OpenAI-compatible chat completions backed by Anthropic Claude Fable 5 — tuned for narrative and long-form writing, with adaptive thinking, image input, tool use, prompt caching and streaming. Set model to claude-fable-5. Note: this model deprecates temperature / top_p / top_k upstream; the gateway silently drops them (no error) and sampling runs at the model default. Use reasoning_effort (low / medium / high / xhigh / max) to control thinking depth instead.
📥 Context window (max input)：~200K tokens.
Try in Playground →Authentication
Authorization: Bearer YOUR_API_KEY Content-Type: application/json
Create an API Key in the console to start.
Request body
| Parameter | Type | Required | Description |
|---|---|---|---|
| model | string | Yes | Model ID, here claude-fable-5. |
| messages | array | Yes | Array of messages; each has role (system/user/assistant) and content. content may be a string, or an array of {type:text} and {type:image_url} parts for image understanding (multimodal/vision). |
| stream | boolean | No | Stream the response as SSE. Default false. |
| temperature | number | No | Not supported. This model deprecates sampling params; the gateway drops temperature / top_p / top_k silently (no error) and sampling runs at the model default. Use reasoning_effort to trade thinking depth for speed instead. |
| max_tokens | integer | No | Maximum number of tokens to generate. |
| web_search | boolean | No | Set true to enable web search: the gateway augments the prompt with live results (citing sources) before the model answers. Can also be triggered via a tools entry {"type":"web_search"}. |
| reasoning_effort | string | No | Thinking effort: low / medium / high / xhigh / max. Higher means deeper, slower and more expensive — thinking tokens bill at the output rate inside usage.completion_tokens. Omit it and the model decides adaptively. The gateway translates this into the model's native output_config.effort; on the native Anthropic endpoint you can send output_config yourself. The suffix form works too (e.g. claude-opus-5-high) and bills exactly as the base model. |
Request example
curl https://nezhagate.com/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model": "claude-fable-5", "messages": [{"role": "user", "content": "Hello"}], "stream": false}'Response
{
"id": "chatcmpl_xxx",
"object": "chat.completion",
"model": "claude-fable-5",
"choices": [
{"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}
],
"usage": {"prompt_tokens": 11, "completion_tokens": 7, "total_tokens": 18}
}Image input (Vision)
Put an image in the message content array and the model will analyze it (visual Q&A, reading text / OCR, …). image_url accepts a public image link or an inline base64 data URL (data:image/png;base64,...). Available on multimodal models (gpt-5.5, gemini series, …).
curl https://nezhagate.com/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model": "claude-fable-5", "messages": [{"role": "user", "content": [{"type": "text", "text": "这张图里有什么？"}, {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}]}]}'Error codes
| Code | Description |
|---|---|
| 401 | Invalid or missing API key |
| 402 | Insufficient balance / key limit reached |
| 400 | Invalid parameters or model |
| 429 | Rate limited |
| 502 | All upstream providers failed |
