What are Open Source LLMs for Agent Workflows?
Open source LLMs for agent workflows are specialized large language models designed to autonomously execute complex tasks through reasoning, planning, tool use, and interaction with external environments. Unlike traditional chat models, these agent-capable LLMs can break down complex goals, make decisions, invoke functions, browse the web, write and execute code, and iteratively solve problems. They excel at function calling, API integration, and multi-step task execution. This technology enables developers to build autonomous AI agents that can handle everything from software development and data analysis to web automation and enterprise workflow orchestration, all while maintaining transparency, customization, and cost-effectiveness through open-source accessibility.
GLM-4.5-Air
GLM-4.5-Air is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture. 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.
GLM-4.5-Air: Purpose-Built Agent Foundation Model
GLM-4.5-Air is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture with 106B total parameters and 12B active 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 a 131K context window and competitive SiliconFlow pricing at $0.86/M output tokens and $0.14/M input tokens, it delivers exceptional value for agent workflows.
Pros
- Purpose-built for AI agent applications with MoE efficiency.
- Extensively optimized for tool use and web browsing.
- Seamless integration with coding agents like Claude Code.
Cons
- Smaller active parameter count than flagship models.
- May require fine-tuning for highly specialized domains.
Why We Love It
- It's the only open-source model explicitly designed from the ground up for AI agent workflows, delivering optimized tool use, web browsing, and seamless integration with coding agents—all at exceptional efficiency and cost.
Qwen3-Coder-30B-A3B-Instruct
Qwen3-Coder-30B-A3B-Instruct is a code model from the Qwen3 series developed by Alibaba's Qwen team. As a streamlined and optimized model, it maintains impressive performance and efficiency while focusing on enhanced coding capabilities. It demonstrates significant performance advantages among open-source models on complex tasks such as Agentic Coding, Agentic Browser-Use, and other foundational coding tasks.
Qwen3-Coder-30B-A3B-Instruct: Specialized Agentic Coding Powerhouse
Qwen3-Coder-30B-A3B-Instruct is a specialized code model from the Qwen3 series with 30.5B total parameters and 3.3B activated parameters. It demonstrates significant performance advantages among open-source models on complex tasks such as Agentic Coding, Agentic Browser-Use, and foundational coding tasks. The model natively supports a long context of 256K tokens (262K), which can be extended up to 1M tokens, enabling better repository-scale understanding and processing. It provides robust agentic coding support for platforms like Qwen Code and CLINE, featuring a specially designed function call format. With SiliconFlow pricing at $0.4/M output tokens and $0.1/M input tokens, it offers exceptional value for agentic coding workflows.
Pros
- State-of-the-art performance in agentic coding tasks.
- Excels at Agentic Browser-Use and tool integration.
- 256K native context, extensible to 1M tokens.
Cons
- Specialized for coding; less general-purpose than flagship models.
- Requires agentic framework integration for best results.
Why We Love It
- It's the definitive specialist for agentic coding workflows, delivering state-of-the-art performance on autonomous code generation, repository understanding, and tool-based coding—with massive context and purpose-built agent features.
Qwen3-30B-A3B-Thinking-2507
Qwen3-30B-A3B-Thinking-2507 is the latest thinking model in the Qwen3 series, released by Alibaba's Qwen team. As a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion active parameters, it is focused on enhancing capabilities for complex tasks. The model demonstrates significantly improved performance on reasoning tasks and excels in agentic capabilities.
Qwen3-30B-A3B-Thinking-2507: Advanced Reasoning for Complex Agents
Qwen3-30B-A3B-Thinking-2507 is the latest thinking model in the Qwen3 series with 30.5B total parameters and 3.3B active parameters. It demonstrates significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. The model shows markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences. It natively supports a 256K long-context understanding capability, which can be extended to 1 million tokens. This version is specifically designed for 'thinking mode' to tackle highly complex problems through step-by-step reasoning and excels in agentic capabilities. SiliconFlow pricing is $0.4/M output tokens and $0.1/M input tokens.
Pros
- Specialized 'thinking mode' for complex reasoning tasks.
- Outstanding performance in mathematical and logical reasoning.
- Excellent agentic capabilities with tool usage.
Cons
- Thinking mode may produce longer response times.
- Requires careful prompt engineering for optimal agent behavior.
Why We Love It
- It combines advanced reasoning with agentic capabilities, enabling AI agents to tackle highly complex, multi-step problems through deep, step-by-step thinking—all while maintaining tool usage, massive context, and exceptional efficiency.
Agent-Capable LLM Comparison
In this table, we compare 2025's leading open-source LLMs for agent workflows, each with a unique strength. For purpose-built agent applications, GLM-4.5-Air provides optimized tool use and web browsing. For specialized agentic coding, Qwen3-Coder-30B-A3B-Instruct delivers state-of-the-art performance. For complex reasoning agents, Qwen3-30B-A3B-Thinking-2507 offers advanced thinking capabilities. This side-by-side view helps you choose the right model for your specific agent workflow needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing (Output) | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5-Air | zai | Reasoning, MoE, 106B | $0.86/M tokens | Purpose-built agent foundation |
| 2 | Qwen3-Coder-30B-A3B-Instruct | Qwen | Coder, MoE, 30B | $0.4/M tokens | State-of-the-art agentic coding |
| 3 | Qwen3-30B-A3B-Thinking-2507 | Qwen | Reasoning, MoE, 30B | $0.4/M tokens | Advanced reasoning for agents |
Frequently Asked Questions
Our top three picks for 2025 are GLM-4.5-Air, Qwen3-Coder-30B-A3B-Instruct, and Qwen3-30B-A3B-Thinking-2507. Each of these models stood out for their agent capabilities, including tool use, function calling, reasoning, and autonomous task execution in real-world agentic applications.
Our in-depth analysis shows several leaders for different agent needs. GLM-4.5-Air is the top choice for general-purpose agent applications with extensive tool use and web browsing optimization. Qwen3-Coder-30B-A3B-Instruct is best for agentic coding workflows, excelling at autonomous code generation and repository understanding. Qwen3-30B-A3B-Thinking-2507 is ideal for agents requiring advanced reasoning and step-by-step problem solving. For maximum scale, models like Qwen3-Coder-480B-A35B-Instruct or moonshotai/Kimi-K2-Instruct offer enterprise-grade agent capabilities.