Using VS Code

Compatibility: works with caveats. VS Code's chat has a built-in Custom Endpoint model provider, part of its "bring your own key" (BYOK) support. It can call any endpoint that speaks the Chat Completions API, which luv13 does. Set the API type to Chat Completions. The other two types, Responses and Messages, call endpoints luv13 doesn't serve. BYOK covers chat and utility tasks only. Inline suggestions, semantic search and embedding-based features still need GitHub Copilot.

Before you start

You need:

• VS Code. These steps follow the VS Code docs page "AI language models in VS Code", dated 9/30/2026. The latest stable VS Code release that day was 1.140.0.
• A luv13 API key. The Quickstart shows how to get one.
• The model id luv13/glm-5.3-flash, from the live list at https://api.luv13.ai/v1/models. See GLM-5.3 Flash for details on the model.
• Optional: a GitHub account. VS Code's docs say BYOK models work without a GitHub account or Copilot plan, but some features then need extra setup (see "Utility tasks" below).
• On Copilot Business or Enterprise: your admin must enable the Bring Your Own Language Model Key in VS Code policy on GitHub.com.

Facts about luv13 that affect this setup:

• luv13 serves one generation endpoint, POST /v1/chat/completions, plus GET /v1/models. /v1/responses, /v1/messages, /v1/completions and /v1/embeddings return 404 (checked 2026-09-30). See Endpoints.
• Every model costs a flat $0.33 per 1M tokens, input the same as output. See Pricing.
• The model list at https://api.luv13.ai/v1/models doesn't report context length, so luv13 publishes no per-model limits. See the model list.

Test your key and model first

Run these two checks in a terminal before you touch the tool. They take a few seconds and rule out key and model problems.

1. The model id exists. This call needs no key:

curl -s https://api.luv13.ai/v1/models

The list should include "id":"luv13/glm-5.3-flash".

2. Your key works for chat. Set the key in your shell first (export LUV13_API_KEY="your luv13 key"), then run:

curl -s https://api.luv13.ai/v1/chat/completions \
  -H "Authorization: Bearer $LUV13_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "luv13/glm-5.3-flash",
    "messages": [{"role": "user", "content": "Reply with the word ready."}],
    "max_tokens": 20
  }'

With a valid key you should get back a JSON chat completion whose choices[0].message.content holds the reply, plus a usage block. If you see {"error":{"code":401,"message":"unauthorized","type":"invalid_auth"}} instead, the key is missing or wrong, and no tool setting will fix that.

Set up the Custom Endpoint provider

1. Open the Chat view. In the model picker, select Manage Language Models (gear icon), or run Chat: Manage Language Models from the Command Palette.
2. Select Add Models, then choose Custom Endpoint from the list.
3. Enter a group name, such as luv13. This label groups the models in the model picker.
4. Enter a display name and your luv13 API key.
5. When asked for the API type, choose Chat Completions.
6. VS Code opens chatLanguageModels.json. Make the luv13 entry look like this, then save:

[
  {
    "name": "luv13",
    "vendor": "customendpoint",
    "apiKey": "${input:luv13ApiKey}",
    "apiType": "chat-completions",
    "models": [
      {
        "id": "luv13/glm-5.3-flash",
        "name": "GLM 5.3 Flash (luv13)",
        "url": "https://api.luv13.ai/v1/chat/completions",
        "toolCalling": true,
        "vision": false,
        "streaming": true
      }
    ]
  }
]

7. Pick GLM 5.3 Flash (luv13) in the chat model picker. If it doesn't appear, restart VS Code, as the docs suggest.

What each field does

About token limits

VS Code's reference lists maxInputTokens, maxOutputTokens and contextWindow to describe a model's context window. luv13 doesn't publish context lengths, so this page leaves them out rather than invent numbers. If your VS Code version refuses to save the model without them, enter conservative values you've tested, and treat them as your own estimate. See Context Window.

What works and what doesn't

Utility tasks

VS Code also uses small background models for titles, commit messages and similar tasks. With a Copilot account these default to GitHub's models. If you use luv13 without signing in to GitHub, VS Code shows a notice asking you to configure utility models. You can set chat.utilityModel and chat.utilitySmallModel to the luv13 model, or set chat.byokUtilityModelDefault to Main Agent Model. Either way, those background requests are billed by luv13 like any other tokens.

Another option: an extension

If you'd rather not use VS Code's built-in chat, the Continue and Cline extensions both run in VS Code and support OpenAI-compatible endpoints. See Using Continue and Using Cline.

Common errors

General tip: if agent requests fail but plain chat works, set "toolCalling": false to confirm that tool calls are the problem, then see Tool Calling on luv13.

Sources

• VS Code docs, "AI language models in VS Code" (page dated 9/30/2026, read 2026-09-30). Covers BYOK, the Custom Endpoint provider, its configuration reference, URL resolution and utility models.
• VS Code release list (latest stable 1.140.0 on 2026-09-30)
• luv13 endpoints and errors checked live with curl on 2026-09-30.

Related: Using Cursor, Base URL, OpenAI-Compatible APIs.