Continue.dev ar Claude, GPT un Gemini

Pievienojiet vienu models ierakstu Continue failā config.yaml ar sniedzēju openai un avots base URL, un VS Code vai JetBrains asistents darbojas uz avots.

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Connect in three fields

Continue nolasa modeļus no config.yaml. Pievienojiet openai sniedzēju, kas norāda uz avots.

FieldValue
Base URLhttps://api.avots.ai/openai/v1
API keyav_mcp_… · get your key
Modelanthropic/claude-sonnet-4.6 or any id from the table below

Setup

1. Atveriet config.yaml

Atveriet ~/.continue/config.yaml, pašreizējo Continue konfigurācijas formātu. Windows tas ir .continue mape jūsu lietotāja profilā.

2. Pievienojiet openai modeli

Pievienojiet ierakstu zem models ar sniedzēju openai, apiBase iestatītu uz avots base URL, savu av_mcp_ atslēgu un modeļa id.

3. Pārlādējiet un izvēlieties modeli

Saglabājiet, pārlādējiet Continue un izvēlieties jauno modeli asistenta nolaižamajā izvēlnē.

~/.continue/config.yaml
models:
  - name: Claude Sonnet (avots)
    provider: openai
    model: anthropic/claude-sonnet-4.6
    apiBase: https://api.avots.ai/openai/v1
    apiKey: av_mcp_YOUR_KEY

provider jābūt openai (vispārīgais OpenAI-compatible adapteris), nevis anthropic, citādi Continue izmanto vietējo Anthropic API. Saglabājiet /v1 uz apiBase. config.yaml aizstāj veco config.json; ja parādās straumēšanas vai Responses-API kļūdas, pievienojiet useResponsesApi: false.

Example request

curl
curl https://api.avots.ai/openai/v1/chat/completions \
  -H "Authorization: Bearer av_mcp_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-4.6",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Model and MCP tools in one config

Continue keeps the chat provider and MCP servers in the same file, so you paste both at once and get Claude, GPT and Gemini plus the avots creative tools (image, video, audio, talking avatars) on one av_mcp_ key. You save it yourself, nothing is added automatically.

~/.continue/config.yaml, model + MCP tools
models:
  - name: Claude Sonnet (avots)
    provider: openai
    model: anthropic/claude-sonnet-4.6
    apiBase: https://api.avots.ai/openai/v1
    apiKey: av_mcp_YOUR_KEY

mcpServers:
  - name: avots
    type: streamable-http
    url: https://mcp.avots.ai/
    requestOptions:
      headers:
        Authorization: Bearer av_mcp_YOUR_KEY

MCP setup guide →

Models and prices

Set the model field to any id below, or a lazy name like claude / gpt / gemini that maps to a sensible default.

ModelBest for⚡ / 1M tokens (in / out)
openai/gpt-5.5-proMax intelligence, hardest tasks60 000 / 360 000
anthropic/claude-opus-5Top Claude, long reasoning10 000 / 50 000
anthropic/claude-sonnet-4.6Balanced (default)6 000 / 30 000
google/gemini-3.1-pro-previewFresh Gemini Pro4 000 / 24 000
google/gemini-2.5-proStable Gemini Pro2 500 / 20 000
anthropic/claude-haiku-4.5Fast Claude2 000 / 10 000
deepseek/deepseek-r1Cheap reasoning1 400 / 5 000
google/gemini-2.5-flashFast and cheap600 / 5 000
openai/gpt-5-miniCheap OpenAI500 / 4 000
openai/gpt-oss-120b:freeFree (OpenAI open model, needs a positive balance)Free

* Price in ⚡ per 1,000,000 tokens, input / output (1000⚡ ≈ $1). Free models need a positive balance. Full list via GET /openai/v1/models.

Continue.dev ar Claude, GPT un Gemini

Generate an av_mcp_ key in Settings, paste three fields, and reach 40+ models on one balance. The same key works for MCP.

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FAQ

Jautājumi

config.yaml vai config.json?
config.yaml ir pašreizējais formāts un aizēno jebkuru veco config.json. Ievietojiet avots modeli sarakstā models.
Kāpēc sniedzējs openai priekš Claude?
Tāpēc ka avots ir OpenAI-compatible. Sniedzējs openai izmanto /chat/completions ceļu; sniedzējs anthropic apietu avots.
Vai gan VS Code, gan JetBrains?
Jā. Continue koplieto to pašu config.yaml abās IDE.
Kā par to maksā?
Par katru pieprasījumu no jūsu avots bilances.
Does it support function calling and tools?
Yes. The endpoint forwards the standard OpenAI tools, tool_choice and parallel_tool_calls fields (and the legacy functions/function_call), in both streaming and non-streaming mode, so agent frameworks like LangChain, LlamaIndex, Langflow and n8n get real tool_calls back. Whether a given request uses tools depends on the model you pick: Claude, GPT and Gemini all support function calling.

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