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 5,000 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.001818
- Tokens per dollar: 550,000 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: 3 minutes, 3.51 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
|
$0.001400 Best Value | ↓ 98.6% cheaper |
| 🥈 |
Devstral Small 2
Mistral AI
|
$0.001400 | ↓ 98.6% cheaper |
| 🥉 |
Ministral 3 (14B)
Mistral AI
|
$0.002300 | ↓ 97.7% cheaper |
| #4 |
Nemotron 3 Super
NVIDIA
|
$0.004100 | ↓ 95.9% cheaper |
| #5 |
Gemini 3.1 Flash Lite
Google
|
$0.004438 | ↓ 95.6% cheaper |
| #6 |
Devstral 2
Mistral AI
|
$0.005225 (rounded ~ $0.01) | ↓ 94.8% cheaper |
| #7 |
Gemini 3.5 Flash-Lite
Google
|
$0.006200 (rounded ~ $0.01) | ↓ 93.8% cheaper |
| #8 |
Gemini 2.5 Flash
Google
|
$0.006200 (rounded ~ $0.01) | ↓ 93.8% cheaper |
| #9 |
Mistral Large 3
Mistral AI
|
$0.007000 (rounded ~ $0.01) | ↓ 93% cheaper |
| #10 |
Gemini 3.8 Flash
Google
|
$0.012375 (rounded ~ $0.01) | ↓ 87.6% cheaper |
| #11 |
GPT-5.4 mini
OpenAI
|
$0.013313 (rounded ~ $0.01) | ↓ 86.7% cheaper |
| #12 |
o4-mini Deep Research
OpenAI
|
$0.015250 (rounded ~ $0.02) | ↓ 84.8% cheaper |
| #13 |
Claude Haiku 4.5
Anthropic
|
$0.016500 (rounded ~ $0.02) | ↓ 83.5% cheaper |
| #14 |
o4-mini
OpenAI
|
$0.016775 (rounded ~ $0.02) | ↓ 83.2% cheaper |
| #15 |
Gemini 3.1 Flash
Google
|
$0.017750 (rounded ~ $0.02) | ↓ 82.3% cheaper |
| #16 |
GPT-5.6 Luna
OpenAI
|
$0.017750 (rounded ~ $0.02) | ↓ 82.3% cheaper |
| #17 |
Gemini 3.6 Flash
Google
|
$0.024750 (rounded ~ $0.02) | ↓ 75.3% cheaper |
| #18 |
Gemini 3.5 Flash
Google
|
$0.026625 (rounded ~ $0.03) | ↓ 73.4% cheaper |
| #19 |
Magistral Medium
Mistral AI
|
$0.026750 (rounded ~ $0.03) | ↓ 73.3% cheaper |
| #20 |
Claude Sonnet 5
Anthropic
|
$0.033000 (rounded ~ $0.03) | ↓ 67% cheaper |
| #21 |
GPT-5.3 Codex Spark
OpenAI
|
$0.035438 (rounded ~ $0.04) | ↓ 64.6% cheaper |
| #22 |
GPT-5.3 Instant
OpenAI
|
$0.035438 (rounded ~ $0.04) | ↓ 64.6% cheaper |
| #23 |
GPT-5.6 Terra
OpenAI
|
$0.044375 (rounded ~ $0.04) | ↓ 55.6% cheaper |
| #24 |
Claude Sonnet 4.6
Anthropic
|
$0.049500 | ↓ 50.5% cheaper |
| #25 |
Gemini 2.5 Pro
Google
|
$0.050625 | ↓ 49.4% cheaper |
| #26 |
Grok 4.3
xAI
|
$0.051000 (rounded ~ $0.05) | ↓ 49% cheaper |
| #27 |
Grok 4.20 Beta
xAI
|
$0.051000 (rounded ~ $0.05) | ↓ 49% cheaper |
| #28 |
Gemini 3.1 Pro
Google
|
$0.071000 (rounded ~ $0.07) | ↓ 29% cheaper |
| #29 |
Claude Opus 4.7
Anthropic
|
$0.082500 (rounded ~ $0.08) | ↓ 17.5% cheaper |
| #30 |
Claude Opus 5
Anthropic
|
$0.082500 (rounded ~ $0.08) | ↓ 17.5% cheaper |
| #31 |
Claude Opus 4.8
Anthropic
|
$0.082500 (rounded ~ $0.08) | ↓ 17.5% cheaper |
| #32 |
Claude Opus 4.6
Anthropic
|
$0.082500 (rounded ~ $0.08) | ↓ 17.5% cheaper |
| #33 |
GPT-5.4
OpenAI
|
$0.088750 (rounded ~ $0.09) | ↓ 11.3% cheaper |
| #34 |
GPT-5.4 Thinking
OpenAI
|
$0.088750 (rounded ~ $0.09) | ↓ 11.3% cheaper |
| #35 |
GPT-5.5 Instant
OpenAI
|
$0.088750 (rounded ~ $0.09) | ↓ 11.3% cheaper |
| #36 |
GPT-5.6 Sol
OpenAI
|
