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Overview

This endpoint provides native Anthropic Messages API compatibility. Use this for Claude models with features like extended thinking.
Base URL for Anthropic SDK: https://api.lemondata.cc (no /v1 suffix)

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Request Headers

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x-api-key
string
required
Your LemonData API key. Alternative to Bearer token.
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anthropic-version
string
required
Anthropic API version. Use 2023-06-01.

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Request Body

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model
string
required
Claude model ID (e.g., claude-sonnet-4-6 or claude-opus-4-6).
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messages
array
required
Array of message objects with role and content.For Claude models with vision support, content can be either a plain string or an array of content blocks. To send images, use structured content blocks rather than placing image URLs or Base64 strings directly into plain text.Example content blocks:
  • text block: { "type": "text", "text": "Describe this image" }
  • image block via URL: { "type": "image", "source": { "type": "url", "url": "https://example.com/image.jpg" } }
  • image block via Base64: { "type": "image", "source": { "type": "base64", "media_type": "image/png", "data": "iVBORw0KGgoAAA..." } }
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max_tokens
integer
required
Maximum tokens to generate.
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system
string
System prompt (separate from messages array).
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temperature
number
default:"1"
Sampling temperature (0-1).
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stream
boolean
default:"false"
Enable streaming responses.
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thinking
object
Extended thinking configuration (Claude Opus 4.5).
  • type (string): "enabled" to enable
  • budget_tokens (integer): Token budget for thinking
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tools
array
Available tools for the model.
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tool_choice
object
How the model should use tools. Options: auto, any, tool (specific tool).
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top_p
number
Nucleus sampling parameter. Use either temperature or top_p, not both.
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top_k
integer
Only sample from the top K options for each token.
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stop_sequences
array
Custom stop sequences that will cause the model to stop generating.
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metadata
object
Metadata to attach to the request for tracking purposes.

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Response

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id
string
Unique message identifier.
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type
string
Always message.
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role
string
Always assistant.
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content
array
Array of content blocks (text, thinking, tool_use).
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model
string
Model used.
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stop_reason
string
Why generation stopped (end_turn, max_tokens, tool_use).
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usage
object
Token usage with input_tokens and output_tokens.
curl -X POST "https://api.lemondata.cc/v1/messages" \
  -H "x-api-key: sk-your-api-key" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "system": "You are a helpful assistant.",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

{
  "id": "msg_abc123",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Hello! How can I help you today?"
    }
  ],
  "model": "claude-sonnet-4-6",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 15,
    "output_tokens": 10
  }
}

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Vision Input Example

For Claude models with vision support, place images inside messages[].content as structured image blocks. 
{
  "model": "claude-sonnet-4-6",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Please describe this image."
        },
        {
          "type": "image",
          "source": {
            "type": "url",
            "url": "https://example.com/demo.jpg"
          }
        }
      ]
    }
  ]
}

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Please describe this image."
        },
        {
          "type": "image",
          "source": {
            "type": "base64",
            "media_type": "image/jpeg",
            "data": "/9j/4AAQSkZJRgABAQ..."
          }
        }
      ]
    }
  ]
}

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Extended Thinking Example

message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[{"role": "user", "content": "Solve this math problem..."}]
)

for block in message.content:
    if block.type == "thinking":
        print(f"Thinking: {block.thinking}")
    elif block.type == "text":
        print(f"Response: {block.text}")

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Anthropic Message Batches

LemonData now exposes the native Anthropic Message Batches flow alongside /v1/messages. Available routes:
  • POST /v1/messages/batches
  • GET /v1/messages/batches
  • GET /v1/messages/batches/:message_batch_id
  • GET /v1/messages/batches/:message_batch_id/results
  • POST /v1/messages/batches/:message_batch_id/cancel
  • DELETE /v1/messages/batches/:message_batch_id
Operational notes:
  • Use the same LemonData API key plus Anthropic-native headers.
  • If batch items reference file_id, also include anthropic-beta: files-api-2025-04-14.
  • Batch jobs keep Anthropic-native request/response shapes while LemonData tracks their internal settlement lifecycle.