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Gemini 3.5 Flash (OpenAI) API Pricing

Gemini 3.5 Flash (OpenAI-compatible) is a high-speed, multimodal language model built for real-time text generation, supporting text and image inputs natively. Token-based pricing: $0.60/M input tokens and $3.60/M output tokens. Two endpoints: standard async (/gemini-3-5-flash-openai) and live streaming (/gemini-3-5-flash-openai/stream) via SSE.

Savings Alert-700% ↓Cheaper than Google (official)

About Gemini 3.5 Flash (OpenAI)

Gemini 3.5 Flash (OpenAI-compatible) is a high-speed, multimodal language model optimized for rapid text generation and real-time image understanding, accessed via an OpenAI-compatible API interface. Token-based pricing: $0.60/M input tokens and $3.60/M output tokens.

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

$6000.00

$0.60/M input, $3.60/M output tokens
Google (official) Cost

$750.00

$0.075/M input, $0.30/M output tokens (under 128k context)
Estimated Monthly Savings$-5250.00
Annual Savings$-63000.00

Detailed Pricing Breakdown

ProviderEstimated RateNotes
muapiapp$0.60/M input, $3.60/M output tokensFast, high-quality, token-based pricing with an upfront minimum of $0.0001.
Google (official)$0.075/M input, $0.30/M output tokens (under 128k context)Official API pricing. We scale our rates to match standard Gemini rates with preserved developer margin.

Developer Integration Snippets

1import requests 2import time 3import json 4 5api_key = "YOUR_API_KEY" 6url = "https://api.muapi.ai/workflow/run/gemini-3-5-flash-openai" 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 "image_url", 32 "system_prompt" 33 ], 34 "type": "object", 35 "properties": { 36 "prompt": { 37 "type": "string", 38 "title": "Prompt", 39 "name": "prompt", 40 "description": "The user message or instruction for the model.", 41 "examples": [ 42 "Summarize the key points of the attached image." 43 ] 44 }, 45 "image_url": { 46 "type": "string", 47 "title": "Image URL", 48 "name": "image_url", 49 "description": "Optional image URL to include as multimodal input.", 50 "field": "image", 51 "examples": [] 52 }, 53 "system_prompt": { 54 "type": "string", 55 "title": "System Prompt", 56 "name": "system_prompt", 57 "description": "Optional system-level instruction to guide model behavior.", 58 "examples": [ 59 "You are a helpful assistant that responds concisely." 60 ] 61 } 62 }, 63 "title": "Text", 64 "required": [ 65 "prompt" 66 ], 67 "endpoint_url": "gemini-3-5-flash-openai" 68 }, 69 "output_data": { 70 "type": "object", 71 "properties": { 72 "text": { 73 "type": "string", 74 "title": "Text", 75 "description": "Generated text response from the model.", 76 "examples": [ 77 "The image shows a mountain landscape at sunset." 78 ] 79 } 80 }, 81 "title": "Gemini35FlashOpenAIOutput", 82 "required": [ 83 "text" 84 ] 85 } 86 } 87} 88 89headers = { 90 "Content-Type": "application/json", 91 "x-api-key": f"{api_key}", 92} 93 94response = requests.post(url, json=data, headers=headers) 95 96result = response.json() 97print("Queued:", result) 98 99outputs = poll_for_result(result["run_id"]) 100print("Completed:", outputs) 101

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