What are Open Source LLMs for Data Analysis?
Open source LLMs for data analysis are specialized Large Language Models designed to process, interpret, and extract insights from complex datasets, documents, charts, tables, and multimodal content. Using advanced deep learning architectures including reasoning capabilities and vision-language understanding, they can analyze structured and unstructured data, perform mathematical computations, generate data visualizations, and provide intelligent responses to analytical queries. These models democratize access to powerful analytical tools, enabling developers and data scientists to build sophisticated data analysis applications, automate report generation, and extract actionable insights from diverse data sources with unprecedented accuracy and efficiency.
Qwen2.5-VL-72B-Instruct
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.
Qwen2.5-VL-72B-Instruct: Comprehensive Multimodal Data Analysis
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 a 131K context length enabling deep analysis of extensive datasets. With 72B parameters, this model excels at extracting structured information from complex visual data sources, making it ideal for comprehensive data analysis workflows.
Pros
- Powerful multimodal analysis of charts, tables, and documents.
- Supports structured data extraction from invoices and forms.
- 131K context length for analyzing extensive datasets.
Cons
- Higher computational requirements with 72B parameters.
- Requires balanced pricing at $0.59/M tokens on SiliconFlow.
Why We Love It
- It delivers state-of-the-art multimodal data analysis, seamlessly extracting insights from visual data, charts, and long-form documents with exceptional accuracy.
DeepSeek-V3
DeepSeek-V3-0324 utilizes a Mixture-of-Experts (MoE) architecture with 671B total parameters and incorporates reinforcement learning techniques from the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. The model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities.
DeepSeek-V3: Advanced Reasoning for Complex Data Analysis
DeepSeek-V3-0324 utilizes a Mixture-of-Experts (MoE) architecture with 671B total parameters and incorporates reinforcement learning techniques from the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. Additionally, the model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities. With a 131K context length, DeepSeek-V3 excels at complex analytical reasoning, making it perfect for data scientists who need to perform sophisticated mathematical computations, statistical analysis, and derive insights from large datasets. The model's efficient MoE design ensures powerful performance while maintaining reasonable computational costs at $1.13/M output tokens and $0.27/M input tokens on SiliconFlow.
Pros
- Exceptional reasoning capabilities for mathematical analysis.
- Efficient MoE architecture with 671B total parameters.
- Superior performance on coding and data manipulation tasks.
Cons
- Primarily text-focused without native vision capabilities.
- Moderate pricing for extensive analytical workloads.
Why We Love It
- It combines cutting-edge reasoning with mathematical prowess, making it the go-to model for complex data analysis requiring deep logical processing and statistical computation.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. Built on a Mixture-of-Experts (MoE) architecture with 106B total parameters and 12B active parameters, it introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. The model features a 'Thinking Mode' switch, allowing users to flexibly choose between quick responses and deep reasoning.
GLM-4.5V: Intelligent Multimodal Data Understanding
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 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. With a 66K context length and competitive pricing at $0.86/M output tokens and $0.14/M input tokens on SiliconFlow, GLM-4.5V offers exceptional value for comprehensive data analysis tasks.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- Flexible 'Thinking Mode' for balancing speed and depth.
- Efficient MoE architecture with 12B active parameters.
Cons
- Smaller context length (66K) compared to competitors.
- May require mode switching for optimal performance.
Why We Love It
- It offers unparalleled flexibility with its thinking mode toggle, enabling data analysts to seamlessly switch between rapid exploration and deep analytical reasoning across multimodal datasets.
LLM Data Analysis Model Comparison
In this table, we compare 2025's leading open source LLMs for data analysis, each with unique strengths. Qwen2.5-VL-72B-Instruct excels in multimodal visual data analysis, DeepSeek-V3 provides advanced reasoning for mathematical computations, and GLM-4.5V offers flexible thinking modes for diverse analytical tasks. This side-by-side comparison helps you choose the right model for your specific data analysis requirements.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen2.5-VL-72B-Instruct | Qwen2.5 | Vision-Language Model | $0.59/M tokens | Multimodal data extraction |
| 2 | DeepSeek-V3 | deepseek-ai | Reasoning Model | $1.13/M output, $0.27/M input | Advanced mathematical reasoning |
| 3 | GLM-4.5V | zai | Vision-Language Model | $0.86/M output, $0.14/M input | Flexible thinking modes |
Frequently Asked Questions
Our top three picks for 2025 are Qwen2.5-VL-72B-Instruct, DeepSeek-V3, and GLM-4.5V. Each of these models stood out for their innovation, performance, and unique approach to solving data analysis challenges—from multimodal document understanding to advanced mathematical reasoning and flexible analytical workflows.
For visual data analysis, Qwen2.5-VL-72B-Instruct and GLM-4.5V are the top choices. Qwen2.5-VL-72B-Instruct excels at analyzing texts, charts, and layouts within images, and supports structured outputs for scanned data like invoices and forms. GLM-4.5V offers state-of-the-art performance on multimodal benchmarks with its flexible thinking mode, making it ideal for diverse visual data analysis tasks including images, videos, and long documents.