Custom LLM Provider

Connect any OpenAI-compatible AI endpoint to GitHub Copilot Chat in Visual Studio Code.
Works out of the box with Alibaba DashScope (Qwen), MiniMax, OpenRouter, Ollama, LiteLLM, vLLM, and any other API that speaks the OpenAI /v1/chat/completions format — including self-hosted models.

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🆕 What's New in v0.5.4

• Thinking / reasoning effort control — a new customLlm.thinkingEffort setting (auto | off | low | medium | high, default auto) controls how deeply a model reasons before answering. Add "thinkingEffort": "<value>" to a customLlm.models entry for a per-model override.
  • Qwen / DashScope (qwen*) → enable_thinking + thinking_budget (1 024 / 8 192 / 32 768 tokens for low / medium / high)
  • OpenAI o-series, DeepSeek-V4, GPT-5 → reasoning_effort: "low" | "medium" | "high"
  • All other providers → silently ignored

See the changelog for complete release notes.

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🆕 What's New in v0.5.3

• Hide models from the picker — set "hidden": true on an entry in customLlm.models. Unlike deleting it, which the next refresh undoes, a hidden model stays hidden.
• Non-chat models filtered automatically — text-to-speech, image, video, embedding and rerank models are hidden on first discovery when the endpoint reports a mode (LiteLLM). Your own choice always wins on later refreshes.

See the changelog for complete release notes.

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🆕 What's New in v0.5.2

• maxOutputTokens finally works — requests were capped at 8192 no matter what the setting said, which left reasoning models returning empty content. The configured value is now sent as-is.
• Token limits read correctly from proxies — max_output_tokens from LiteLLM's /model/info is picked up, and namespaced IDs such as tensorix/z-ai/glm-5.2 match the built-in limits table instead of silently falling back to 8192.
• Refreshes no longer reset hand-tuned limits — a value you set by hand survives model discovery.
• Clearer empty-response diagnostics — the warning reports the max_tokens actually sent instead of the configured value.

See the changelog for complete release notes.

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🆕 What's New in v0.5.1

• Tool-result images now reach multimodal models — screenshots and other images returned by tools are forwarded in a compatible follow-up message instead of being dropped.
• Mixed tool results are preserved — textual and JSON data returned alongside tool calls are decoded and sent to the model.
• More accurate vision errors — unrelated 400 responses are no longer misreported as missing image support.
• Per-model vision control — set "imageInput": false on a text-only model; LiteLLM's supports_vision metadata is detected automatically.
• Better diagnostics — request logs show whether image content was sent.

See the changelog for complete release notes.

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🎯 Primary Use Case — Alibaba Cloud Coding Plan

This extension was developed primarily to bring Alibaba Cloud Coding Plan into Visual Studio Code.

Alibaba's Coding Plan feature in Model Studio lets you run powerful Qwen Coder models in full agent mode — editing files, running tests, searching your codebase — all from within GitHub Copilot Chat. This extension bridges the gap by exposing those models directly in the VS Code model picker.

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✨ Features

• Models appear directly in the Copilot Chat model picker — no extra setup
• @qwen chat participant (opt-in) — type @qwen in any chat turn to route just that message through your custom model
• Multi-provider support — connect Alibaba DashScope, MiniMax, OpenRouter, and any other provider simultaneously, each with its own URL and API key
• Secure API key storage — keys are kept in VS Code's encrypted SecretStorage instead of provider settings
• Dynamic model discovery — models are fetched automatically from each provider's /v1/models (or /model/info for LiteLLM-compatible endpoints) on startup
• Stable provider IDs — providers are identified by a human-readable slug (e.g. alibaba-dashscope), so renaming or changing a provider's URL never breaks the model list
• Image input support — attach images directly in Copilot Chat (requires a multimodal model such as qwen-vl-max)
• Tool-result image support — screenshots and other images returned by tools are forwarded to multimodal models
• Full streaming support (Server-Sent Events)
• Tool calling support — agent mode, /fix, /edit, @workspace all work
• Automatic retry with exponential backoff for network failures and rate limits
• Hot-reload on settings change — no restart needed
• Zero runtime dependencies

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🚀 Quick Start

1. Add your first provider

Open the Command Palette (Ctrl+Shift+P) and run Custom LLM: Add provider.

