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 |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 57.5% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↑ 5% more |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.210000 | ↑ 110% more |
| #4 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 602.5% more |
| #5 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 1297.5% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 1297.5% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 5500% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 5500% 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
For indie developers automating legal contract ingestion, the bottleneck is rarely the LLM reasoning—it is the quality of the raw data. When processing 50-page legal agreements, standard text extractors often mangle complex clauses, nested lists, and table-based schedules that are essential for accurate clause extraction. Mistral OCR 3 stands out as a specialized tool for this exact challenge. By treating documents as structured artifacts rather than flat images, it preserves the logical hierarchy of a contract, ensuring that headings, definitions, and specific clause sections remain intact. This structural fidelity is critical for downstream RAG pipelines; if your retrieval engine cannot parse a table of liabilities correctly, your AI agent will hallucinate the risk assessment. The model excels at reconstructing document layout, making it an ideal first step for building high-accuracy compliance tools or EULA analysis features in your game. Instead of relying on generic vision-to-text approaches, Mistral OCR 3 provides the clean, machine-readable markdown that allows your downstream reasoning model to focus purely on the legal nuance rather than deciphering garbled formatting. For teams managing large volumes of documentation, this clean ingestion layer is the key to minimizing errors in automated contract review.