What are Open Source LLMs for Knowledge Graph Construction?
Open source LLMs for knowledge graph construction are specialized large language models designed to extract, structure, and organize information into interconnected knowledge representations. These models excel at identifying entities, relationships, and semantic connections from unstructured text, documents, and multimodal content. Using advanced reasoning architectures, reinforcement learning, and structured output generation, they transform raw data into graph-based knowledge structures. They foster collaboration, accelerate enterprise data integration, and democratize access to powerful knowledge extraction tools, enabling a wide range of applications from enterprise knowledge bases to scientific research and intelligent search systems.
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 164K context length, it excels at complex reasoning workflows, making it ideal for extracting multi-hop relationships and constructing comprehensive knowledge graphs from large document collections.
DeepSeek-R1: Premier Reasoning for Complex Knowledge Extraction
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 its massive 671B MoE architecture and 164K context window, DeepSeek-R1 excels at understanding complex relationships, performing multi-step reasoning, and extracting structured knowledge—making it the gold standard for building sophisticated knowledge graphs from diverse data sources.
Pros
- State-of-the-art reasoning capabilities for complex entity relationship extraction.
- 164K context length handles large documents and codebases.
- MoE architecture with 671B parameters delivers exceptional performance.
Cons
- Higher computational requirements due to model size.
- Premium pricing at $2.18/M output tokens from SiliconFlow.
Why We Love It
- Its unparalleled reasoning depth and massive context window make it the ultimate choice for constructing comprehensive, multi-layered knowledge graphs from complex data sources.
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 logical reasoning and non-thinking mode for efficient processing. The model excels in agent capabilities for precise integration with external tools and supports over 100 languages, making it ideal for multilingual knowledge graph construction.
Qwen3-235B-A22B: Versatile Reasoning with Agent Capabilities
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. With 131K context length, it's perfectly suited for extracting structured knowledge from diverse multilingual sources and integrating with external knowledge bases.
Pros
- Dual-mode operation optimizes for both complex reasoning and efficient processing.
- Superior agent capabilities enable seamless tool integration for knowledge extraction.
- Multilingual support across 100+ languages for global knowledge graph construction.
Cons
- Requires understanding of thinking vs. non-thinking mode selection.
- 131K context is smaller than some competitors for extremely long documents.
Why We Love It
- Its unique dual-mode architecture and exceptional agent capabilities make it the perfect choice for building dynamic, tool-integrated knowledge graphs across multiple languages.
GLM-4.5
GLM-4.5 is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts architecture with 335B total parameters. It has been extensively optimized for tool use, web browsing, software development, and front-end development, enabling seamless integration with coding agents. GLM-4.5 employs a hybrid reasoning approach for complex reasoning tasks and everyday use cases, making it highly effective for knowledge graph construction workflows.
GLM-4.5: Agent-First Architecture for Knowledge Integration
GLM-4.5 is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture with 335B total parameters. It has been extensively optimized for tool use, web browsing, software development, and front-end development, enabling seamless integration with coding agents such as Claude Code and Roo Code. GLM-4.5 employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of application scenarios—from complex reasoning tasks to everyday use cases. With 131K context length and deep agent optimization, it excels at orchestrating multi-step knowledge extraction workflows, integrating external data sources, and generating structured outputs for knowledge graph population.
Pros
- Purpose-built for AI agent workflows and tool integration.
- Hybrid reasoning adapts to varying complexity in knowledge extraction tasks.
- 335B MoE parameters deliver powerful performance.
Cons
- Agent-focused design may have a learning curve for traditional NLP tasks.
- Context length is sufficient but not leading for extremely large documents.
Why We Love It
- Its agent-first architecture and hybrid reasoning make it the ideal choice for building intelligent, self-directed knowledge graph construction pipelines that can autonomously interact with multiple data sources.
LLM Model Comparison for Knowledge Graph Construction
In this table, we compare 2025's leading open source LLMs for knowledge graph construction, each with unique strengths. DeepSeek-R1 offers unmatched reasoning depth with the largest context window. Qwen3-235B-A22B provides exceptional multilingual and agent capabilities with flexible dual-mode operation. GLM-4.5 delivers purpose-built agent architecture for autonomous knowledge extraction workflows. This side-by-side view helps you choose the right model for your specific knowledge graph construction requirements.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning Model | $0.50 input / $2.18 output per M tokens | Premier reasoning with 164K context |
| 2 | Qwen3-235B-A22B | Qwen3 | MoE Reasoning Model | $0.35 input / $1.42 output per M tokens | Multilingual + agent capabilities |
| 3 | GLM-4.5 | zai | AI Agent Model | $0.50 input / $2.00 output per M tokens | Agent-first architecture |
Frequently Asked Questions
Our top three picks for 2025 are DeepSeek-R1, Qwen3-235B-A22B, and GLM-4.5. Each of these models stood out for their exceptional reasoning capabilities, structured output generation, and unique approaches to extracting entities and relationships—critical requirements for building comprehensive knowledge graphs.
Our in-depth analysis shows several leaders for different needs. DeepSeek-R1 is the top choice for complex, multi-layered knowledge extraction requiring deep reasoning and large context windows. For multilingual knowledge graphs with agent integration, Qwen3-235B-A22B offers unmatched versatility. For autonomous, tool-integrated extraction workflows, GLM-4.5's agent-first architecture is the best fit.