Top picks for Table Extraction from PDFs (2026)
Pulling structured tables out of complex documents. Ranked from 340 live models on the OpenRouter catalog, weighted for vision input, structured output, context window.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 150 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | OpenAI: GPT-5openai/gpt-5 | 148 | $1.25 | $10.00 | 400,000 | Details → |
| 3 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 146 | $5.00 | $25.00 | 1,000,000 | Details → |
| 4 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 144 | $5.00 | $25.00 | 1,000,000 | Details → |
| 5 | OpenAI: o3openai/o3 | 143 | $2.00 | $8.00 | 200,000 | Details → |
| 6 | OpenAI: GPT-4.1openai/gpt-4.1 | 141 | $2.00 | $8.00 | 1,047,576 | Details → |
| 7 | Google: Gemini 2.5 Progoogle/gemini-2.5-pro | 136 | $1.25 | $10.00 | 1,048,576 | Details → |
| 8 | Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 136 | $0.15 | $0.60 | 1,048,576 | Details → |
| 9 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 134 | $0.30 | $2.50 | 1,048,576 | Details → |
| 10 | OpenAI: GPT-4.1 Miniopenai/gpt-4.1-mini | 133 | $0.40 | $1.60 | 1,047,576 | Details → |
| 11 | OpenAI: o4 Mini Highopenai/o4-mini-high | 132 | $1.10 | $4.40 | 200,000 | Details → |
| 12 | OpenAI: GPT-4.1 Nanoopenai/gpt-4.1-nano | 131 | $0.10 | $0.40 | 1,047,576 | Details → |
| 13 | Qwen: Qwen3.7 Plusqwen/qwen3.7-plus | 131 | $0.40 | $1.60 | 1,000,000 | Details → |
| 14 | MiniMax: MiniMax M3minimax/minimax-m3 | 131 | $0.30 | $1.20 | 1,048,576 | Details → |
| 15 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 131 | $1.50 | $9.00 | 1,048,576 | Details → |
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How we ranked these
For Table Extraction from PDFs, we weight models on vision input, structured output, context window. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Table Extraction from PDFs
Table extraction from PDFs is the process of identifying tabular data within document pages and converting it into machine-readable structured formats like CSV or JSON. You need this when you're ingesting financial reports, research datasets, inventory sheets, or regulatory documents at scale and can't manually copy tables. Good models handle rotated tables, merged cells, multi-page tables, and noisy scans; poor ones fail on non-standard layouts or confuse text proximity for cell boundaries. The primary trade-off is accuracy versus speed: vision-based models (Claude, GPT-4V) excel at complex layouts but cost more per page than lightweight OCR-plus-rule engines, which are faster but brittle on irregular structures.
When to use: Use this when you have PDF documents containing data tables that need to become usable spreadsheets or databases, and manual copy-paste would take too long or introduce errors.
Common questions
What is the most accurate AI model for extracting tables from scanned PDFs?
Claude 3.5 Sonnet and GPT-4 Vision consistently rank highest for accuracy on messy, scanned documents because they reason about spatial relationships and handle visual ambiguity well. For production workflows, many teams use hybrid approaches pairing vision models with post-processing validation to catch edge cases like partial tables or headers that span multiple rows.
How much does it cost to extract tables from a 500-page PDF using an AI model?
With GPT-4 Vision, expect roughly $5-15 depending on image resolution and table density; Claude costs $2-8 for the same job. Open-source alternatives like Paddle OCR cost nothing to run but require engineering time to handle failures. Most teams find the per-page cost acceptable only when tables are mission-critical or when volume justifies building a custom pipeline.