What exact API endpoints and parameters test multimodal image plus voice inputs

Here are the detailed insights into exact endpoints, parameters, and usage patterns for testing multimodal image plus voice inputs based on latest available documentation and developer guides:

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Unified Multimodal Endpoint and Its Structure

One common approach is using a single endpoint like:

POST /api/v1/chat/completions

or a similar chat completions endpoint depending on the provider. This endpoint accepts a `messages` parameter which is an array of message objects where each message may contain different content types and modalities.

Each message in the array can specify content in various modalities by setting the `type` property alongside relevant modality data. For example:

- For images: `type: "image_url"` or `type: "image_base64"`
- For audio: `type: "input_audio"` with audio URL or encoded audio data
- For text: `type: "text"` with text content

By combining these message objects, you can send both voice and image inputs in one API call.

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How to Specify Image Inputs

Images can be sent either via URLs that point to image files or by embedding the image directly as base64-encoded data. Common structure for image input in the messages array:

json
{
  "role": "user",
  "content": [
    {
      "type": "image_url",
      "image_url": {
        "url": "https://example.com/image.jpg"
      }
    }
  ]
}

Or for base64 embedded image:

json
{
  "role": "user",
  "content": [
    {
      "type": "image_base64",
      "image_base64": {
        "data": "/9j/4AAQSkZJRgABAQAAAQABAAD...base64-encoded-data..."
      }
    }
  ]
}

Image inputs are typically accepted in common formats like JPEG, PNG, and sometimes others like BMP or GIF depending on the provider. Some platforms specify max image size limits in MB or resolution.

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How to Specify Voice (Audio) Inputs

Voice is generally provided as audio file inputs either via URLs or byte data. The typical content type for input audio is usually named `input_audio` or `audio` in different APIs.

Example format with audio URL:

json
{
  "role": "user",
  "content": [
    {
      "type": "input_audio",
      "input_audio": {
        "url": "https://example.com/audio_sample.mp3"
      }
    }
  ]
}

Alternatively, base64 embedded audio is also supported in some APIs:

json
{
  "role": "user",
  "content": [
    {
      "type": "audio_base64",
      "audio_base64": {
        "data": "base64-encoded-audio-data..."
      }
    }
  ]
}

Supported audio formats typically include mp3, wav, m4a, and sometimes ogg or flac. Audio sampling rates and size limits are usually imposed (e.g., max length in seconds or file size limits).

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Combining Image and Voice Input in One Request

To test multimodal input with both image and voice together, you combine the different modality objects inside the single `messages` array within one user role message. For example:

json
POST /api/v1/chat/completions

{
  "model": "multimodal-model-name",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Analyze this photo and transcribe the accompanying audio."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://example.com/sample.jpg"
          }
        },
        {
          "type": "input_audio",
          "input_audio": {
            "url": "https://example.com/sample.mp3"
          }
        }
      ]
    }
  ]
}

The model will then process both inputs — visually analyzing the image, as well as transcribing or understanding the audio clip — and generate a composite output.

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Additional Parameters and Options

Most APIs include additional parameters to customize behavior or improve accuracy of multimodal input processing:

- model: specifies the multimodal-capable model to use (e.g., `gpt-4o`, `meta-llama-4-scout`, or vendor-specific models).
- temperature: controls randomness in text output generation.
- max_tokens: limits the length of the text output.
- detail: for image analysis, can specify levels like `"high"` or `"low"` to influence depth of analysis.
- language: for audio transcription, specify the spoken language if known to improve accuracy.
- response_format: select the output format such as `text`, `json` with timestamps for audio.
- multiple images/audio: you can provide arrays of multiple image or audio inputs by adding more objects with corresponding types.

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Model and API Provider Variations

- OpenRouter supports `/api/v1/chat/completions` with message content types including `image_url` for images and `input_audio` for audio.
- OpenAI's GPT-4o model (not fully open yet) supports similar multi-inputs with images and voice via transit services.
- Google Gemini API also supports multimodal inputs in chat format, allowing images, audio, and text in combined requests with asynchronous or streaming responses.
- Groq API's vision-enabled multimodal models accept image URL or base64 images, with similar combined input payloads including audio.
- LiteLLM and others expose detailed options for speech transcription, audio prompt settings, and multiple image formats through SDKs or REST endpoints.

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Summary Example for Testing

To conduct a straightforward test of multimodal image plus voice input, here is an abstraction of the typical API call you can make:

1. POST Request to a chat completions endpoint.
2. Specify model capable of multimodal inputs.
3. Send `messages` array with one user message.
4. In that message's `content`, include an array with entries:

- Text prompt asking for combined analysis.
- Image input (via URL or base64 data).
- Audio input (via URL or base64 data).

5. Set any additional parameters you need (temperature, detail, max tokens).

The response will include combined understanding or transcription with image captioning, object recognition, or insights blended with audio transcription or voice understanding.

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Practical Considerations and Best Practices

- Preprocess images (resize, compress) to meet provider max file size and resolution limits (e.g., 4MB base64 image, 20MB URL image).
- Compress or trim audio samples to fit limits (e.g., max 30 seconds to 1 minute depending on API).
- Use clear and specific prompts in the text portion to direct the model on what to pay attention to in the image and audio.
- When testing multiple images or audio files, understand provider limit on max files per request (often around 5).
- Validate file URLs are publicly accessible on the internet or use appropriate authentication if needed.
- Check specific API's error codes for errors related to size, unsupported formats, or malformed requests.

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This detailed explanation covers the exact approach to test multimodal image plus voice input APIs: the unifying use of a chat completions endpoint with a structured messages array supporting diverse content types (image_url, input_audio, text), combined with configurable parameters for detailed processing on top of multiple leading AI multimodal platforms available by 2025.