AI in VS Code, Cursor & JetBrains: The 2026 Setup Guide

The Three Shapes of AI in Your Editor

By 2026, AI in the IDE has settled into three distinct modes, and it helps to name them because each has a different cost profile and a different failure mode. Inline completion is the ghost-text that finishes your line or block as you type — high-frequency, latency-sensitive, and best served by a fast, cheap model. Chat is the side panel where you ask questions, paste errors, and request a refactor with full file or selection context — lower frequency, but you want a stronger reasoning model here. Agent mode is the newest: you describe an outcome ("add pagination to this endpoint and update the tests") and the editor reads multiple files, plans, edits, and sometimes runs commands, looping until it's done.

Knowing which mode you're in tells you which model you actually want behind it. Completion wants speed; chat wants depth; agents want a model that follows multi-step instructions without drifting. The mistake is letting one default model serve all three.

What Each Editor Does Well

The big three editors have converged on the same feature set but still have distinct personalities.

• • VS Code — the most extensible. Beyond the first-party Copilot experience, a large ecosystem of AI extensions plugs into it, and many of them expose a setting for a custom API endpoint. That openness is the whole reason VS Code is the easiest place to run your own models.
• • Cursor — a VS Code fork built around AI from the ground up. Its strengths are deep repo awareness, fast multi-file edits, and a polished agent loop. It also lets you supply your own provider keys in settings, so you can point its chat at models you control rather than only its bundled lineup.
• • JetBrains (IntelliJ, PyCharm, GoLand, Rider) — the deepest language understanding, because it pairs AI suggestions with the IDE's own static analysis and refactoring engine. Its AI Assistant and third-party plugins bring chat and completion; some plugins accept OpenAI-compatible endpoints for bringing your own backend.

The exact menu paths shift between releases, so treat the names above as a map, not turn-by-turn directions — the principle that matters is that all three let you, in some form, swap in a custom backend instead of accepting the built-in one.

Bring Your Own Models: The OpenAI-Compatible Trick

Here's the single most useful thing to understand about AI editors in 2026: nearly every AI extension and several editors themselves speak the OpenAI Chat Completions wire format. That format has become the lingua franca. So if you can point an extension at a custom base URL and give it an API key, you can route it to almost any model — not just the one the vendor ships.

This is where a unified aggregator earns its place. Vincony exposes one OpenAI-compatible endpoint that reaches 800+ models behind a single key, so you set the base URL once and then choose models by name. Instead of juggling an OpenAI key, an Anthropic key, and a Gemini key across three different settings panes, you bring one key into your editor and switch models with a string. The full contract is in the developer API docs, and if you already hold provider contracts you can bring your own keys so traffic runs on your existing accounts behind the same interface.

A Realistic Setup Walkthrough

Most AI extensions ask for two things: a base URL and an API key, plus a model name. Set them once. The pattern below is what those settings look like as JSON — the same three values map onto the fields in a Cursor, VS Code extension, or JetBrains plugin settings page.

{
  // Point any OpenAI-compatible AI extension at the unified endpoint.
  "ai.baseUrl": "https://vincony.com/api/v1",
  "ai.apiKey": "YOUR_VINCONY_KEY",

  // Pick a model per task by name — no new account or key per provider.
  "ai.models": {
    "completion": "codestral",      // fast + cheap for inline ghost-text
    "chat":       "claude-opus-4.5", // strong reasoning for the side panel
    "agent":      "gpt-5"            // reliable multi-step instruction following
  }
}

Prefer to confirm the wiring works before touching editor settings? Hit the same endpoint from a one-line CLI. If it returns a completion, your editor will too — it's the identical contract.

curl https://vincony.com/api/v1/chat/completions \
  -H "Authorization: Bearer $VINCONY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4.5",
    "messages": [
      { "role": "user", "content": "Write a TypeScript debounce with cleanup." }
    ]
  }'

Don't want to maintain a model-per-task table at all? Send everything to the Smart Model Router as the model name and let it analyze each request and pick the best model for quality, speed, and cost automatically — you get model choice without the lookup table.

Cost Control & Choosing Models Per Task

Inline completion is where bills quietly explode, because it fires on almost every keystroke pause. The fix is matching model to mode: a cheap, fast model for completion and a premium model reserved for chat and agent work. Routing completion to a frontier model is like flying a fighter jet to the corner shop — you pay for capability you don't use.

• • Completion — a small, fast coding model. Latency matters more than depth here; you'll accept or reject in under a second.
• • Chat / refactor — a strong reasoning model. You're asking it to understand a selection and propose real changes; the per-call cost is worth it because the calls are infrequent.
• • Agent runs — a model that reliably follows multi-step plans. Cap iterations and review diffs; agent loops can burn tokens fast when they thrash.

A unified key with credit-based usage makes this trivial to act on: cheap autocomplete and a heavy agent run draw from the same balance, so you can shift spend across modes without managing three separate bills. Before committing to an implementation, it's worth running the same prompt through a couple of models and comparing — the approach in our multi-model code review guide.

Privacy Considerations

Whatever your editor's AI touches, it's sending fragments of your code somewhere. Three habits keep that in check. First, know your editor's indexing scope — many AI features embed your repo to provide context, so confirm what's uploaded and exclude secrets, env files, and customer data via the editor's ignore settings. Second, prefer providers and routes with a clear no-training-on-your-datastance for code. Third, for the most sensitive work, the BYOK route lets traffic flow through accounts you control under your own data agreements, and self-hosted open models keep code entirely on-prem. Routing through a single layer also means one place to audit and rotate keys rather than secrets scattered across editor settings on every machine.

FAQ

The takeaway: your editor is already AI-capable — the upgrade in 2026 isn't switching tools, it's un-locking the model behind them. Point your IDE at one unified endpoint, read the developer API guide, or start free and route your next keystroke through any model you want.