$0.088750 (rounded ~ $0.09) | ↓ 11.3% cheaper |
| #37 |
o3 Deep Research
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 52.5% more |
| #38 |
Claude Fable 5.1
Anthropic
|
$0.163125 (rounded ~ $0.16) | ↑ 63.1% more |
| #39 |
Claude Mythos 5.1
Anthropic
|
$0.163125 (rounded ~ $0.16) | ↑ 63.1% more |
| #40 |
Claude Fable 5
Anthropic
|
$0.165000 (rounded ~ $0.17) | ↑ 65% more |
| #41 |
Claude Mythos 5
Anthropic
|
$0.165000 (rounded ~ $0.17) | ↑ 65% more |
| #42 |
GPT-5.5
OpenAI
|
$0.177500 (rounded ~ $0.18) | ↑ 77.5% more |
| #43 |
o3 Pro
OpenAI
|
$0.305000 (rounded ~ $0.31) | ↑ 205% more |
| #44 |
GPT-6 Astra
OpenAI
|
$0.330000 | ↑ 230% more |
| #45 |
GPT-5.2 Pro
OpenAI
|
$0.425250 (rounded ~ $0.43) | ↑ 325.3% more |
| #46 |
GPT-5.2 Pro
OpenAI
|
$0.425250 (rounded ~ $0.43) | ↑ 325.3% more |
Mistral Small 3 Mistral AI
Devstral Small 2 Mistral AI
Ministral 3 (14B) Mistral AI
Nemotron 3 Super NVIDIA
Gemini 3.1 Flash Lite Google
Devstral 2 Mistral AI
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
o4-mini OpenAI
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Magistral Medium Mistral AI
Claude Sonnet 5 Anthropic
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
o3 Deep Research OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Automating Unstructured Document Workflows
Traditional OCR often fails when faced with the messy reality of enterprise documentation—handwritten annotations, complex tables, and non-standard form layouts frequently result in broken data and manual cleanup. Mistral OCR 3 addresses these pain points by prioritizing structural understanding over simple character recognition. By parsing documents into clean Markdown and structured HTML, the model preserves the semantic hierarchy that is often lost in legacy optical character recognition systems.
For organizations processing millions of pages, the transition from raw text extraction to document intelligence is transformative. Mistral OCR 3 is built to ingest dense, multi-level tables and layered form content, making it an ideal engine for RAG pipelines that ingest invoices, legal contracts, and scanned archival records. Its ability to convert visually complex pages into a structured format allows downstream agents to perform reasoning tasks with high accuracy, as the model inherently understands the relationship between labels, checkboxes, and grid structures.
When scaling to 1M documents per month, the batch-processing capability becomes a critical operational asset. By offloading document parsing to an asynchronous, structure-aware model, engineering teams can significantly reduce the overhead associated with pre-processing and cleaning document data. This shift allows developers to focus on higher-value logic rather than fighting to correct garbled, poorly formatted text. Mistral OCR 3 stands out as a specialized infrastructure layer, providing the high-fidelity outputs required for reliable, automated document analysis at a scale where performance and structural integrity are non-negotiable.