Mistral OCR 3 Mistral AI
💰 Total Cost Calculation (from Plugin)
Output: $0.000000
Output: $0.000000
Unit: $0.100000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 500 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.000100
- Tokens per dollar: 10,005,000 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: 55 minutes, 35.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 300 tokens/second
Best Use Cases
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← Back to Mistral OCR 3| Rank | AI Model & Provider | Total Cost | vs Mistral OCR 3 |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.379153 Best Value | ↑ 279.2% more |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.947569 (rounded ~ $0.95) | ↑ 847.6% more |
| 🥉 |
Gemini 3.6 Flash
Google
|
$1.895138 (rounded ~ $1.90) | ↑ 1795.1% more |
| #4 |
Gemini 2.5 Pro
Google
|
$6.317750 (rounded ~ $6.32) | ↑ 6217.8% more |
| #5 |
GPT-5.4
OpenAI
|
$12.633625 (rounded ~ $12.63) | ↑ 12533.6% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$12.633625 (rounded ~ $12.63) | ↑ 12533.6% more |
| #7 |
GPT-6 Astra
OpenAI
|
$50.537000 (rounded ~ $50.54) | ↑ 50437% more |
| #8 |
GPT-6 Astra
OpenAI
|
$50.537000 (rounded ~ $50.54) | ↑ 50437% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Mistral OCR 3 is designed for organizations moving beyond simple text extraction to true document intelligence. For a workload of 10,000 scanned pages per month, this model excels at preserving complex layouts, multi-level tables, and dense forms that typically break legacy OCR engines. Unlike traditional solutions that output raw, unstructured text, Mistral OCR 3 provides clean, machine-readable data, significantly reducing the downstream effort required for RAG pipelines or enterprise search integration. It is particularly effective for archives where handwriting overlays or damaged document structures are common. By focusing on structural integrity, this model allows teams to treat their document repositories as active knowledge bases rather than passive image storage. For e-commerce owners or operations managers digitizing legacy catalogs or compliance paperwork, the primary advantage is the reduction in manual validation and layout cleanup. It is built to serve as foundational infrastructure, ensuring that once a document is processed, the resulting output is immediately ready for agentic workflows or automated database indexing. When your digitization project requires high structural fidelity and consistent performance across varying document types, this model is a robust choice for maintaining high throughput without sacrificing the quality of the parsed data.