The wizard will ask for:

1. Provider name — e.g. Alibaba DashScope
2. Base URL — e.g. https://coding-intl.dashscope.aliyuncs.com/v1
3. API key — your sk-… key

After saving, the extension automatically fetches available models from the provider.

The API key is stored in VS Code's encrypted secret storage, not in your user or workspace settings.json.

Get your API key from Alibaba Cloud Model Studio → API Keys section.
Note that Coding Plan API keys are separate from regular DashScope keys.

2. Pick a model in Copilot Chat

Open Copilot Chat (Ctrl+Alt+I) → click the model name → your models appear under Custom LLM.

First time only: Open Ctrl+Shift+P → Chat: Manage Language Models → hover over each model → click the eye icon 👁 to enable it in the picker.

3. Use the @qwen participant (optional)

Type @qwen at the start of a message to route that single turn through your custom model — regardless of which model is selected in the picker.

@qwen explain the auth flow in this codebase
@qwen qwen3-coder-plus refactor this function

Per-turn, not sticky: since v0.4.5 the participant is not sticky — you have to type @qwen every time you want it. Without @qwen, the message goes to whatever model you picked in the model picker (including original GitHub Copilot models like GPT-4 or Claude). This prevents the participant from accidentally hijacking native Copilot turns. If you want to always use your custom model, select it in the picker instead.

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🌐 Supported Endpoints

Provider	Base URL
Alibaba DashScope (international / Coding Plan)	https://coding-intl.dashscope.aliyuncs.com/v1
Alibaba DashScope (standard)	https://dashscope.aliyuncs.com/compatible-mode/v1
MiniMax ✅	https://api.minimaxi.chat/v1
OpenRouter	https://openrouter.ai/api/v1
Ollama (local)	http://localhost:11434/v1
LiteLLM proxy	http://localhost:4000/v1
vLLM	http://localhost:8000/v1
Any OpenAI-compatible API	custom URL

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🔌 Multi-Provider Support

You can connect multiple providers at once — for example, use Alibaba DashScope and OpenRouter side by side. Each provider has its own URL and API key; models from all providers are merged into a single list in the Copilot Chat picker.

Add a provider:

Ctrl+Shift+P → Custom LLM: Add provider

Manage providers (edit / remove):

Ctrl+Shift+P → Custom LLM: Manage providers

Refresh the model list:

Ctrl+Shift+P → Custom LLM: Refresh model list from API

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⚙️ Settings

Provider configuration

Providers are stored in the customLlm.providers array. Each entry has the following fields:

Field	Description
id	Auto-generated slug used as a stable internal identifier (e.g. "alibaba-dashscope"). Set automatically — you don't need to write this by hand.
name	Display name shown in the UI and info messages (e.g. "Alibaba DashScope")
baseUrl	Base URL ending with /v1

The id slug is derived from the provider name when you add it via the wizard. It stays stable even if you later rename the provider or change its URL — so the model list never gets orphaned, and the API key stays attached to the right provider.

You can also edit settings directly in User Settings JSON (Ctrl+Shift+P → Open User Settings (JSON)):

"customLlm.providers": [
  {
    "id": "alibaba-dashscope",
    "name": "Alibaba DashScope",
    "baseUrl": "https://coding-intl.dashscope.aliyuncs.com/v1"
  },
  {
    "id": "minimax",
    "name": "MiniMax",
    "baseUrl": "https://api.minimaxi.chat/v1"
  },
  {
    "id": "openrouter",
    "name": "OpenRouter",
    "baseUrl": "https://openrouter.ai/api/v1"
  }
]

🔐 API keys (v0.5.0+)

Starting with v0.5.0, API keys are kept out of settings.json during normal operation. Keys are stored through VS Code's SecretStorage API, which VS Code documents as encrypted. On desktop, VS Code uses Electron's safeStorage; the underlying implementation varies by platform.

