> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evolink.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# EvoLink Auto - Smart Model Routing
> The system automatically selects the most suitable model to process the request
## Smart Model Routing
EvoLink Auto is an intelligent model routing feature that automatically selects a suitable AI model based on your request content, without manual model specification.
### Key Benefits
* **Smart Matching**: Automatically analyzes request content and selects a suitable model
* **Cost Optimization**: Prioritizes cost-effective models while maintaining quality
* **Load Balancing**: Automatically distributes requests across multiple models for improved stability
* **Transparency**: Returns the actual model name used in the response for tracking and optimization
### How It Works
The system selects the best-fit model from the model pool based on request complexity, length, and type.
### Supported Models
EvoLink Auto intelligently routes between mainstream AI models including GPT-4, GPT-3.5, Claude, Gemini, and more.
### Limitations
* Not suitable for scenarios requiring specific model capabilities (e.g., GPT-4 vision features)
* Does not guarantee the same model for every request
### Use Cases
Ideal for scenarios where you're unsure which model to use, or want the system to automatically optimize model selection.
Simply set the `model` parameter to `evolink/auto`, and the system will automatically select a suitable model for you.
**BaseURL**: The default BaseURL is `https://direct.evolink.ai`, which has better support for text models and long-lived connections. `https://api.evolink.ai` is the primary endpoint for multimodal services and serves as a fallback address for text models.
## OpenAPI
````yaml /en/api-manual/language-series/evolink-auto/evolink-auto-quickstart.json POST /v1/chat/completions
openapi: 3.1.0
info:
title: EvoLink Auto - Smart Model Selection
description: >-
Smart model routing feature that automatically selects the most suitable AI
model
license:
name: MIT
version: 1.0.0
servers:
- url: https://direct.evolink.ai
description: Production (recommended)
- url: https://api.evolink.ai
description: Alternative URL
security:
- bearerAuth: []
paths:
/v1/chat/completions:
post:
tags:
- Smart Routing
summary: Smart Model Routing
description: >-
The system automatically selects the most suitable model to process the
request
operationId: createChatCompletionAuto
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/AutoRequest'
responses:
'200':
description: Request successful
content:
application/json:
schema:
$ref: '#/components/schemas/ChatCompletionResponse'
'400':
description: Invalid request parameters
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 400
message: Invalid request parameters
type: invalid_request_error
'401':
description: Unauthenticated, invalid or expired token
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 401
message: Invalid or expired token
type: authentication_error
'402':
description: Insufficient quota, recharge required
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 402
message: Insufficient quota
type: insufficient_quota_error
fallback_suggestion: https://evolink.ai/dashboard/billing
'403':
description: Access denied
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 403
message: Access denied for this feature
type: permission_error
'429':
description: Rate limit exceeded
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 429
message: Rate limit exceeded
type: rate_limit_error
fallback_suggestion: retry after 60 seconds
'500':
description: Internal server error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 500
message: Internal server error
type: internal_server_error
fallback_suggestion: try again later
'502':
description: Upstream service error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 502
message: Upstream AI service unavailable
type: upstream_error
fallback_suggestion: try again later
'503':
description: Service temporarily unavailable
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
error:
code: 503
message: Service temporarily unavailable
type: service_unavailable_error
fallback_suggestion: retry after 30 seconds
components:
schemas:
AutoRequest:
type: object
required:
- model
- messages
properties:
model:
type: string
description: Use smart routing
enum:
- evolink/auto
default: evolink/auto
example: evolink/auto
messages:
type: array
description: List of conversation messages
items:
$ref: '#/components/schemas/MessageInput'
minItems: 1
example:
- role: user
content: Introduce the history of artificial intelligence
temperature:
type: number
description: |-
Sampling temperature, controls the randomness of the output
**Notes**:
- Lower values (e.g., 0.2): More deterministic and focused output
- Higher values (e.g., 1.5): More random and creative output
minimum: 0
maximum: 2
example: 0.7
top_p:
type: number
description: >-
Nucleus Sampling parameter
**Notes**:
- Controls sampling from the top tokens by cumulative probability
- For example, 0.9 means sampling from tokens whose cumulative
probability reaches 90%
- Default: 1.0 (considers all tokens)
**Recommendation**: Do not adjust both temperature and top_p
simultaneously
minimum: 0
maximum: 1
example: 0.9
top_k:
type: integer
description: >-
Top-K sampling parameter
**Notes**:
- For example, 10 means only the top 10 highest-probability tokens
are considered during each sampling step
- Smaller values make the output more focused
- No limit by default
minimum: 1
example: 40
stream:
type: boolean
description: |-
Whether to return the response in streaming mode
- `true`: Stream response, returning content in real-time chunks
- `false`: Wait for the complete response before returning
default: false
example: false
ChatCompletionResponse:
type: object
properties:
id:
type: string
description: Unique identifier for the chat completion
example: chatcmpl-20260308112637503180122ABCD1234
model:
type: string
description: The model actually used
example: gpt-5.4
object:
type: string
enum:
- chat.completion
description: Response type
example: chat.completion
created:
type: integer
description: Creation timestamp
example: 1741428397
choices:
type: array
description: List of generated choices
items:
$ref: '#/components/schemas/Choice'
usage:
$ref: '#/components/schemas/Usage'
ErrorResponse:
type: object
properties:
error:
type: object
properties:
code:
type: integer
description: HTTP status error code
message:
type: string
description: Error description
type:
type: string
description: Error type
param:
type: string
description: Related parameter name
fallback_suggestion:
type: string
description: Suggestion on error
MessageInput:
type: object
required:
- role
- content
properties:
role:
type: string
description: Message role
enum:
- system
- user
- assistant
content:
type: string
description: Message content
Choice:
type: object
properties:
index:
type: integer
description: Index of the choice
example: 0
message:
$ref: '#/components/schemas/AssistantMessage'
finish_reason:
type: string
description: Finish reason
enum:
- stop
- length
- content_filter
example: stop
Usage:
type: object
description: Token usage statistics
properties:
prompt_tokens:
type: integer
description: Number of tokens in the input
example: 15
completion_tokens:
type: integer
description: Number of tokens in the output
example: 120
total_tokens:
type: integer
description: Total number of tokens
example: 135
AssistantMessage:
type: object
properties:
role:
type: string
description: Role of the message sender
enum:
- assistant
example: assistant
content:
type: string
description: AI response message content
example: The history of artificial intelligence dates back to the 1950s...
securitySchemes:
bearerAuth:
type: http
scheme: bearer
description: >-
## All endpoints require Bearer Token authentication ##
**Get your API Key:**
Visit the [API Key Management Page](https://evolink.ai/dashboard/keys)
to get your API Key
**Add the following to your request headers:**
```
Authorization: Bearer YOUR_API_KEY
```
bearerFormat: sk-evo-xxxxxxxxxx
````