🖼️ 10000 Image (Medium)
⚡ 20% Cached
📊 Batch API
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✗ Not Available
📄
OCR Support
✓ Available
⚡
Caching
✗ Not Available
💰
Total Cost Calculation (from Plugin)
Base Cost (No Optimizations)
$0.200000
Input: $0.000000
Output: $0.000000
Optimized Cost
$0.100000
Input: $0.000000
Output: $0.000000
Unit: $0.100000
Fees: $0.000000
Total Savings
$0.100000
50.0% discount
Advanced Cost Breakdown (from Plugin)
📄 OCR Processing
$0.200000
100 pages
📊 Batch API
50.0% off
Asynchronous processing discount
Detailed Cost Analysis (from Plugin)
For 10,000,000 input tokens and 2,000 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.000010
- Tokens per dollar: 100,020,000 tokens
- Context Window: 65536 tokens
- Thinking Source: (0 tokens)
Speed & Performance Analysis
With a processing speed of 300 tokens per second and 200ms time to first token:
- Processing Time: 9 hours, 15 minutes, 40.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 300 tokens/second
Best Use Cases
High-throughput text extraction from PDFs and scanned documents where structured markdown output is the priority.
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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.
Optimizing Legacy Document Digitization
For technical teams managing large-scale legacy archives, Mistral OCR 3 represents a specialized tool designed specifically for document parsing. Unlike general-purpose vision models that might struggle with the nuances of varied layouts, this model excels at converting scanned PDFs into machine-readable markdown. It is built to handle the structural complexities of archives—such as headers, footers, and tables—without the overhead of unnecessary reasoning capabilities.
Why Choose a Dedicated OCR Model?
The primary advantage for high-volume archival tasks is efficiency. Mistral OCR 3 focuses exclusively on text extraction, minimizing the latency and computational cost associated with broader visual reasoning tasks. For document pipelines where the goal is simply to convert a high volume of images (10,000 scanned pages) into clean, structured text for downstream indexing or search, this specialization is a massive benefit. It reduces the likelihood of hallucinations or off-topic generation, providing a more consistent output for standard document types. However, users should note that this model is not designed for interpreting images beyond text extraction; if your archive includes charts, complex diagrams, or handwritten annotations that require visual analysis, you may find the outputs limited compared to a multimodal vision-language model. This choice is best suited for teams that need reliable, high-throughput text ingestion into a database or RAG pipeline at scale.
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.
