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 10,000 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $0.100000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.001667
- Tokens per dollar: 600,000 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, 20.18 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
|
$1.059575 Best Value | ↑ 959.6% more |
| 🥈 |
Ministral 3 (14B)
Mistral AI
|
$2.118250 (rounded ~ $2.12) | ↑ 2018.3% more |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$2.650813 | ↑ 2550.8% more |
| #4 |
Gemini 3.5 Flash-Lite
Google
|
$3.182725 (rounded ~ $3.18) | ↑ 3082.7% more |
| #5 |
Gemini 2.5 Flash
Google
|
$3.182725 (rounded ~ $3.18) | ↑ 3082.7% more |
| #6 |
Mistral Large 3
Mistral AI
|
$5.298125 (rounded ~ $5.30) | ↑ 5198.1% more |
| #7 |
Gemini 3.8 Flash
Google
|
$7.950563 | ↑ 7850.6% more |
| #8 |
GPT-5.4 mini
OpenAI
|
$7.952438 (rounded ~ $7.95) | ↑ 7852.4% more |
| #9 |
o4-mini Deep Research
OpenAI
|
$10.598250 (rounded ~ $10.60) | ↑ 10498.3% more |
| #10 |
Claude Haiku 4.5
Anthropic
|
$10.600750 | ↑ 10500.8% more |
| #11 |
GPT-5.6 Luna
OpenAI
|
$10.603250 (rounded ~ $10.60) | ↑ 10503.3% more |
| #12 |
o4-mini
OpenAI
|
$11.658075 (rounded ~ $11.66) | ↑ 11558.1% more |
| #13 |
Gemini 3.6 Flash
Google
|
$15.901125 (rounded ~ $15.90) | ↑ 15801.1% more |
| #14 |
Gemini 3.5 Flash
Google
|
$15.904875 (rounded ~ $15.90) | ↑ 15804.9% more |
| #15 |
GPT-5.3 Codex Spark
OpenAI
|
$18.564438 (rounded ~ $18.56) | ↑ 18464.4% more |
| #16 |
GPT-5.3 Instant
OpenAI
|
$18.564438 (rounded ~ $18.56) | ↑ 18464.4% more |
| #17 |
Claude Sonnet 5
Anthropic
|
$21.201500 (rounded ~ $21.20) | ↑ 21101.5% more |
| #18 |
Gemini 3.1 Flash
Google
|
$21.206500 (rounded ~ $21.21) | ↑ 21106.5% more |
| #19 |
GPT-5.6 Terra
OpenAI
|
$26.508125 (rounded ~ $26.51) | ↑ 26408.1% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$31.802250 (rounded ~ $31.80) | ↑ 31702.3% more |
| #21 |
Claude Opus 4.7
Anthropic
|
$53.003750 (rounded ~ $53.00) | ↑ 52903.8% more |
| #22 |
Claude Opus 5
Anthropic
|
$53.003750 (rounded ~ $53.00) | ↑ 52903.8% more |
| #23 |
Claude Opus 4.8
Anthropic
|
$53.003750 (rounded ~ $53.00) | ↑ 52903.8% more |
| #24 |
Claude Opus 4.6
Anthropic
|
$53.003750 (rounded ~ $53.00) | ↑ 52903.8% more |
| #25 |
Gemini 2.5 Pro
Google
|
$53.016250 (rounded ~ $53.02) | ↑ 52916.3% more |
| #26 |
GPT-5.5 Instant
OpenAI
|
$53.016250 (rounded ~ $53.02) | ↑ 52916.3% more |
| #27 |
GPT-5.6 Sol
OpenAI
|
$53.016250 (rounded ~ $53.02) | ↑ 52916.3% more |
| #28 |
Grok 4.3
xAI
|
$84.746000 (rounded ~ $84.75) | ↑ 84646% more |
| #29 |
Gemini 3.1 Pro
Google
|
$84.796000 (rounded ~ $84.80) | ↑ 84696% more |
| #30 |
Claude Fable 5.1
Anthropic
|
$104.070625 | ↑ 103970.6% more |
| #31 |
Claude Mythos 5.1
Anthropic
|
$104.070625 | ↑ 103970.6% more |
| #32 |
o3 Deep Research
OpenAI
|
$105.982500 (rounded ~ $105.98) | ↑ 105882.5% more |
| #33 |
GPT-5.4
OpenAI
|
$105.995000 (rounded ~ $106.00) | ↑ 105895% more |
| #34 |
GPT-5.4 Thinking
OpenAI
|
$105.995000 (rounded ~ $106.00) | ↑ 105895% more |
| #35 |
Claude Fable 5
Anthropic
|
$106.007500 (rounded ~ $106.01) | ↑ 105907.5% more |
| #36 |
Claude Mythos 5
Anthropic
|
$106.007500 (rounded ~ $106.01) | ↑ 105907.5% more |
| #37 |
o3 Pro
OpenAI
|
$211.965000 (rounded ~ $211.97) | ↑ 211865% more |
| #38 |
GPT-5.5
OpenAI
|
$211.990000 | ↑ 211890% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$222.773250 (rounded ~ $222.77) | ↑ 222673.3% more |
| #40 |
GPT-6 Astra
OpenAI
|
$424.030000 | ↑ 423930% more |
| #41 |
GPT-6 Astra
OpenAI
|
$424.030000 | ↑ 423930% more |
Mistral Small 3 Mistral AI
Ministral 3 (14B) 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
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
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
o3 Pro OpenAI
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Scaling financial data extraction across thousands of 10-K filings requires a robust, high-fidelity OCR solution that preserves document structure. Mistral OCR 3 has emerged as a specialized tool for this exact requirement, focusing on structural fidelity—maintaining the integrity of tables, formulas, and complex document layouts—rather than just raw text transcription. This precision is critical when your downstream models depend on correctly aligned financial data for quantitative analysis.
For an enterprise managing 10,000 annual reports, the challenge isn’t just volume; it is the degradation of data quality in older or scanned documents. Mistral OCR 3 excels here by outputting structured formats like Markdown or HTML, which are significantly easier for secondary models (or database ingestion pipelines) to map into a relational structure. This capability bridges the gap between raw, unstructured PDF files and clean, machine-readable datasets.
Choosing Mistral OCR 3 for high-volume pipelines allows teams to offload the document-parsing burden from their primary reasoning models. By delegating the extraction and structuring phase to a specialized OCR model, you ensure that your main LLMs—like Claude or Gemini—receive higher-quality input, which inherently reduces hallucination risks and improves the accuracy of financial insights. This separation of concerns is a standard architectural pattern for teams aiming to process massive archives of historical earnings reports with high programmatic reliability.