Each key is stored under the provider's stable id, so renaming a provider or changing its endpoint keeps the key attached to the correct provider.

SecretStorage is global to this extension on the current machine, not scoped to a workspace. Provider configurations that use the same id therefore share the same stored key.

Set or change a key through the UI:

Ctrl+Shift+P → Custom LLM: Manage providers → <provider> → Edit API key

Leaving the input empty removes the stored key. Removing a provider deletes its key too.

Do not add an apiKey property to customLlm.providers manually. Because keys live outside settings, they are not exposed through user or workspace settings.json. VS Code does not sync SecretStorage across machines, so enter each key once per machine.

Upgrading to v0.5.0

On startup, the extension scans user, workspace, and workspace-folder configurations. Any non-empty apiKey found in customLlm.providers is stored in SecretStorage, then the apiKey property is removed from that settings scope. The same cleanup runs again if customLlm.providers is edited later. A notification reports how many keys were moved.

Existing provider entries without an id are upgraded automatically. Legacy customLlm.baseUrl / customLlm.apiKey settings (pre-v0.4.0) and the old providerUrl model field are migrated as well.

A key that has already leaked into a committed settings.json should be rotated at the provider — migration removes the value locally, but cannot un-publish it.

Model list

customLlm.models is auto-populated by model discovery and does not normally need to be edited manually. The extension merges discovered models with any existing entries — custom entries are preserved.

Field	Description
id	Model identifier sent to the provider
name	Display name shown in the model picker
providerId	Stable provider slug referenced by the model
maxInputTokens	Maximum input context reported to VS Code
maxOutputTokens	Output budget — reported to VS Code and sent as max_tokens on every request
imageInput	Optional vision override; use false for a text-only model
hidden	Optional; true keeps the model out of the Copilot model picker

When imageInput is omitted, the model is treated as image-capable because the standard OpenAI-compatible /models response does not advertise vision support. LiteLLM-compatible /model/info responses can set this automatically through supports_vision.

Hiding models from the picker

Provider catalogues list more than chat models — text-to-speech, image and video generation, embeddings, rerankers. They cannot serve a chat request, so they only add noise to the model picker.

Set "hidden": true on any entry to keep it out of the picker:

{
  "id": "qwen-tts",
  "name": "Qwen Tts",
  "providerId": "alibaba-dashscope",
  "maxInputTokens": 131072,
  "maxOutputTokens": 8192,
  "hidden": true
}

Hide rather than delete: a deleted entry is added back by the next model refresh, a hidden one stays hidden. Hiding only affects the picker — the entry stays in your settings, so a chat already using that model keeps working, and "hidden": false brings it straight back.

Endpoints that report a model's mode — LiteLLM's /model/info — get this for free: models whose mode is not chat or completion, and deployments the proxy marks as blocked, are hidden the first time they are discovered. Only the first time: once an entry exists, the flag is yours, so unhiding one is never undone by a refresh. Plain OpenAI-compatible /models responses (Alibaba DashScope among them) carry no mode at all, so nothing is hidden automatically there — set the flag by hand.

Output budget and reasoning models

maxOutputTokens is sent verbatim as max_tokens. Reasoning models (GLM, Qwen thinking modes, DeepSeek-R1, QwQ …) spend that budget on their chain of thought before writing any answer, so a budget that is too small produces a reply with no content at all. If View → Output → Custom LLM shows

⚠️  EMPTY CONTENT — model produced 27615 chars of reasoning but 0 chars of content.

raise maxOutputTokens for that model; 32768 or more suits most reasoning models. The request log line shows the value actually sent as max_tokens=….

Discovery fills the field in from the endpoint when it reports one — max_output_tokens or max_tokens from LiteLLM's /model/info, max_completion_tokens or context_length from /models. When the endpoint reports nothing, a value you set by hand is kept as-is across refreshes; only models the extension has never seen a limit for fall back to a built-in guess.

Editing over Remote SSH: customLlm.models is a user setting, so it lives on whichever machine runs the extension host. In a Remote SSH window that is the remote host — edit it there, not in your local settings.json.

