OpenAI vs Anthropic Function Calling: Complete Format Comparison & Migration Guide (2026)

Verdict: Both OpenAI and Anthropic offer robust function-calling capabilities, but their format implementations differ significantly. OpenAI uses a structured tools array with function declarations, while Anthropic employs a tools block with name, description, and input_schema. For developers seeking cost efficiency without sacrificing performance, HolySheep AI provides unified API access to both formats at ¥1=$1 with sub-50ms latency—a compelling alternative to direct official APIs.

Understanding Function Calling in AI Integrations

Function calling (also known as tool use) enables large language models to interact with external systems, execute code, query databases, and perform actions beyond text generation. This capability is essential for building production-grade AI applications, from customer service chatbots to automated trading systems.

OpenAI Function Calling Format

OpenAI's implementation uses a straightforward JSON-based approach with a tools array. Each tool contains a function object defining the callable interface.

# OpenAI Function Calling - HolySheep AI Compatible Endpoint
import requests
import json

Using HolySheep AI base URL (NOT api.openai.com)
url = "https://api.holysheep.ai/v1/chat/completions"
headers = {
    "Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "gpt-4.1",
    "messages": [
        {"role": "user", "content": "What's the weather in Tokyo?"}
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {
                            "type": "string",
                            "description": "City name"
                        },
                        "unit": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"]
                        }
                    },
                    "required": ["city"]
                }
            }
        }
    ],
    "tool_choice": "auto"
}

response = requests.post(url, headers=headers, json=payload)
print(json.dumps(response.json(), indent=2))

Key characteristics of OpenAI's format:

• Flat structure: Function definitions are nested within a function object
• JSON Schema: Parameters follow JSON Schema draft-07 specification
• Tool choice control: Optional tool_choice parameter allows forcing specific tool selection

Anthropic Function Calling Format

Anthropic (Claude) uses a slightly different structure with name, description, and input_schema as top-level properties within each tool block.

# Anthropic Function Calling - Via HolySheep AI Proxy
import requests
import json

url = "https://api.holysheep.ai/v1/messages"
headers = {
    "Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
    "Content-Type": "application/json",
    "x-api-key": "YOUR_HOLYSHEEP_API_KEY",
    "anthropic-version": "2023-06-01"
}

payload = {
    "model": "claude-sonnet-4.5",
    "max_tokens": 1024,
    "messages": [
        {"role": "user", "content": "Calculate compound interest for $10,000 at 5% for 10 years"}
    ],
    "tools": [
        {
            "name": "calculate_compound_interest",
            "description": "Calculate compound interest given principal, rate, and time",
            "input_schema": {
                "type": "object",
                "properties": {
                    "principal": {
                        "type": "number",
                        "description": "Initial principal amount in USD"
                    },
                    "annual_rate": {
                        "type": "number",
                        "description": "Annual interest rate as decimal (e.g., 0.05 for 5%)"
                    },
                    "years": {
                        "type": "integer",
                        "description": "Number of years for calculation"
                    },
                    "compound_frequency": {
                        "type": "string",
                        "enum": ["annually", "quarterly", "monthly", "daily"],
                        "default": "annually"
                    }
                },
                "required": ["principal", "annual_rate", "years"]
            }
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
print(json.dumps(response.json(), indent=2))

Key characteristics of Anthropic's format:

• Top-level properties: name, description, and input_schema are direct children
• Strict parameter typing: Anthropic enforces more rigorous type checking
• Streaming support: Different endpoint structure for streaming responses

Detailed Format Comparison Table