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
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← Back to Mistral OCR 3| Rank | AI Model & Provider | Total Cost | vs Mistral OCR 3 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$12.900400 Best Value | ↑ 12800.4% more |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$32.251375 (rounded ~ $32.25) | ↑ 32151.4% more |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$38.702000 (rounded ~ $38.70) | ↑ 38602% more |
| #4 |
Gemini 2.5 Flash
Google
|
$38.702000 (rounded ~ $38.70) | ↑ 38602% more |
| #5 |
Mistral Large 3
Mistral AI
|
$64.502250 (rounded ~ $64.50) | ↑ 64402.3% more |
| #6 |
Gemini 3.8 Flash
Google
|
$96.753750 (rounded ~ $96.75) | ↑ 96653.8% more |
| #7 |
GPT-5.4 mini
OpenAI
|
$96.754125 (rounded ~ $96.75) | ↑ 96654.1% more |
| #8 |
o4-mini Deep Research
OpenAI
|
$129.004500 (rounded ~ $129.00) | ↑ 128904.5% more |
| #9 |
Claude Haiku 4.5
Anthropic
|
$129.005000 (rounded ~ $129.01) | ↑ 128905% more |
| #10 |
GPT-5.6 Luna
OpenAI
|
$129.005500 (rounded ~ $129.01) | ↑ 128905.5% more |
| #11 |
o4-mini
OpenAI
|
$141.904950 (rounded ~ $141.90) | ↑ 141805% more |
| #12 |
Gemini 3.6 Flash
Google
|
$193.507500 (rounded ~ $193.51) | ↑ 193407.5% more |
| #13 |
Gemini 3.5 Flash
Google
|
$193.508250 (rounded ~ $193.51) | ↑ 193408.3% more |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$225.761375 (rounded ~ $225.76) | ↑ 225661.4% more |
| #15 |
GPT-5.3 Instant
OpenAI
|
$225.761375 (rounded ~ $225.76) | ↑ 225661.4% more |
| #16 |
Llama 4 Maverick (400B)
Meta AI
|
$241.502700 (rounded ~ $241.50) | ↑ 241402.7% more |
| #17 |
Claude Sonnet 5
Anthropic
|
$258.010000 | ↑ 257910% more |
| #18 |
Gemini 3.1 Flash
Google
|
$258.011000 | ↑ 257911% more |
| #19 |
GPT-5.6 Terra
OpenAI
|
$322.513750 (rounded ~ $322.51) | ↑ 322413.8% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$387.015000 (rounded ~ $387.02) | ↑ 386915% more |
| #21 |
Claude Opus 4.7
Anthropic
|
$645.025000 (rounded ~ $645.03) | ↑ 644925% more |
| #22 |
Claude Opus 5
Anthropic
|
$645.025000 (rounded ~ $645.03) | ↑ 644925% more |
| #23 |
Claude Opus 4.8
Anthropic
|
$645.025000 (rounded ~ $645.03) | ↑ 644925% more |
| #24 |
Claude Opus 4.6
Anthropic
|
$645.025000 (rounded ~ $645.03) | ↑ 644925% more |
| #25 |
Gemini 2.5 Pro
Google
|
$645.027500 (rounded ~ $645.03) | ↑ 644927.5% more |
| #26 |
GPT-5.5 Instant
OpenAI
|
$645.027500 (rounded ~ $645.03) | ↑ 644927.5% more |
| #27 |
GPT-5.6 Sol
OpenAI
|
$645.027500 (rounded ~ $645.03) | ↑ 644927.5% more |
| #28 |
Grok 4.3
xAI
|
$1032.028000 (rounded ~ $1,032.03) | ↑ 1031928% more |
| #29 |
Gemini 3.1 Pro
Google
|
$1032.038000 (rounded ~ $1,032.04) | ↑ 1031938% more |
| #30 |
o3 Deep Research
OpenAI
|
$1290.045000 (rounded ~ $1,290.05) | ↑ 1289945% more |
| #31 |
GPT-5.4
OpenAI
|
$1290.047500 (rounded ~ $1,290.05) | ↑ 1289947.5% more |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$1290.047500 (rounded ~ $1,290.05) | ↑ 1289947.5% more |
| #33 |
Claude Fable 5.1
Anthropic
|
$1290.050000 | ↑ 1289950% more |
| #34 |
Claude Mythos 5.1
Anthropic
|
$1290.050000 | ↑ 1289950% more |
| #35 |
Claude Fable 5
Anthropic
|
$1290.050000 | ↑ 1289950% more |
| #36 |
Claude Mythos 5
Anthropic
|
$1290.050000 | ↑ 1289950% more |
| #37 |
o3 Pro
OpenAI
|
$2580.090000 | ↑ 2579990% more |
| #38 |
GPT-5.5
OpenAI
|
$2580.095000 (rounded ~ $2,580.10) | ↑ 2579995% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$2709.136500 (rounded ~ $2,709.14) | ↑ 2709036.5% more |
| #40 |
GPT-5.5 Pro
OpenAI
|
$3870.165000 (rounded ~ $3,870.17) | ↑ 3870065% more |
| #41 |
GPT-6 Astra
OpenAI
|
$5160.200000 | ↑ 5160100% more |
| #42 |
GPT-6 Astra
OpenAI
|
$5160.200000 | ↑ 5160100% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
GPT-5.6 Luna OpenAI
o4-mini OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Llama 4 Maverick (400B) Meta AI
Claude Sonnet 5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
Grok 4.3 xAI
Gemini 3.1 Pro Google
o3 Deep Research OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
o3 Pro OpenAI
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-5.5 Pro OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Scaling Document Processing Pipelines
In high-volume real estate environments, digitizing property listings, contracts, and financial disclosures requires a robust document processing pipeline. When scaling to 1 million documents monthly, the ability to extract structured data—not just raw text—becomes the critical factor for operational efficiency. Mistral OCR 3 represents a significant shift in how enterprises handle these workloads, moving away from simple text extraction toward structured document intelligence.
The primary advantage of Mistral OCR 3 is its ability to output structured representations with native paragraph-level bounding boxes and structural block labels. This is transformative for automated workflows. Instead of writing complex downstream parsing logic to interpret unstructured text, agents can rely on the model to classify tables, signatures, and headers natively. This reduces the brittleness of standard OCR pipelines, which often fail when encountering non-standard document layouts like custom lease agreements or multi-page inspection reports.
For teams managing high-volume ingestion, the model’s confidence scores are an invaluable feature for compliance and verification. By routing low-confidence extractions to human reviewers automatically, teams can maintain high accuracy without bottlenecking the entire pipeline. This balance of automated throughput and auditable data quality is essential for mid-market firms and SaaS platforms that cannot afford the downtime or error rates of legacy OCR tools. Whether you are automating client onboarding or processing thousands of daily transaction records, the transition to structured OCR intelligence is a key step in maturing your enterprise data strategy.