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 2,000 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.000100
- Tokens per dollar: 10,020,000 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: 55 minutes, 40.18 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 |
|---|---|---|---|
| 🏆 |
Grok 4.20 Beta
xAI
|
$0.278000 (rounded ~ $0.28) Best Value | ↑ 178% more |
| 🥈 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 602.5% more |
| 🥉 |
Gemini 3.1 Pro
Google
|
$1.118000 (rounded ~ $1.12) | ↑ 1018% more |
| #4 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 1297.5% more |
| #5 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 1297.5% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 1297.5% more |
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
For OCR document specialists, high-volume archival tasks require a balance between speed and parsing fidelity. Mistral OCR 3 has emerged as a specialized tool for converting dense, unstructured document archives—such as historical records, handwritten logs, and complex multi-column forms—into clean, searchable text. In a typical 50-document knowledge base Q&A scenario, the ability to maintain spatial context is paramount.
Unlike general-purpose models, this architecture is optimized specifically for image-to-text workflows, making it a natural choice for digitizing paper archives where layout preservation is as critical as content extraction. When handling 1 million tokens of input, the model excels at identifying tabular structures and hierarchical layouts that often confuse standard LLMs. For teams managing internal Q&A, this means the retrieval engine receives higher-quality text chunks, which directly correlates to better RAG accuracy.
We recommend this model for workflows where the primary bottleneck is the ingestion and digitization of low-quality or non-standardized documents. By delegating the heavy lifting of OCR to a model tuned for the task, you reduce the preprocessing burden on your primary reasoning model. This separation of concerns—parsing documents with a dedicated OCR engine and querying them with a separate reasoning model—is a proven pattern for maintaining high reliability in internal knowledge bases while keeping operational overhead predictable at scale.