Default models

If no providers are configured and the model list is empty, the extension adds these built-in defaults (all available in Alibaba Cloud Coding Plan):

Model ID	Display Name	Provider	Context
qwen3-coder-plus	Qwen3 Coder Plus	Alibaba DashScope	128K
qwen3-coder-next	Qwen3 Coder Next	Alibaba DashScope	128K
qwen3-max-2026-01-23	Qwen3 Max	Alibaba DashScope	128K
qwen3.5-plus	Qwen3.5 Plus	Alibaba DashScope	1M
qwen3.6-plus	Qwen3.6 Plus	Alibaba DashScope	1M
glm-5	GLM-5	Zhipu	200K
glm-4.7	GLM-4.7	Zhipu	128K
kimi-k2.5	Kimi K2.5	Moonshot	256K
MiniMax-M2.5	MiniMax M2.5	MiniMax ✅	256K

MiniMax models (MiniMax-M2.5 and others) are fully supported — connect via https://api.minimaxi.chat/v1 with your MiniMax API key.

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🛠️ Commands

Command	Description
Custom LLM: Add provider	Guided wizard to add a new provider (name → URL → API key → auto-discover models)
Custom LLM: Manage providers	List, edit, or remove configured providers
Custom LLM: Refresh model list from API	Manually re-fetch models from all configured providers
Custom LLM: Test connection	Send a test request to each configured provider and report the result

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📋 Requirements

• Visual Studio Code 1.119.0 or later
• GitHub Copilot extension installed and signed in (individual plan)
• An API key if your chosen provider requires authentication

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🔁 Retry Behavior

The extension automatically retries failed requests with exponential backoff:

Retry	Delay	Triggered by
1st	~1 second	Rate limit (429), Server errors (5xx)
2nd	~2 seconds	Same as above
3rd	~4 seconds	Same as above

Maximum delay capped at 10 seconds. Request cancellation is never retried.

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⚠️ Known Limitations

• GitHub Copilot Coding Plan (GitHub's native multi-step agent mode) is tied to GitHub's own infrastructure and cannot use custom providers. Use Alibaba Cloud Coding Plan as a powerful alternative.
• Inline completions (ghost text) are provided by GitHub Copilot and cannot be redirected
• Models only appear in the picker on individual GitHub Copilot plans (not Business/Enterprise)

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🛠️ Troubleshooting

Models lose their provider after changing a provider's URL (pre-v0.5.0)

Fixed in v0.5.0. Providers now have a stable slug id (e.g. "alibaba-dashscope") that models reference instead of the raw endpoint URL. Changing a provider's URL no longer orphans its models. Existing configs are migrated automatically on first launch.

"Add Models" → "Custom LLM" shows no input dialogs (v0.4.8)

Fixed in v0.4.8. In earlier versions, clicking Add Models → Custom LLM in the Copilot model picker opened the management command, but the name / URL / API key dialogs immediately disappeared without any input. The root cause was that VS Code 1.104+ keeps the model-picker webview in focus when it invokes the managementCommand, causing any synchronous showInputBox call to be dismissed as soon as the webview stole focus back. The fix adds a short settle-time so the picker panel fully closes before the first input dialog opens.

Custom model can't be invoked — Copilot loops on gpt-4o-mini requests (v0.4.7)

Fixed in v0.4.7. If a custom model appeared to do nothing and VS Code kept sending auxiliary gpt-4o-mini (*.copilotmd) requests in a loop, the extension was inadvertently writing model IDs into Copilot's BYOK settings (github.copilot.chat.customOAIModels). When Copilot found the same ID in both registrations, it routed via BYOK, found no API key there, and silently looped. The fix removes that secondary write entirely. The extension also cleans up stale BYOK entries left by earlier versions on startup.

Original Copilot models reply "no input given" / context fills to ~95% in a loop

Fixed in v0.4.5. In earlier versions, the @qwen participant was sticky, so once you used it, every subsequent message was silently routed through the participant — even when you switched the picker back to a built-in Copilot model (GPT-4, Claude, etc.). On agent-mode turns where the actual content was in references/tool results rather than plain prompt text, the participant forwarded an empty user message, the model replied "what do you need?", and the loop repeated until the context filled.

