includes Terra and Sol. Luna is the entry-level tier: it costs $0.60 per 1M input tokens and $3.60 per 1M output tokens. That's significantly cheaper than Terra ($1.50/$9.00) and Sol ($2.50/$15.00). For agentic coding, where you might burn through hundreds of thousands of tokens in a single session, those savings matter.

But “cheap” doesn't mean “weak.” Luna is a general-purpose model that handles code generation, debugging, and refactoring well. In Cursor Agent Mode, it can reason about file structures, write functions, and fix errors. For most REST API tasks—creating endpoints, models, validation, and boilerplate—Luna gives you plenty of power at a fraction of the price.

OneMux makes it easy to compare models. Here's a snapshot from the OneMux model catalogue:

When you use Cursor as an agent, you don't need the most expensive model for every step. Standard CRUD endpoints, Pydantic schemas, and test scaffolding are well within Luna's reach. For complex architectural decisions or nuanced debugging, you might switch to Sol or Claude Opus—but for most of the work, Luna is the cost-effective default.

Setting Up GPT-5.6 Luna in Cursor

Cursor lets you plug in custom models via an OpenAI-compatible API. OneMux provides exactly that: a single API endpoint for many leading models, including GPT-5.6 Luna. Here's how to get started.

1. Create an account at OneMux and grab an API key.
2. In Cursor, open Settings > Models > OpenAI API Key and paste your OneMux key.
3. Set the API base URL to OneMux's endpoint. The exact URL is in the OneMux quickstart guide.
4. Select a model. You'll need the Luna model identifier, which you can find on the models page or in the docs.

Once configured, Cursor Agent Mode will route its calls through OneMux. You'll see the model in your model picker and can start using it for agentic tasks. OneMux also gives you spend visibility and credit management, so you'll always know how many tokens your agent consumed.

Building a REST API with Cursor Agent Mode

Let's put Luna to work. We'll build a simple in-memory to-do list REST API using FastAPI. Open Cursor Agent Mode and give it this prompt:

Create a FastAPI REST API for a to-do list app. It should support creating, listing, updating, and deleting todos. Store todos in memory. Add validation with Pydantic and include a health endpoint.

Cursor Agent Mode will create the project files, write the code, and might even install dependencies. Here's an example of the kind of code Luna can generate:

from fastapi import FastAPI, HTTPException, status
from pydantic import BaseModel
from typing import List, Optional

app = FastAPI()

class TodoCreate(BaseModel):
    title: str
    completed: bool = False

class Todo(TodoCreate):
    id: int

todos = []
next_id = 1

@app.get("/health")
def health():
    return {"status": "ok"}

@app.get("/todos", response_model=List[Todo])
def list_todos(completed: Optional[bool] = None):
    if completed is None:
        return todos
    return [t for t in todos if t.completed == completed]

@app.post("/todos", response_model=Todo, status_code=status.HTTP_201_CREATED)
def create_todo(todo: TodoCreate):
    global next_id
    new_todo = Todo(id=next_id, **todo.dict())
    todos.append(new_todo)
    next_id += 1
    return new_todo

@app.put("/todos/{todo_id}", response_model=Todo)
def update_todo(todo_id: int, todo: TodoCreate):
    for t in todos:
        if t.id == todo_id:
            t.title = todo.title
            t.completed = todo.completed
            return t
    raise HTTPException(status_code=404, detail="Todo not found")

@app.delete("/todos/{todo_id}", status_code=status.HTTP_204_NO_CONTENT)
def delete_todo(todo_id: int):
    for i, t in enumerate(todos):
        if t.id == todo_id:
            todos.pop(i)
            return
    raise HTTPException(status_code=404, detail="Todo not found")

This is a solid foundation. The agent can then run tests, add more features, or refactor the code—all within Cursor Agent Mode. Luna handles the context and edits without missing a beat.

Understanding the Cost: Luna vs. More Powerful Models

A typical agent session for a small API might consume around 100,000 input tokens (including codebase context and prompts) and 20,000 output tokens (generated code and edits). Here's what that costs with different models:

So Luna is not just cheaper—it's dramatically cheaper for iterative workflows. If you're building an API over several days, those savings compound. For a more complex project requiring deep reasoning, you could always switch to Sol or Claude Opus. But for the majority of agentic REST API work, Luna is the pragmatic choice.

Tips for Getting the Best Results from Cursor Agent Mode

• Be explicit in your prompt: Describe the framework, data models, endpoints, and any constraints. The agent's output improves significantly when you give it a clear spec.
• Ask for tests: Have Cursor generate and run a test suite. Luna can iterate on failures and fix edge cases.
• Use the built-in context: Point the agent to existing files or folders so it works within your project's architecture.
• Monitor your spend: OneMux gives you real-time visibility into token usage and costs, so you can spot unexpected spikes.
• Start with Luna, escalate when needed: If you hit a reasoning wall, switch to a larger model for that specific sub-task, then return to Luna.

FAQ

Is GPT-5.6 Luna available through Cursor Agent Mode?

Yes. Cursor supports custom OpenAI-compatible endpoints. By setting your OneMux API key and base URL in Cursor's settings, Luna becomes available as a model for agentic tasks.

How does Luna compare to Terra and Sol in the GPT-5.6 family?

Luna is the most affordable, with input at $0.60/1M and output at $3.60/1M. Terra and Sol cost more and are designed for heavier reasoning. For typical REST API generation, Luna's capabilities are usually sufficient.

Can I switch models mid-project with Cursor and OneMux?

Absolutely. Because OneMux routes through a single API, you can change the model in Cursor's settings without modifying any code. This lets you start with Luna and switch to a more powerful model if the task gets complex.

Where can I find the full benchmark table for GPT-5.6 models?

DataCamp's Cursor Agent Mode tutorial references a full benchmark table and three-tier pricing guide for GPT-5.6 Sol, Terra, and Luna. You can check that source for deeper performance data.

Conclusion

Cursor Agent Mode turns your ideas into working code, but the model you choose determines both the quality and the cost. GPT-5.6 Luna, available through OneMux, hits a sweet spot for building REST APIs: enough intelligence to produce clean, functional code, with pricing that keeps agentic experimentation affordable.

Set up Luna in Cursor today via OneMux's unified API, and start building faster—without watching every token explode your budget. For more details on models, pricing, and integration, explore the OneMux pricing page and the quickstart guide.

Sources

• DataCamp: Cursor Agent Mode Tutorial