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How to Call Grok and Claude with Standard OpenAI Format (cURL Examples)
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How to Call Grok and Claude with Standard OpenAI Format (cURL Examples)

January 26, 2026
8 min read

Why Standard API Format Matters

The AI landscape is fragmenting fast. xAI requires gRPC, Anthropic has its own Messages API, and OpenAI sets a different standard. For developers, this means maintaining separate integration code for each provider—until now.

The xAI Messages API Deprecation Problem

On February 20, 2026, xAI will remove the /v1/messages endpoint entirely. Any requests to this endpoint will return a 410 Gone error. The xAI team is pushing developers toward their gRPC-based Chat service or RESTful Responses API, which adds significant complexity to integration work.

This deprecation creates a critical problem: teams must either invest in learning gRPC implementation or face service disruption. Neither option is appealing when you simply want to call an LLM.

Different Providers, Different Authentication Methods

Each AI provider implements authentication differently:

  • xAI native API requires complex gRPC setup with specific credential handling
  • Anthropic uses x-api-key headers with version specifications
  • OpenAI uses Bearer token authentication
  • Other providers have their own variations

This fragmentation forces developers to maintain multiple authentication layers, increasing code complexity and maintenance burden.

The Case for API Unification

A unified API interface solves this problem by providing a single standard format—the OpenAI chat completions format—that works across all major LLM providers. This approach delivers immediate benefits:

  • Write once, use everywhere
  • Swap models with a single parameter change
  • Reduce integration time from days to minutes
  • Simplify testing and deployment

Understanding the xAI Grok Challenge

Native xAI API Complexity

The xAI native implementation requires developers to work with gRPC, a high-performance RPC framework that uses Protocol Buffers. While gRPC offers performance benefits, it introduces significant overhead:

  • Learning curve for gRPC concepts and tooling
  • Additional dependencies for Protocol Buffer compilation
  • More complex error handling and debugging
  • Platform-specific client generation requirements

For most applications that simply need to call Grok for text generation, this complexity is unnecessary.

Messages Endpoint Sunset (Feb 20, 2026)

The official xAI deprecation notice states clearly:

"The Messages endpoint (/v1/messages) will be removed from the xAI API on February 20, 2026. After that date, any requests sent to /v1/messages will return a 410 Gone error."

Developers using the Messages endpoint have limited time to migrate. The official recommendation is to move to gRPC Chat or the RESTful Responses API, both of which require substantial code changes.

Migration to gRPC vs. Standard REST

The official xAI migration path involves:

  • Installing gRPC runtime libraries for your language
  • Downloading and compiling .proto definition files
  • Implementing streaming message handlers
  • Managing connection lifecycle and credentials

This migration can take weeks for a development team to implement and test properly.

The Wisdom Gate Solution

Wisdom Gate provides an OpenAI-compatible gateway that abstracts away provider-specific complexity. Instead of learning each provider's unique API, you use a single, familiar interface.

OpenAI-Compatible Interface

The Wisdom Gate API implements the OpenAI chat completions specification exactly. If you have existing code that calls OpenAI, you can redirect it to Wisdom Gate with just two changes:

Everything else stays the same: request structure, response format, error handling, and authentication.

Unified Authentication

Authentication is simple and consistent across all models:

Authorization: YOUR_API_KEY

No Bearer prefix, no x-api-key headers, no version negotiation. One header, one format, every time.

Multi-Model Support

Wisdom Gate currently supports:

  • xAI Grok models (grok-4, grok-2, etc.)
  • Anthropic Claude models (claude-sonnet-4, claude-opus-4, etc.)
  • OpenAI models (gpt-4, gpt-4-turbo, etc.)
  • Other major providers

Check the full model list at https://wisdom-gate.juheapi.com/models

Calling Grok via Standard OpenAI Format

Base URL and Authentication

Set up your request with these fundamentals:

Complete cURL Example

Here is a complete working example for calling Grok-4:

curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--header 'Host: wisdom-gate.juheapi.com' \
--header 'Connection: keep-alive' \
--data-raw '{
    "model":"grok-4",
    "messages": [
      {
        "role": "user",
        "content": "Hello, how can you help me today?"
      }
    ]
}'

This request:

  • Uses standard REST POST method
  • Requires no gRPC libraries or Protocol Buffers
  • Works from any HTTP client (curl, Postman, browser fetch, etc.)
  • Returns standard JSON response

Response Structure

The response follows OpenAI format:

{
  "id": "chatcmpl-xyz123",
  "object": "chat.completion",
  "created": 1738000000,
  "model": "grok-4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm here to help you with..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 45,
    "total_tokens": 57
  }
}

Parse this response exactly as you would an OpenAI response. No special handling required.

