⚠️ Bulk Calculation: Total volume exceeds single-request limit of 1,000,000 tokens. Budgeting mode active.
🖼️ 1000 Image (Medium)
⚡ 20% Cached
📊 Batch API
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✓ Available
🎥
Video Analysis
✓ Available
📄
OCR Support
✓ Available
⚡
Caching
✓ Available
90% savings
💰
Total Cost Calculation (from Plugin)
Base Cost (No Optimizations)
$0.657438 (rounded ~ $0.66)
Input: $0.657250 (rounded ~ $0.66)
Output: $0.000188
Optimized Cost
$0.539133
Input: $0.657250 (rounded ~ $0.66)
Output: $0.000188
Unit: $0.000000
Fees: $0.000000
Total Savings
$0.118305 (rounded ~ $0.12)
18.0% discount
Advanced Cost Breakdown (from Plugin)
🖼️ Multimodal Input
$0.000000
516,000 tokens
📊 Batch API
50.0% off
Asynchronous processing discount
Multimodal Input Details
🖼️ Images
Count: 1000
Resolution: Medium
Tokens: 516,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 10,000,000 input tokens and 500 output tokens:
- Input Cost: $0.657250 (rounded ~ $0.66)
- Output Cost: $0.000188
- Total Cost: $0.539133
- Cost per 1K tokens: $0.000051
- Tokens per dollar: 19,506,337 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 1,000 tokens per second and 80ms time to first token:
- Processing Time: 2 hours, 58 minutes, 47.01 seconds
- Latency: 80 milliseconds to first token
- Base Throughput: 1,000 tokens/second
- Effective Throughput: 980 tokens/second (temperature-adjusted)
Best Use Cases
High-volumerepetitive structured data extraction from invoices where throughput and cost-efficiency are prioritized.
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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 translation agency owners processing high volumes of financial documentation, the transition from legacy OCR to vision-capable language models is a major efficiency unlock. Gemini 3.1 Flash Lite has emerged as a primary contender for this specific workflow, particularly for teams needing to bridge the gap between traditional document scanning and structured data extraction.
In invoice processing, the bottleneck is rarely the raw extraction of text but rather the reliable identification of key-value pairs—such as invoice numbers, tax IDs, and line-item totals—across varied document layouts. Gemini 3.1 Flash Lite offers a unique balance of speed and instruction fidelity that makes it well-suited for repetitive, high-throughput tasks. Unlike heavier models that might introduce unnecessary latency, this model is engineered for consistency in structured output, a critical requirement when feeding data directly into downstream accounting or translation management systems.
For agencies handling 1,000 invoice PDFs, the primary decision factor is the model’s ability to maintain high compliance with JSON schema requirements without needing complex prompt engineering. Because it is optimized for high-volume, cost-sensitive traffic, it allows teams to scale their document ingestion pipelines significantly without the prohibitive costs associated with frontier reasoning models. When the task is well-defined—extracting specific data fields from a standardized or semi-standardized set of invoices—Flash Lite often delivers the optimal return on investment by prioritizing throughput and accuracy over deep, multi-step logical reasoning.
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 are image tokens calculated?
Images are tokenized based on resolution: Low: 85 tokens, Medium: 170 tokens, High: 255 tokens, Full: 765 tokens per image. Some models (like Llama 4 Maverick) use tile-based encoding with 1,610 tokens/image (standard) or 8,050 tokens/image (high-res).
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.
