🖼️ 10000 Image (Medium)
⚡ 50% Cached
📊 Batch API
🔧 Tools
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✗ Not Available
requested
📄
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
Choose Mistral OCR 3 for high-fidelitystructured document parsing (tablesforms). Choose Gemini 3.6 Flash for OCR tasks that require immediate reasoning or complex multimodal analysis.
Want this applied to YOUR actual stack?
This calculator shows the math for Mistral OCR 3. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
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⚠️ Bulk Calculation: Total volume exceeds single-request limit of 1,048,576 tokens. Budgeting mode active.
🖼️ 10000 Image (Medium)
⚡ 50% 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)
$5.688750 (rounded ~ $5.69)
Input: $5.685000 (rounded ~ $5.69)
Output: $0.003750
Optimized Cost
$3.130500
Input: $5.685000 (rounded ~ $5.69)
Output: $0.003750
Unit: $0.000000
Fees: $0.000000
Total Savings
$2.558250 (rounded ~ $2.56)
45.0% discount
Advanced Cost Breakdown (from Plugin)
🖼️ Multimodal Input
$0.000000
5,160,000 tokens
📊 Batch API
50.0% off
Asynchronous processing discount
Multimodal Input Details
🖼️ Images
Count: 10000
Resolution: Medium
Tokens: 5,160,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 10,000,000 input tokens and 2,000 output tokens:
- Input Cost: $5.685000 (rounded ~ $5.69)
- Output Cost: $0.003750
- Total Cost: $3.130500
- Cost per 1K tokens: $0.000206
- Tokens per dollar: 4,843,316 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 304 tokens per second and 120ms time to first token:
- Processing Time: 14 hours, 49 minutes, 26.43 seconds
- Latency: 120 milliseconds to first token
- Base Throughput: 304 tokens/second
- Effective Throughput: 284 tokens/second (temperature-adjusted)
Best Use Cases
Choose Mistral OCR 3 for high-fidelitystructured document parsing (tablesforms). Choose Gemini 3.6 Flash for OCR tasks that require immediate reasoning or complex multimodal analysis.
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.6 Flash. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →
🔍
No Alternatives Found
No other models in the registry support all your current input parameters.
Try adjusting some parameters to see more options.
Remove Images
Remove Video
Remove Audio
Remove OCR
Remove Tools
✨ 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.
Choosing the Right OCR Engine for Legacy Digitization
For startup teams managing 10,000 pages per month, the choice between a specialized OCR model and a general-purpose multimodal powerhouse determines both your processing quality and your workflow complexity. Mistral OCR 3 is built explicitly for document understanding. It excels at parsing complex table structures, forms, and messy handwriting that often break traditional document processing pipelines. For archives that require structural fidelity—such as extracting financial tables into clean markdown or JSON—Mistral OCR 3 provides a specialized architecture that minimizes the need for downstream cleanup.
Conversely, Gemini 3.6 Flash offers a broader set of capabilities for scenarios where digitization is only the first step. If your pipeline requires immediate reasoning over the extracted text—such as summarizing historical documents, categorizing content by sentiment, or performing entity extraction alongside the OCR task—Gemini 3.6 Flash functions as a versatile agent. Its multimodal reasoning allows it to interpret visual context that pure OCR models might ignore, such as interpreting handwritten marginalia or analyzing the visual relationship between graphical elements on the page.
Decision Factors:
- Specialization: Mistral OCR 3 is superior for pure, high-fidelity text and table extraction from dense or damaged legacy documents.
- Reasoning: Gemini 3.6 Flash is the better fit if your workflow integrates OCR directly with agentic tasks or downstream document analysis.
- Scalability: Both models support high-volume batch APIs, essential for avoiding rate limits during large archive ingestion projects.
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.