Calling Claude Sonnet 4 the Same Way

Using Identical Request Structure

To call Claude Sonnet 4 instead of Grok, change only the model parameter:

curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--header 'Host: wisdom-gate.juheapi.com' \
--header 'Connection: keep-alive' \
--data-raw '{
    "model":"claude-sonnet-4-5",
    "messages": [
      {
        "role": "user",
        "content": "Hello, how can you help me today?"
      }
    ]
}'

Notice that everything except the model field is identical. The same headers, same endpoint, same authentication, same message structure.

Model Parameter Differences

The only difference between calling different providers is the model string:

  • For xAI Grok: "model":"grok-4"
  • For Claude Sonnet: "model":"claude-sonnet-4"
  • For GPT-4: "model":"gpt-4"

This makes A/B testing trivial. You can test the same prompt across multiple models by changing a single parameter.

Side-by-Side Comparison

Traditional approach (different code per provider):

  • xAI: gRPC client with Protocol Buffers
  • Claude: Anthropic SDK with Messages API
  • OpenAI: OpenAI SDK with Chat Completions

Wisdom Gate approach (one code path):

  • xAI: Standard HTTP POST with model="grok-4"
  • Claude: Standard HTTP POST with model="claude-sonnet-4"
  • OpenAI: Standard HTTP POST with model="gpt-4"

The unified approach eliminates hundreds of lines of provider-specific code.

Practical Benefits for Developers

Single Codebase for Multiple Models

With a unified interface, your LLM integration code becomes model-agnostic:

function callLLM(model, messages) {
  return fetch('https://wisdom-gate.juheapi.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': process.env.WISDOM_GATE_API_KEY,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({ model, messages })
  }).then(r => r.json());
}

// Call any model with the same function
await callLLM('grok-4', messages);
await callLLM('claude-sonnet-4', messages);
await callLLM('gpt-4', messages);

This pattern works in any language: Python, Go, Java, Ruby, PHP, or any platform with HTTP support.

No Vendor Lock-in

Switching between providers becomes a configuration change rather than a code refactor. If xAI pricing changes or Claude releases a better model, you can switch instantly without touching your integration code.

Your code depends on the OpenAI standard interface, not any specific provider. This is true API portability.

Simplified Error Handling

All providers return errors in the same format:

{
  "error": {
    "message": "Invalid API key",
    "type": "invalid_request_error",
    "code": "invalid_api_key"
  }
}

You write error handling logic once and it works for all providers. No need to learn provider-specific error codes or response structures.

Migration Best Practices

Testing Your Integration

Before migrating production traffic:

  1. Test with a simple prompt to verify authentication
  2. Compare responses between direct provider API and Wisdom Gate
  3. Test error scenarios (invalid key, rate limits, malformed requests)
  4. Measure latency to ensure performance meets requirements
  5. Test streaming responses if your application uses them

Start with development or staging environments before touching production.

Error Handling Tips

Implement retry logic with exponential backoff:

  • Retry on 5xx server errors (temporary failures)
  • Retry on 429 rate limit errors after waiting
  • Do not retry on 4xx client errors (fix the request instead)
  • Set maximum retry attempts to avoid infinite loops

Log the full request and response for debugging. The standard format makes logs easy to read and troubleshoot.

Performance Considerations

Wisdom Gate adds minimal latency overhead (typically under 50ms) compared to calling provider APIs directly. For most applications, this is negligible compared to model inference time.

Benefits often outweigh the small latency cost:

  • Automatic failover if a provider has an outage
  • Request/response logging without custom code
  • Unified rate limiting and quota management
  • Simplified monitoring across multiple models

For latency-critical applications, test in your specific environment to measure actual impact.

Conclusion

The xAI Messages API deprecation highlights a broader problem: provider-specific APIs create unnecessary complexity. By adopting the OpenAI standard format through Wisdom Gate, you can:

  • Call Grok and Claude with identical code
  • Avoid gRPC complexity for simple use cases
  • Switch models with a single parameter change
  • Future-proof your integration against API changes

The examples in this guide show exactly how to implement this pattern with cURL. The same principles apply to any programming language or framework.

Get started by testing the cURL examples above with your API key, then adapt the pattern to your production codebase. Your future self will thank you when the next API deprecation notice arrives.