The short version: LongCat-2.0 — Meituan's 1.6-trillion-parameter, MIT-licensed open coder that spent two months quietly topping OpenRouter under the alias "Owl Alpha" — is now the cheapest near-frontier model to point a coding agent at. The wiring takes about ten minutes because the API is OpenAI-compatible and the OpenRouter model id is meituan/longcat-2.0. Below: confirm your key with curl, call it from Python, drop it into Cline for a real in-editor agent, and set the two knobs that control your bill. (For why you'd pick it over the alternatives, see LongCat-2.0 vs Kimi K3.)

0. Get a key (1 minute)#

Create an OpenRouter account, add a few dollars of credit, and generate an API key. Everything below assumes it's in your shell:

export OPENROUTER_API_KEY="sk-or-..."

That's the only credential you need. OpenRouter runs the hardware; you pay per token. You do not self-host — LongCat-2.0 is a trillion-parameter MoE model that needs a multi-GPU node, so the API is the realistic path for a team of one.

1. Confirm it works with one curl call (2 minutes)#

Before wiring anything into an editor, prove that your key and the model id actually work with a single raw request. This is the step that catches the two most common mistakes up front — a mistyped key, or the wrong model string — before they surface as a confusing error deep inside an editor extension. OpenRouter's endpoint is OpenAI-shaped, so this is a standard chat-completions call:

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meituan/longcat-2.0",
    "messages": [
      {"role": "user", "content": "Write a Python function that reverses a linked list. Code only."}
    ],
    "max_tokens": 400
  }'

If you get a JSON response with a choices[0].message.content full of code, you're done with the hard part. Note the max_tokens: 400 — we'll come back to why that number is a cost decision, not a formality.

2. Call it from Python with the OpenAI SDK (3 minutes)#

Because the API is OpenAI-compatible, you don't need an OpenRouter-specific library or any custom HTTP plumbing. You can point the standard OpenAI SDK — the very same one you would use for GPT — straight at OpenRouter's base URL. Then hand it your OpenRouter key and swap the model string, and the rest of the call stays exactly as you already know it:

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
)

resp = client.chat.completions.create(
    model="meituan/longcat-2.0",
    messages=[
        {"role": "system", "content": "You are a senior engineer. Return a unified diff, nothing else."},
        {"role": "user", "content": "Add input validation to the /login route in app.py."},
    ],
    max_tokens=2000,   # cap output — this is where the bill lives
)

print(resp.choices[0].message.content)

That's a complete, single-file agent step: system prompt, task, capped output. Wrap that call in your own loop (read files → ask for a diff → apply → test) and you have a bespoke coding agent on a near-frontier open model for cents.

3. Wire it into Cline for a real in-editor agent (3 minutes)#

If you'd rather not write the loop yourself, use an agent that already has one. Cline (and its fork Roo Code) are VS Code extensions that support OpenRouter natively — and both are among the coding agents founders actually reach for (we compared the field in Cline vs Roo Code vs Kilo Code).

  1. Install the Cline extension in VS Code.
  2. Open its settings and choose OpenRouter as the API provider.
  3. Paste your OpenRouter key.
  4. In the model dropdown, select meituan/longcat-2.0.

That's it — Cline will now plan, read your files, propose diffs, and run commands using LongCat-2.0 as the brain. Roo Code is identical to configure. Because you're paying LongCat-2.0's token price instead of a frontier closed model's, you can leave a longer agent run going without watching the meter.

4. The two settings that decide your bill#

Output tokens are the expensive half. Cap max_tokens and know which price tier you're on — those two knobs move your cost more than anything else.

max_tokens. LongCat-2.0 can emit up to about 262,144 output tokens. That's a feature for long refactors, but left uncapped it's a way to burn credit — and output is the pricier half of the bill. For ordinary coding turns, cap it to what an edit actually needs (a few thousand tokens). Only raise it when you're deliberately asking for a large, whole-file generation.

Which price you're on. At launch, OpenRouter listed LongCat-2.0 around $0.30 / $1.20 per million input/output tokens as a promotion (with free cached-input reads), reverting to roughly $0.75 / $2.95 afterward. Either way it's about 4–10× cheaper than Kimi K3 (~$2.90/$14). Check the live number on the model page before you standardize — launch-window pricing moves.

That's the whole loop#

Key → curl to confirm → Python for custom agents → Cline for in-editor work → two knobs for cost. You now have a near-frontier, MIT-licensed coding brain you can leave running in a loop for a fraction of frontier prices. The one discipline to keep: verify the current pricing and benchmark claims against Meituan's and OpenRouter's first-party pages before you hard-code anything — this model is new, and the launch-window numbers will settle.