What are Open Source LLMs for Contract Processing & Review?
Open source LLMs for contract processing & review are specialized large language models designed to analyze, extract, and understand complex legal documents. Using advanced deep learning architectures, these models can process lengthy contracts, identify key clauses, extract structured data from tables and forms, and provide reasoning-based insights. This technology allows legal professionals, compliance teams, and businesses to automate contract review, reduce manual effort, and ensure accuracy with unprecedented efficiency. They foster collaboration, accelerate legal workflows, and democratize access to powerful contract analysis tools, enabling a wide range of applications from due diligence to risk assessment and compliance management.
Qwen/Qwen2.5-VL-72B-Instruct
Qwen2.5-VL-72B-Instruct 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.
Qwen/Qwen2.5-VL-72B-Instruct: Comprehensive Contract Document Understanding
Qwen2.5-VL-72B-Instruct 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. The model demonstrates excellent performance across various benchmarks including image, video, and agent tasks. With 72B parameters and 131K context length, it excels at extracting structured information from complex contract documents, making it ideal for legal document processing and review workflows.
Pros
- Powerful 72B parameter model with 131K context length for long contracts.
- Excels at analyzing text, charts, and layouts within contract documents.
- Supports structured outputs for extracting data from scanned forms and tables.
Cons
- Requires significant computational resources for deployment.
- Higher cost compared to smaller models for high-volume processing.
Why We Love It
- It combines powerful vision-language capabilities with structured output generation, making it perfect for extracting and analyzing complex contract clauses, tables, and legal provisions from any document format.
zai-org/GLM-4.5V
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. Built upon the flagship text model GLM-4.5-Air with 106B total parameters and 12B active parameters, it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. The model is capable of processing diverse visual content such as images, videos, and long documents, achieving state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks.
zai-org/GLM-4.5V: Efficient Multi-Document Contract Analysis
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. The model is built upon the flagship text model GLM-4.5-Air, which has 106B total parameters and 12B active parameters, and it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. Technically, GLM-4.5V follows the lineage of GLM-4.1V-Thinking and introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. Through optimization across pre-training, supervised fine-tuning, and reinforcement learning phases, the model is capable of processing diverse visual content such as images, videos, and long documents, achieving state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks. Additionally, the model features a 'Thinking Mode' switch, allowing users to flexibly choose between quick responses and deep reasoning to balance efficiency and effectiveness—perfect for contract review scenarios.
Pros
- MoE architecture with only 12B active parameters for cost-efficient inference.
- Processes images, videos, and long documents with 66K context length.
- Features 'Thinking Mode' for deep reasoning on complex contract clauses.
Cons
- Smaller context window compared to some competitors.
- May require mode switching between efficiency and deep reasoning.
Why We Love It
- It delivers exceptional contract processing capabilities through its innovative MoE architecture and thinking mode, enabling both rapid document screening and deep legal reasoning at a fraction of the computational cost.
deepseek-ai/DeepSeek-R1
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 and 164K context length.
deepseek-ai/DeepSeek-R1: Advanced Reasoning for Contract Review
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 leveraging a MoE architecture and an impressive 164K context length, DeepSeek-R1 excels at complex contract analysis requiring deep logical reasoning, clause interpretation, and risk assessment. The model's reinforcement learning training ensures accurate, robust, and practical legal analysis aligned with real-world contract review standards.
Pros
- Massive 671B parameter MoE model with advanced reasoning capabilities.
- 164K context length handles extremely long and complex contracts.
- Performance comparable to OpenAI-o1 for reasoning tasks.
Cons
- Higher SiliconFlow pricing at $2.18/M output tokens and $0.5/M input tokens.
- Requires significant computational resources for deployment.
Why We Love It
- It represents the pinnacle of reasoning-based contract analysis, combining massive scale with reinforcement learning optimization to deliver nuanced legal insights, risk identification, and clause interpretation that rivals human expert review.
Contract Processing LLM Comparison
In this table, we compare 2025's leading open source LLMs for contract processing & review, each with a unique strength. For vision-language document understanding, Qwen/Qwen2.5-VL-72B-Instruct provides comprehensive analysis of multi-format contracts. For cost-efficient multi-document processing with deep reasoning capabilities, zai-org/GLM-4.5V offers flexible thinking modes, while deepseek-ai/DeepSeek-R1 prioritizes advanced reasoning for complex legal analysis. This side-by-side view helps you choose the right tool for your specific contract review and processing needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen/Qwen2.5-VL-72B-Instruct | Qwen2.5 | Vision-Language Model | $0.59/M tokens (I/O) | Structured data extraction from documents |
| 2 | zai-org/GLM-4.5V | zai | Vision-Language Model (MoE) | $0.86/M (O) | $0.14/M (I) | Efficient processing with thinking mode |
| 3 | deepseek-ai/DeepSeek-R1 | deepseek-ai | Reasoning Model (MoE) | $2.18/M (O) | $0.5/M (I) | Advanced reasoning for complex contracts |
Frequently Asked Questions
Our top three picks for 2025 are Qwen/Qwen2.5-VL-72B-Instruct, zai-org/GLM-4.5V, and deepseek-ai/DeepSeek-R1. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in contract document understanding, structured data extraction, multi-format processing, and deep legal reasoning.
Our in-depth analysis shows that Qwen/Qwen2.5-VL-72B-Instruct is the top choice for extracting structured data from contracts, thanks to its powerful vision-language capabilities and support for structured outputs from scanned forms, tables, and multi-format documents. For organizations requiring cost-efficient processing with deep reasoning capabilities, zai-org/GLM-4.5V offers an excellent balance with its MoE architecture and thinking mode. For the most complex contract analysis requiring advanced logical reasoning and risk assessment, deepseek-ai/DeepSeek-R1 delivers unmatched performance with its 164K context length and reinforcement learning optimization.