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 50,000 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: $0.001923
- Tokens per dollar: 520,000 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: 2 minutes, 53.51 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 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.007850 (rounded ~ $0.01) Best Value | ↓ 92.2% cheaper |
| 🥈 |
Ministral 3 (14B)
Mistral AI
|
$0.015600 (rounded ~ $0.02) | ↓ 84.4% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.020000 | ↓ 80% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.024350 (rounded ~ $0.02) | ↓ 75.7% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.039500 | ↓ 60.5% cheaper |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.060000 | ↓ 40% cheaper |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.079000 (rounded ~ $0.08) | ↓ 21% cheaper |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.079500 | ↓ 20.5% cheaper |
| #9 |
o4-mini
OpenAI
|
$0.086900 (rounded ~ $0.09) | ↓ 13.1% cheaper |
| #10 |
Grok 4.3
xAI
|
$0.097500 (rounded ~ $0.10) | ↓ 2.5% cheaper |
| #11 |
Gemini 3.5 Flash
Google
|
$0.120000 | ↑ 20% more |
| #12 |
Llama 4 Maverick (400B)
Meta AI
|
$0.129450 | ↑ 29.5% more |
| #13 |
GPT-5.3 Codex Spark
OpenAI
|
$0.141750 (rounded ~ $0.14) | ↑ 41.8% more |
| #14 |
GPT-5.3 Instant
OpenAI
|
$0.141750 (rounded ~ $0.14) | ↑ 41.8% more |
| #15 |
Gemini 3.1 Flash
Google
|
$0.160000 | ↑ 60% more |
| #16 |
Claude Sonnet 4.6
Anthropic
|
$0.238500 (rounded ~ $0.24) | ↑ 138.5% more |
| #17 |
Claude Opus 4.7
Anthropic
|
$0.397500 (rounded ~ $0.40) | ↑ 297.5% more |
| #18 |
Claude Opus 4.8
Anthropic
|
$0.397500 (rounded ~ $0.40) | ↑ 297.5% more |
| #19 |
Claude Opus 4.6
Anthropic
|
$0.397500 (rounded ~ $0.40) | ↑ 297.5% more |
| #20 |
Gemini 2.5 Pro
Google
|
$0.400000 | ↑ 300% more |
| #21 |
GPT-5.5 Instant
OpenAI
|
$0.400000 | ↑ 300% more |
| #22 |
Gemini 3.1 Pro
Google
|
$0.634000 (rounded ~ $0.63) | ↑ 534% more |
| #23 |
o3 Deep Research
OpenAI
|
$0.790000 | ↑ 690% more |
| #24 |
GPT-5.4
OpenAI
|
$0.792500 (rounded ~ $0.79) | ↑ 692.5% more |
| #25 |
GPT-5.4 Thinking
OpenAI
|
$0.792500 (rounded ~ $0.79) | ↑ 692.5% more |
| #26 |
o3 Pro
OpenAI
|
$1.580000 | ↑ 1480% more |
| #27 |
GPT-5.5
OpenAI
|
$1.585000 (rounded ~ $1.59) | ↑ 1485% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$1.701000 (rounded ~ $1.70) | ↑ 1601% more |
| #29 |
GPT-5.5 Pro
OpenAI
|
$2.400000 | ↑ 2300% more |
| #30 |
GPT-5.5 Pro
OpenAI
|
$2.400000 | ↑ 2300% more |
Mistral Small 3 Mistral AI
Ministral 3 (14B) Mistral AI
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
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.5 Flash Google
Llama 4 Maverick (400B) Meta AI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Gemini 3.1 Flash Google
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.5 Instant OpenAI
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
Optimizing Archival Digitization
Educational content creators often face the significant challenge of converting legacy physical archives into searchable digital curricula. Mistral OCR 3 stands out for its specialized focus on optical character recognition, particularly for documents that feature complex visual layouts, such as old textbooks or multi-column research papers. Unlike general-purpose models that may struggle with structural integrity during extraction, this model is designed to preserve the relationship between text and its original visual context.
For creators processing 500 scanned pages at a time, the decision to use a dedicated OCR model rather than a standard vision-language model often comes down to factual precision. Mistral’s architecture excels at identifying dense text blocks and small annotations often found in historical archives. This makes it an ideal choice for building high-fidelity datasets that will eventually be used for RAG systems or automated course generation. The model’s efficiency allows for rapid batch processing, ensuring that large-scale digitization projects remain manageable for individual developers or small hobbyist teams. When choosing this path, creators benefit from a tool that prioritizes the structural accuracy of the data, which is the foundation of any reliable educational resource.