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Kontext Pro T2I API Pricing

Flux Kontext Pro T2I offers fast and reliable generation with creative flexibility. It supports stylized prompts, character design, and fantasy themes while maintaining clear subject coherence.

Savings Alert25% ↓Cheaper than Fal.ai

About Kontext Pro T2I

Flux Kontext Pro T2I is a cutting-edge text-to-image generation model designed to provide fast and reliable outputs with exceptional creative flexibility. Built with advanced AI techniques, it excels at understanding and translating complex text prompts into vivid visual representations. The model's support for stylized prompts, character design, and fantasy themes ensures that every generated image is both evocative and coherent, making it an ideal choice for creative professionals and hobbyists alike. Leveraging state-of-the-art machine learning algorithms, Flux Kontext Pro T2I maintains clear subject coherence even when working with intricate narratives. Its impressive versatility allows users to explore a wide range of artistic styles and themes while enjoying efficient and cost-effective performance. With a rate of just $0.03 per generation, it provides a powerful yet affordable solution for all your text-to-image needs.

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Monthly API Generations10,000 runs
50025,00050,00075,000100,000+
MuAPI Monthly Cost

$300.00

$0.03 per generation
Fal.ai Cost

$400.00

$0.04 per generation
Estimated Monthly Savings$100.00
Annual Savings$1200.00

Detailed Pricing Breakdown

ProviderEstimated RateNotes
muapiapp$0.03 per generationmuapiapp offers the most affordable pricing, being 20-50% cheaper than its competitors while delivering comparable or superior quality.
Fal.ai$0.04 per generationFal.ai is slightly more expensive with a cost of $0.04 per generation. muapiapp remains 20-50% more cost-effective, providing excellent value without sacrificing quality.
Replicate$0.04 per generationReplicate matches Fal.ai at $0.04 per generation. In comparison, muapiapp is 20-50% cheaper, ensuring users get high-quality outputs at a significantly lower price.

Developer Integration Snippets

1import requests 2import time 3import json 4 5api_key = "YOUR_API_KEY" 6url = "https://api.muapi.ai/workflow/run/flux-kontext-pro-t2i" 7 8def poll_for_result(run_id): 9 url = f"https://api.muapi.ai/workflow/run/{run_id}/api-outputs" 10 headers = { 11 "Content-Type": "application/json", 12 "x-api-key": f"{api_key}", 13 } 14 while True: 15 response = requests.get(url, headers=headers) 16 result = response.json() 17 18 if result['status'] == 'completed': 19 return json.loads(result['outputs']) 20 21 if result['status'] == 'failed': 22 raise Exception(result.get('error', 'Generation failed')) 23 24 time.sleep(5) 25 26data = { 27 "schemas": { 28 "input_data": { 29 "x-order-properties": [ 30 "prompt", 31 "aspect_ratio" 32 ], 33 "type": "object", 34 "properties": { 35 "prompt": { 36 "examples": [ 37 "A steampunk owl with mechanical wings, perched on a glowing gear, cinematic lighting." 38 ], 39 "description": "Text prompt describing the image.", 40 "type": "string", 41 "title": "Prompt", 42 "name": "prompt" 43 }, 44 "aspect_ratio": { 45 "enum": [ 46 "16:9", 47 "9:16", 48 "1:1", 49 "4:3", 50 "3:4", 51 "21:9", 52 "16:21" 53 ], 54 "title": "Aspect Ratio", 55 "name": "aspect_ratio", 56 "type": "string", 57 "description": "Aspect ratio of the output image.", 58 "default": "1:1" 59 } 60 }, 61 "title": "BaseInput", 62 "required": [ 63 "prompt" 64 ], 65 "endpoint_url": "flux-kontext-pro-t2i" 66 }, 67 "output_data": { 68 "x-fal-order-properties": [ 69 "images" 70 ], 71 "type": "object", 72 "properties": { 73 "video": { 74 "examples": [ 75 "https://d3adwkbyhxyrtq.cloudfront.net/muapi/homepage/flux-kontext-pro-t2i.avif" 76 ], 77 "description": "The generated images", 78 "title": "Image", 79 "comparison": false 80 } 81 }, 82 "title": "FluxKontextPro", 83 "required": [ 84 "images" 85 ] 86 } 87 } 88} 89 90headers = { 91 "Content-Type": "application/json", 92 "x-api-key": f"{api_key}", 93} 94 95response = requests.post(url, json=data, headers=headers) 96 97result = response.json() 98print("Queued:", result) 99 100outputs = poll_for_result(result["run_id"]) 101print("Completed:", outputs) 102

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