⚠️ Bulk Calculation: Total volume exceeds single-request limit of 1,048,576 tokens. Budgeting mode active.
🎧 18000min Audio
⚡ 20% Cached
📊 Batch API
🔧 Tools
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✓ Available
🎥
Video Analysis
✓ Available
📄
OCR Support
✗ Not Available
⚡
Caching
✓ Available
90% savings
💰
Total Cost Calculation (from Plugin)
Base Cost (No Optimizations)
$9.855938 (rounded ~ $9.86)
Input: $9.855000 (rounded ~ $9.86)
Output: $0.000938
Optimized Cost
$8.082038 (rounded ~ $8.08)
Input: $9.855000 (rounded ~ $9.86)
Output: $0.000938
Unit: $0.000000
Fees: $0.000000
Total Savings
$1.773900 (rounded ~ $1.77)
18.0% discount
Advanced Cost Breakdown (from Plugin)
🖼️ Multimodal Input
$0.000000
34,560,000 tokens
📊 Batch API
50.0% off
Asynchronous processing discount
Multimodal Input Details
🎧 Audio
Duration: 18000 minutes
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 18,000,000 input tokens and 1,000 output tokens:
- Input Cost: $9.855000 (rounded ~ $9.86)
- Output Cost: $0.000938
- Total Cost: $8.082038 (rounded ~ $8.08)
- Cost per 1K tokens: $0.000154
- Tokens per dollar: 6,503,434 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 340 tokens per second and 105ms time to first token:
- Processing Time: 43 hours, 48 minutes, 3.18 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 333 tokens/second (temperature-adjusted)
Best Use Cases
Ideal for mobile apps needing direct audio-to-text-to-note generation with minimal latency and reduced pipeline complexity.
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No Alternatives Found
No other models in the registry support all your current input parameters.
Try adjusting some parameters to see more options.
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✨ How recommendations work (v8.6.0): We scan all active models in the registry and only include those that support ALL your current inputs. For token-based models, we check if they can handle your token counts. For special pricing models (OCR, video, audio), we verify they have the correct pricing structure. Features marked requested were in your inputs but not supported by that model. Now using official provider pricing without reseller markups.
For developers building mobile applications to assist healthcare professionals, the transition from spoken consultation to structured clinical documentation is a high-stakes workflow. Gemini 3.8 Flash represents a significant opportunity for mobile teams because of its native audio-processing capabilities. By allowing developers to feed raw audio directly into the model, the infrastructure overhead of third-party transcription services is removed, simplifying the app architecture and reducing latency in the note generation pipeline.
In clinical settings, where physicians require immediate summaries following patient interactions, speed is as critical as accuracy. This model provides an ideal balance of fast inference and robust instruction following, which is necessary to transform natural conversation into standardized SOAP notes or EMR-ready formats. Because it handles the multi-modal input natively, you can maintain tighter control over data flow, ensuring that patient audio is processed efficiently without being fragmented across different APIs.
For mid-market SaaS platforms managing hundreds of hours of consultations, the efficiency of this model allows for scalable deployment without the exponential costs associated with heavier, general-purpose reasoning models. When optimizing for a mobile-first user experience, the ability to process variable-length audio inputs directly allows developers to create more responsive, reliable features that fit into a busy clinician’s workflow. The key for implementation here is focusing on consistent system prompting to handle clinical terminology and medical coding accuracy, ensuring the model output meets the stringent requirements of healthcare data standards.
Frequently Asked Questions
How accurate are these AI model cost calculations?
Our calculations are based on official pricing from each provider (Google, OpenAI, Anthropic, Meta, xAI, Perplexity, DeepSeek, Mistral) and are updated regularly.
We account for all factors including multimodal inputs, caching discounts, batch API pricing, tool usage multipliers, OCR processing, audio minutes, silence fees, and research mode pricing.
Note: Reseller markups and dedicated instance multipliers have been removed to reflect official provider pricing.
How does audio billing work?
Audio models are billed by token, not by minute. Voxtral Small 24B costs $0.10 per 1M input tokens and $0.30 per 1M output tokens, matching Mistral Small 3. GPT Realtime Mini uses standard token billing. There are no silence keep-alive surcharges or per-minute duration fees on either provider.
How does prompt caching work?
Caching discounts vary by provider: Google and OpenAI offer 90% discounts on cached input tokens. Anthropic uses write (1.25x) and read (0.10x) multipliers. Savings are applied to the token portion only, not unit-based fees.
How do Market Recommendations work (v8.6.0)?
Our recommendation engine scans the entire model registry and only includes models that support ALL your current input parameters (tokens, images, video, audio, OCR, tools, batch API, etc.). It calculates exact costs with your settings and sorts by price, showing you the best value options that can handle your complete workflow. Special pricing models (OCR, video, audio, image generation) are properly handled and only appear when their specific input types are requested. v8.6.0 removes reseller markups (20% buffer) and dedicated instance multipliers to reflect official provider pricing.
What is the YemHub AI Calculator Tool?
The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 50+ AI models. It calculates token-based pricing, analyzes multimodal processing, accounts for state-dependent pricing (context cliffs, tiered tunnels), provides optimization recommendations, and now offers intelligent market matching to find the best alternatives for your specific needs.
