OpenRouter Vision API: AI Text Generation

OpenRouter Vision API Reference

Endpoint

POST https://api.muapi.ai/api/v1/openrouter-vision

Submit a job with your MuApi API key in the x-api-key header, then poll https://api.muapi.ai/api/v1/predictions/{request_id}/result until status is completed.

Parameters

Name	Type	Required	Description
prompt	string	Yes	The prompt to generate the response
images_list	array	Yes	Upload or provide image urls. Used for image-to-video generation.
system_prompt	string	No	System prompt to provide context or instructions to the model.
model	string	No	Name of the model to use. Premium models are charged at 10x the rate of standard models, they include: deepseek/deepseek-r1, google/gemini-pro-1.5, openai/gpt-4.1, anthropic/claude-3-5-haiku, openai/gpt-4o, anthropic/claude-3.5-sonnet, openai/o3, meta-llama/llama-3.2-90b-vision-instruct, anthropic/claude-3.7-sonnet, openai/gpt-5-chat.Options: google/gemini-2.5-flash, anthropic/claude-sonnet-4.5, openai/gpt-4o, qwen/qwen3-vl-235b-a22b-instruct, x-ai/grok-4-fastDefault: "google/gemini-2.5-flash"
reasoning	boolean	No	Should reasoning be the part of the final answer.Default: false
temperature	int	No	This setting influences the variety in the model's responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input.Default: 1
max_tokens	int	No	This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.Default: null

cURL example

REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/openrouter-vision \
  -H "x-api-key: $MUAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"<prompt>","images_list":[]}' | jq -r .request_id)

curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY"

Python example

import os, time, requests

API = "https://api.muapi.ai/api/v1"
headers = {"x-api-key": os.environ["MUAPI_API_KEY"]}

r = requests.post(f"{API}/openrouter-vision", headers=headers, json={"prompt":"<prompt>","images_list":[]})
request_id = r.json()["request_id"]

while True:
    res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json()
    if res["status"] == "completed":
        print(res["outputs"]); break
    if res["status"] == "failed":
        raise RuntimeError(res.get("error"))
    time.sleep(3)

Full docs and agent/MCP integration: llms.txt. Get an API key at muapi.ai/access-keys.