What are Open Source LLMs for Legal Document Analysis?
Open source LLMs for legal document analysis are specialized large language models designed to process, understand, and extract insights from complex legal documents. These models leverage advanced natural language processing, reasoning capabilities, and extended context windows to analyze contracts, case law, regulatory documents, and legal correspondence. They support tasks such as contract clause extraction, legal precedent research, compliance verification, document summarization, and risk assessment. By offering open weights and transparent architectures, these models enable legal professionals, law firms, and enterprises to build customized legal AI solutions while maintaining data privacy and control over proprietary information.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) with 671B total parameters in a Mixture-of-Experts architecture. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, with a massive 164K context window ideal for processing lengthy legal documents, contracts, and case files.
DeepSeek-R1: Elite Reasoning for Complex Legal Analysis
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness. With 671B total parameters in a MoE architecture and an exceptional 164K context window, DeepSeek-R1 excels at analyzing complex legal documents, multi-party contracts, regulatory compliance materials, and extensive case law. Its advanced reasoning capabilities make it ideal for contract review, legal precedent analysis, risk assessment, and due diligence workflows.
Pros
- Exceptional 164K context window handles extensive legal documents.
- Advanced reasoning capabilities for complex legal logic.
- MoE architecture with 671B parameters for superior performance.
Cons
- Higher computational requirements due to model size.
- Premium pricing from SiliconFlow at $2.18/M output tokens.
Why We Love It
- It combines massive context capacity with elite reasoning abilities, making it the ultimate choice for analyzing complex, multi-document legal matters where logical coherence and comprehensive understanding are critical.
Qwen3-235B-A22B
Qwen3-235B-A22B features a Mixture-of-Experts architecture with 235B total parameters and 22B activated parameters. It uniquely supports seamless switching between thinking mode for complex legal reasoning and non-thinking mode for efficient document processing, with a 131K context window and support for over 100 languages.
Qwen3-235B-A22B: Versatile Legal Intelligence
Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, superior human preference alignment in creative writing, role-playing, and multi-turn dialogues. The model excels in agent capabilities for precise integration with external tools and supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities. For legal document analysis, Qwen3-235B-A22B offers exceptional versatility with its dual-mode operation, extensive multilingual support for international contracts, and robust reasoning for clause interpretation and legal argument construction.
Pros
- Dual-mode switching between deep reasoning and efficient processing.
- Support for over 100 languages for international legal work.
- 131K context window for comprehensive document analysis.
Cons
- Shorter context window compared to DeepSeek-R1.
- May require mode selection optimization for specific tasks.
Why We Love It
- Its unique dual-mode capability and exceptional multilingual support make it perfect for international law firms handling cross-border transactions and contracts in multiple languages.
Qwen2.5-VL-72B-Instruct
Qwen2.5-VL-72B-Instruct is a vision-language model with 72B parameters and 131K context window that excels at analyzing scanned legal documents, contracts with complex layouts, charts, and tables. It can extract structured data from invoices, forms, and legal documents while understanding visual elements.
Qwen2.5-VL-72B-Instruct: Visual Legal Document Intelligence
Qwen2.5-VL is a vision-language model in the Qwen2.5 series that shows significant enhancements in several aspects: it has strong visual understanding capabilities, recognizing common objects while analyzing texts, charts, and layouts in images; it functions as a visual agent capable of reasoning and dynamically directing tools; it can comprehend videos over 1 hour long and capture key events; it accurately localizes objects in images by generating bounding boxes or points; and it supports structured outputs for scanned data like invoices and forms. For legal document analysis, this model excels at processing scanned contracts, legal forms with complex layouts, exhibits with charts and diagrams, and handwritten legal notes. Its ability to generate structured outputs makes it invaluable for extracting key information from diverse legal document formats.
Pros
- Processes scanned and image-based legal documents.
- Extracts structured data from complex layouts and tables.
- Analyzes charts, diagrams, and visual elements in exhibits.
Cons
- Higher pricing from SiliconFlow at $0.59/M tokens for both input and output.
- May be overkill for text-only document processing.
Why We Love It
- It bridges the gap between visual and textual legal information, making it indispensable for processing real-world legal documents that combine text, tables, signatures, and complex formatting.
Legal AI Model Comparison
In this table, we compare 2025's leading open source LLMs for legal document analysis, each with unique strengths. DeepSeek-R1 offers the longest context window for extensive legal files, Qwen3-235B-A22B provides versatile dual-mode reasoning with multilingual support, and Qwen2.5-VL-72B-Instruct excels at visual document processing. This side-by-side comparison helps you select the optimal model for your specific legal AI application, from contract review to compliance analysis. All pricing is from SiliconFlow.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning Model | $2.18/M out, $0.50/M in | 164K context for extensive documents |
| 2 | Qwen3-235B-A22B | Qwen3 | Reasoning Model | $1.42/M out, $0.35/M in | Dual-mode + 100+ languages |
| 3 | Qwen2.5-VL-72B-Instruct | Qwen2.5 | Vision-Language Model | $0.59/M tokens (both) | Visual document + layout analysis |
Frequently Asked Questions
Our top three picks for 2025 are DeepSeek-R1, Qwen3-235B-A22B, and Qwen2.5-VL-72B-Instruct. DeepSeek-R1 leads with its massive 164K context window and exceptional reasoning for complex legal logic. Qwen3-235B-A22B offers versatile dual-mode operation with support for over 100 languages, perfect for international legal work. Qwen2.5-VL-72B-Instruct excels at processing visual legal documents including scanned contracts, forms, and documents with complex layouts.
For analyzing lengthy contracts, merger agreements, and multi-party legal documents, DeepSeek-R1's 164K context window is unmatched. For international contracts and cross-border legal work requiring multilingual support, Qwen3-235B-A22B with its 100+ language capability is ideal. For processing scanned legal documents, court filings with exhibits, forms, and documents with complex tables and charts, Qwen2.5-VL-72B-Instruct's vision-language capabilities are essential. For general contract review and legal research, any of these three models will deliver excellent results, with the choice depending on specific requirements like context length, multilingual needs, or visual processing.