⚠️ 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)
$2.844375 (rounded ~ $2.84)
Input: $2.842500 (rounded ~ $2.84)
Output: $0.001875
Optimized Cost
$1.565250 (rounded ~ $1.57)
Input: $2.842500 (rounded ~ $2.84)
Output: $0.001875
Unit: $0.000000
Fees: $0.000000
Total Savings
$1.279125
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: $2.842500 (rounded ~ $2.84)
- Output Cost: $0.001875
- Total Cost: $1.565250 (rounded ~ $1.57)
- Cost per 1K tokens: $0.000103
- Tokens per dollar: 9,686,632 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: 12 hours, 38 minutes, 6.18 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 333 tokens/second (temperature-adjusted)
Best Use Cases
Ideal for high-volume document digitization where native vision-to-text reconstruction is required to replace traditionalerror-prone OCR pipelines.
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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.
Digitizing legacy archives at a scale of 10,000 scanned pages monthly presents significant infrastructure challenges. Gemini 3.8 Flash stands out in this domain due to its integrated multimodal capabilities. Unlike pipelines requiring separate OCR and LLM stages, this model can ingest raw image data directly to extract text and structure. For large-scale archive projects, the primary advantage here is the reduction in pipeline complexity. Teams can bypass traditional OCR engines, which often struggle with degraded legacy documents, and leverage the model’s native vision intelligence to reconstruct text from noise-heavy scans. This capability is particularly useful for organizations dealing with handwritten notes or non-standard document formats where traditional tools frequently produce artifacts. By centralizing the digitization and extraction process, engineering teams can streamline their data processing workflows and reduce the points of failure in their archival systems. When evaluating this model for high-volume monthly throughput, focus on how its latency profile fits into your batch processing schedule. The ability to handle diverse visual inputs directly makes it a strong contender for teams looking to consolidate their document processing architecture while maintaining high fidelity in the final digitized output.
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.
