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 10,000 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: $0.008333 (rounded ~ $0.01)
- Tokens per dollar: 120,000 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: 40.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 300 tokens/second
Best Use Cases
✨ Market Recommendations AI Model Registry
← Back to Mistral OCR 3| Rank | AI Model & Provider | Total Cost | vs Mistral OCR 3 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.129400 Best Value | ↑ 29.4% more |
| 🥈 |
Ministral 3 (14B)
Mistral AI
|
$0.258700 (rounded ~ $0.26) | ↑ 158.7% more |
| 🥉 |
Grok 4.1 Fast
xAI
|
$0.258750 (rounded ~ $0.26) | ↑ 158.8% more |
| #4 |
Claude Haiku 4.6
Anthropic
|
$0.323750 (rounded ~ $0.32) | ↑ 223.8% more |
| #5 |
Gemini 3.1 Flash Lite
Google
|
$0.323875 (rounded ~ $0.32) | ↑ 223.9% more |
| #6 |
Gemini 2.5 Flash
Google
|
$0.389000 (rounded ~ $0.39) | ↑ 289% more |
| #7 |
Mistral Large 3
Mistral AI
|
$0.647250 (rounded ~ $0.65) | ↑ 547.3% more |
| #8 |
GPT-5.4 mini
OpenAI
|
$0.971625 (rounded ~ $0.97) | ↑ 871.6% more |
| #9 |
o4-mini Deep Research
OpenAI
|
$1.294500 (rounded ~ $1.29) | ↑ 1194.5% more |
| #10 |
o4-mini
OpenAI
|
$1.423950 (rounded ~ $1.42) | ↑ 1324% more |
| #11 |
GPT-5.2
OpenAI
|
$2.268875 (rounded ~ $2.27) | ↑ 2168.9% more |
| #12 |
GPT-5.3 Codex Spark
OpenAI
|
$2.268875 (rounded ~ $2.27) | ↑ 2168.9% more |
| #13 |
GPT-5.3 Instant
OpenAI
|
$2.268875 (rounded ~ $2.27) | ↑ 2168.9% more |
| #14 |
Llama 4 Maverick (400B)
Meta AI
|
$2.417700 (rounded ~ $2.42) | ↑ 2317.7% more |
| #15 |
Gemini 3.1 Flash
Google
|
$2.591000 | ↑ 2491% more |
| #16 |
Claude Sonnet 4.6
Anthropic
|
$3.885000 (rounded ~ $3.89) | ↑ 3785% more |
| #17 |
Grok 4
xAI
|
$3.885000 (rounded ~ $3.89) | ↑ 3785% more |
| #18 |
Grok 4.1
xAI
|
$3.885000 (rounded ~ $3.89) | ↑ 3785% more |
| #19 |
Claude Opus 4.7
Anthropic
|
$6.475000 (rounded ~ $6.48) | ↑ 6375% more |
| #20 |
Claude Opus 4.6
Anthropic
|
$6.475000 (rounded ~ $6.48) | ↑ 6375% more |
| #21 |
Gemini 2.5 Pro
Google
|
$6.477500 (rounded ~ $6.48) | ↑ 6377.5% more |
| #22 |
Gemini 3.1 Pro
Google
|
$10.358000 (rounded ~ $10.36) | ↑ 10258% more |
| #23 |
o3 Deep Research
OpenAI
|
$12.945000 (rounded ~ $12.95) | ↑ 12845% more |
| #24 |
GPT-5.4
OpenAI
|
$12.947500 (rounded ~ $12.95) | ↑ 12847.5% more |
| #25 |
GPT-5.4 Thinking
OpenAI
|
$12.947500 (rounded ~ $12.95) | ↑ 12847.5% more |
| #26 |
o3 Pro
OpenAI
|
$25.890000 | ↑ 25790% more |
| #27 |
GPT-5.5
OpenAI
|
$25.895000 (rounded ~ $25.90) | ↑ 25795% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$27.226500 (rounded ~ $27.23) | ↑ 27126.5% more |
| #29 |
GPT-5.5 Pro
OpenAI
|
$38.865000 (rounded ~ $38.87) | ↑ 38765% more |
| #30 |
GPT-5.5 Pro
OpenAI
|
$38.865000 (rounded ~ $38.87) | ↑ 38765% more |
Mistral Small 3 Mistral AI
Ministral 3 (14B) Mistral AI
Grok 4.1 Fast xAI
Claude Haiku 4.6 Anthropic
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
o4-mini OpenAI
GPT-5.2 OpenAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Llama 4 Maverick (400B) Meta AI
Gemini 3.1 Flash Google
Claude Sonnet 4.6 Anthropic
Grok 4 xAI
Grok 4.1 xAI
Claude Opus 4.7 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
o3 Deep Research OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
o3 Pro OpenAI
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-5.5 Pro OpenAI
GPT-5.5 Pro OpenAI
Scaling Document Pipelines for Legacy Archives
Digitizing legacy archives involves more than just text extraction; it requires structural integrity and reliable layout reconstruction. Mistral OCR 3 is built specifically for these high-volume enterprise pipelines, making it a standout choice for teams tasked with converting 10,000 scanned images into structured data monthly.
Unlike general-purpose multimodal models, Mistral OCR 3 treats the document as a primary data object. This specialization is crucial when dealing with historical records that may feature non-standard layouts, faded ink, or complex multi-column formatting. By focusing on document structure, the model reduces the need for expensive post-processing or data cleanup, which is a common bottleneck in industrial-scale OCR projects.
When planning your deployment, consider how Mistral OCR 3 integrates with your existing ingestion infrastructure. Its architecture is optimized for batch-heavy workflows, providing the throughput needed to maintain consistent performance as your archive grows. For teams managing millions of pages, the efficiency of a dedicated OCR engine—rather than an all-purpose vision model—often translates to lower operational overhead and higher accuracy for structured output. As you scale, focus on the model’s ability to handle edge cases in document variety; a robust OCR solution should minimize manual verification steps. Choosing a model that prioritizes the structural nuances of physical documents will significantly accelerate your digitization timeline and improve the quality of the resulting digital repository for downstream analysis.