If you see this on 0.4.4 or earlier, update to 0.4.5+. As a workaround on older versions, fully clear the @qwen mention from the prompt and reload the chat.

Agent mode loops / repeating the same actions

If the model keeps calling the same tool in a loop, switch to a larger variant — smaller models sometimes struggle with complex multi-step tool orchestration. Try qwen3-coder-plus or qwen3-max instead of lighter models.

Models don't appear in the picker

1. Open Ctrl+Shift+P → Chat: Manage Language Models
2. Hover over each model in the Custom LLM section
3. Click the eye icon 👁 to make it visible in the picker

If the section doesn't appear at all, check that the extension is active: Ctrl+Shift+P → Extensions: Show Installed Extensions and verify Custom LLM Provider is enabled.

To force a model refresh: Ctrl+Shift+P → Custom LLM: Refresh model list from API.

401 Unauthorized — "invalid access token or token expired"

Your API key is missing or incorrect. Fix:

1. Open Ctrl+Shift+P → Custom LLM: Manage providers
2. Select your provider → Edit API key
3. Paste your API key (starts with sk-)
4. Make sure there are no extra spaces around the key

Get your key from Alibaba Cloud Model Studio → API Keys section.

Requests fail with 404 or empty responses

Check that the baseUrl ends with /v1 and the model id values match exactly what your provider expects. For DashScope, use https://coding-intl.dashscope.aliyuncs.com/v1.

Settings changes not taking effect

The extension hot-reloads on settings change, but it may take a few seconds. If models still don't update, reload the VS Code window: Ctrl+Shift+P → Developer: Reload Window.

Inspect request diagnostics

Open View → Output and select Custom LLM. Each request includes the selected model, timing, streamed content and tool-call counts, finish reason, and whether images were included. Token usage is logged as structured JSON when the provider reports it.

Image attachment returns an error

Not all models support image input. If you see "This model does not support image input", switch to a multimodal model. For Alibaba DashScope, qwen-vl-max supports vision. Coding-focused models (qwen3-coder-*, qwen3.6-plus, etc.) are text-only.

Because an OpenAI-compatible endpoint doesn't generally advertise vision support, every model is offered to VS Code as image-capable unless the endpoint says otherwise (LiteLLM's /model/info reports supports_vision). To stop VS Code sending images to a model you know is text-only, set "imageInput": false on its entry in customLlm.models — the flag survives model refreshes.

The model says it cannot see a screenshot from a tool

Images returned by tools (#browser/screenshotPage and similar) are forwarded to the model as a separate message right after the tool result, because the OpenAI chat schema only accepts plain text in a tool message. If the model still reports it cannot see the image, check View → Output → Custom LLM: the request line shows images=yes when image content was actually sent. If it shows images=no, VS Code never handed the image to the extension — verify the model's imageInput flag isn't set to false.

Migrating from v0.4.x or earlier

Update to v0.5.0 and reload VS Code. Keys in v0.4.x customLlm.providers entries are moved to secret storage automatically. The older single-provider customLlm.baseUrl and customLlm.apiKey settings are also migrated, with the key stored securely.

If a migrated key is missing, do not put it back into settings.json. Run Custom LLM: Manage providers, select the provider, and choose Edit API key. You must also repeat this on each additional machine because VS Code does not sync secret storage.

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🔨 Building from Source

# Clone the repository
git clone https://github.com/milhaus123/vscode-custom-llm-provider.git
cd vscode-custom-llm-provider

# Install the locked dependency versions
npm ci

# Compile TypeScript
npm run compile

# Package as .vsix
npx vsce package

# Install locally
code --install-extension vscode-custom-llm-provider-*.vsix

Maintainers can publish the current version with npx vsce publish, or increment and publish it in one step with npx vsce publish patch.

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💖 Support the Project

If this extension saves you time, consider buying me a coffee!

Your support helps keep the project maintained and updated with new model releases. 🙏

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📄 License

MIT © 2026 Martin Říha