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 0 input tokens and 0 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.000000
- Tokens per dollar: 0 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: 0.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 300 tokens/second
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Mistral OCR 3. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ 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
|
$12.900000 Best Value | ↑ 12800% more |
| 🥈 |
Ministral 3 (14B)
Mistral AI
|
$25.800100 | ↑ 25700.1% more |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$32.250000 | ↑ 32150% more |
| #4 |
Gemini 2.5 Flash
Google
|
$38.700000 | ↑ 38600% more |
| #5 |
Mistral Large 3
Mistral AI
|
$64.500250 | ↑ 64400.3% more |
| #6 |
GPT-5.4 mini
OpenAI
|
$96.750000 | ↑ 96650% more |
| #7 |
o4-mini Deep Research
OpenAI
|
$129.000000 | ↑ 128900% more |
| #8 |
Claude Haiku 4.5
Anthropic
|
$129.000000 | ↑ 128900% more |
| #9 |
o4-mini
OpenAI
|
$141.900000 | ↑ 141800% more |
| #10 |
Grok 4.3
xAI
|
$161.250000 | ↑ 161150% more |
| #11 |
Gemini 3.5 Flash
Google
|
$193.500000 | ↑ 193400% more |
| #12 |
GPT-5.3 Codex Spark
OpenAI
|
$225.750000 | ↑ 225650% more |
| #13 |
GPT-5.3 Instant
OpenAI
|
$225.750000 | ↑ 225650% more |
| #14 |
Llama 4 Maverick (400B)
Meta AI
|
$241.500000 | ↑ 241400% more |
| #15 |
Gemini 3.1 Flash
Google
|
$258.000000 | ↑ 257900% more |
| #16 |
Claude Sonnet 4.6
Anthropic
|
$387.000000 | ↑ 386900% more |
| #17 |
Claude Opus 4.7
Anthropic
|
$645.000000 | ↑ 644900% more |
| #18 |
Claude Opus 4.8
Anthropic
|
$645.000000 | ↑ 644900% more |
| #19 |
Claude Opus 4.6
Anthropic
|
$645.000000 | ↑ 644900% more |
| #20 |
Gemini 2.5 Pro
Google
|
$645.000000 | ↑ 644900% more |
| #21 |
GPT-5.5 Instant
OpenAI
|
$645.000000 | ↑ 644900% more |
| #22 |
Gemini 3.1 Pro
Google
|
$1032.000000 | ↑ 1031900% more |
| #23 |
GPT-5.4
OpenAI
|
$1290.000000 | ↑ 1289900% more |
| #24 |
GPT-5.4 Thinking
OpenAI
|
$1290.000000 | ↑ 1289900% more |
| #25 |
o3 Deep Research
OpenAI
|
$1290.000000 | ↑ 1289900% more |
| #26 |
GPT-5.5
OpenAI
|
$2580.000000 | ↑ 2579900% more |
| #27 |
o3 Pro
OpenAI
|
$2580.000000 | ↑ 2579900% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$2709.000000 | ↑ 2708900% more |
| #29 |
GPT-5.5 Pro
OpenAI
|
$3870.000000 | ↑ 3869900% more |
| #30 |
GPT-5.4 Pro
OpenAI
|
$15480.000000 | ↑ 15479900% more |
| #31 |
GPT-5.4 Pro
OpenAI
|
$15480.000000 | ↑ 15479900% 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
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
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
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
o3 Deep Research OpenAI
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.5 Pro OpenAI
GPT-5.4 Pro OpenAI
GPT-5.4 Pro OpenAI
For organizations dealing with high volumes of legacy financial documents, the bottleneck is rarely the LLM’s reasoning power—it is the quality of the initial OCR. Mistral OCR 3 has emerged as a specialized solution for converting dense, messy PDFs, forms, and handwritten notes into structured, machine-readable data. Unlike traditional OCR that treats documents as flat text, Mistral OCR 3 prioritizes layout analysis, correctly identifying tables, nested hierarchies, and form structures before extraction occurs. This is vital for 10-K filings and custodial statements where data integrity in tables is paramount. At a scale of 1 million PDFs, the cost efficiency of the model, particularly with batch processing, becomes a significant factor in your operational budget. By outputting clean Markdown with HTML table reconstruction, it minimizes the pre-processing logic your team has to write to clean the data before feeding it into your LLM. This model acts as the foundational layer of your document-to-data pipeline, enabling more accurate RAG retrieval because your semantic index is built on high-fidelity structured data rather than broken text blocks. If you are building an automated pipeline for competitive intelligence or document analysis, integrating Mistral OCR 3 allows you to move from low-quality, expensive-to-clean text to high-quality, actionable data at a fraction of the traditional cost.