What are Open Source LLMs for Cybersecurity & Threat Analysis?
Open source LLMs for cybersecurity and threat analysis are specialized large language models designed to identify, analyze, and respond to security threats in real-time. Using advanced reasoning architectures and deep learning techniques, they process security logs, network traffic patterns, vulnerability reports, and threat intelligence to detect anomalies, predict attacks, and recommend remediation strategies. These models enable security professionals to automate threat detection, conduct sophisticated security audits, and analyze complex attack vectors with unprecedented accuracy. They foster collaboration across security teams, accelerate incident response, and democratize access to enterprise-grade security intelligence, enabling organizations of all sizes to defend against evolving cyber threats.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) with 671B total parameters in a MoE architecture. It addresses issues of repetition and readability while achieving performance comparable to OpenAI-o1 across math, code, and reasoning tasks. The model's advanced reasoning capabilities make it ideal for analyzing complex security scenarios, identifying multi-stage attacks, and providing detailed threat intelligence with logical step-by-step analysis.
DeepSeek-R1: Advanced Reasoning for Complex Threat 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 parameters in a MoE architecture and 164K context length, the model excels at analyzing complex attack chains, correlating security events across multiple systems, and generating comprehensive threat assessments. Its reinforcement learning approach ensures it provides accurate, actionable security insights that adapt to evolving threat landscapes.
Pros
- Exceptional reasoning for complex multi-stage attack analysis.
- 671B parameters with MoE efficiency for large-scale security data.
- 164K context length for comprehensive log and incident analysis.
Cons
- Higher computational requirements for deployment.
- Premium pricing at $2.18/M output tokens from SiliconFlow.
Why We Love It
- It delivers GPT-o1-level reasoning capabilities specifically optimized for analyzing sophisticated cyber threats and attack patterns with logical, step-by-step explanations that security teams can act upon.
Qwen3-235B-A22B
Qwen3-235B-A22B features a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. It uniquely supports seamless switching between thinking mode for complex security analysis and non-thinking mode for rapid threat triage. The model demonstrates significantly enhanced reasoning capabilities, excels in tool integration for security platforms, and supports over 100 languages for global threat intelligence.
Qwen3-235B-A22B: Versatile Security Intelligence with Dual-Mode Analysis
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, vulnerability analysis, and threat modeling) and non-thinking mode (for efficient, real-time security alerts and incident triage). It demonstrates significantly enhanced reasoning capabilities, superior human preference alignment, and excels in agent capabilities for precise integration with external security tools like SIEM platforms, vulnerability scanners, and threat intelligence feeds. Supporting over 100 languages, it enables global security operations teams to analyze international threats with 131K context length for comprehensive security documentation review.
Pros
- Dual-mode operation for both deep analysis and rapid response.
- Superior tool integration for security platforms and APIs.
- 131K context for analyzing extensive security logs and reports.
Cons
- Requires understanding of mode switching for optimal use.
- May be overpowered for simple security automation tasks.
Why We Love It
- It provides the perfect balance between deep security reasoning and rapid threat response, with exceptional agent capabilities that seamlessly integrate with existing security infrastructure for end-to-end threat management.
GLM-4.5
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 security analysis. The model employs a hybrid reasoning approach that adapts to both complex security investigations and everyday threat monitoring, making it ideal for automated security operations.
GLM-4.5: Agent-Optimized Security Automation Platform
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 security automation platforms, SOAR systems, and penetration testing frameworks. GLM-4.5 employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of security scenarios—from complex threat hunting investigations to automated vulnerability scanning and patch management. With 131K context length, it can analyze entire codebases for security flaws, review extensive audit logs, and generate detailed security reports while actively coordinating with security tools to implement defensive measures.
Pros
- Purpose-built for security agent workflows and automation.
- 335B parameters with MoE efficiency for enterprise security.
- Hybrid reasoning adapts to various security task complexities.
Cons
- Higher cost at $2.00/M output tokens from SiliconFlow.
- Requires robust infrastructure for optimal performance.
Why We Love It
- It transforms cybersecurity operations through intelligent agent capabilities, enabling autonomous threat response, continuous security monitoring, and seamless coordination across security tools for comprehensive defense automation.
Security LLM Comparison
In this table, we compare 2025's leading open source LLMs for cybersecurity and threat analysis, each with unique security-focused strengths. For advanced threat reasoning, DeepSeek-R1 provides unmatched analytical depth. For versatile security operations with tool integration, Qwen3-235B-A22B offers dual-mode flexibility, while GLM-4.5 prioritizes autonomous security agent capabilities. This side-by-side view helps you choose the right model for your specific security infrastructure and threat landscape.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Security Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning, Security Analysis | $2.18/M tokens (output) | Advanced threat reasoning & attack chain analysis |
| 2 | Qwen3-235B-A22B | Qwen3 | Reasoning, Multi-Modal | $1.42/M tokens (output) | Dual-mode with superior tool integration |
| 3 | GLM-4.5 | zai | Security Agents | $2.00/M tokens (output) | Agent-optimized security automation |
Frequently Asked Questions
Our top three picks for cybersecurity and threat analysis in 2025 are DeepSeek-R1, Qwen3-235B-A22B, and GLM-4.5. Each of these models stood out for their exceptional reasoning capabilities, security-focused optimizations, and unique approaches to solving complex threat detection and analysis challenges.
Our in-depth analysis shows different leaders for specific security needs. DeepSeek-R1 is the top choice for complex threat analysis, attack chain investigation, and sophisticated vulnerability assessment requiring deep reasoning. Qwen3-235B-A22B excels at versatile security operations with its dual-mode capability and superior integration with security tools. GLM-4.5 is ideal for organizations building autonomous security agents and automated defense systems that coordinate multiple security tools.