What are Open Source LLMs for Virtual Assistants?
Open source LLMs for virtual assistants are specialized Large Language Models designed to power conversational AI systems that can understand, respond to, and assist users with various tasks. These models excel in natural dialogue, instruction following, tool integration, and multi-turn conversations. Using advanced deep learning architectures including Mixture-of-Experts (MoE) designs, they enable developers to build virtual assistants that can schedule appointments, answer questions, control smart devices, provide recommendations, and perform complex reasoning tasks. Open source models foster innovation, accelerate deployment, and democratize access to powerful conversational AI, enabling a wide range of applications from customer service bots to personal productivity assistants and enterprise AI agents.
Qwen3-30B-A3B-Instruct-2507
Qwen3-30B-A3B-Instruct-2507 is an updated Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion activated parameters. This version features significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. It shows substantial gains in long-tail knowledge coverage across multiple languages and offers markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. The model supports 256K long-context understanding, making it ideal for virtual assistants that need to maintain extended conversations and complex task contexts.
Qwen3-30B-A3B-Instruct-2507: Enhanced Virtual Assistant Excellence
Qwen3-30B-A3B-Instruct-2507 is the updated version of the Qwen3-30B-A3B non-thinking mode. It is a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion activated parameters. This version features key enhancements, including significant improvements in general capabilities such as instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. It also shows substantial gains in long-tail knowledge coverage across multiple languages and offers markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. Furthermore, its capabilities in long-context understanding have been enhanced to 256K. This model supports only non-thinking mode and does not generate thinking blocks in its output, making it perfect for responsive virtual assistant applications. With SiliconFlow pricing at $0.4/M output tokens and $0.1/M input tokens, it offers excellent value for production deployments.
Pros
- Excellent instruction following and tool usage for virtual assistants.
- Strong multilingual support across 100+ languages.
- Enhanced 256K context for extended conversations.
Cons
- Does not support thinking mode for complex reasoning tasks.
- May require fine-tuning for highly specialized domains.
Why We Love It
- It delivers the perfect balance of instruction following, tool integration, and conversational quality needed for production-ready virtual assistants, with efficient resource usage and strong multilingual capabilities.
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 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 various agent frameworks. The model employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of application scenarios—from complex reasoning tasks to everyday conversational use cases, making it ideal for versatile virtual assistant deployments.
GLM-4.5-Air: AI Agent-Optimized Virtual Assistant
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. This makes it exceptionally well-suited for virtual assistants that need to perform multi-step tasks, interact with external tools, and handle both simple queries and sophisticated workflows. The model supports 131K context length and is available on SiliconFlow at $0.86/M output tokens and $0.14/M input tokens.
Pros
- Specifically optimized for AI agent and tool use scenarios.
- Hybrid reasoning approach for versatile task handling.
- Excellent integration with developer tools and frameworks.
Cons
- May be overspecialized for simple conversational tasks.
- Requires proper tool integration setup for full capabilities.
Why We Love It
- It's purpose-built for AI agent applications, making it the ideal choice for virtual assistants that need to autonomously perform tasks, use tools, and handle complex multi-step workflows with minimal human intervention.
Meta-Llama-3.1-8B-Instruct
Meta Llama 3.1 8B Instruct is a multilingual large language model optimized for dialogue use cases. With 8 billion parameters, this instruction-tuned model outperforms many available open-source and closed chat models on common industry benchmarks. Trained on over 15 trillion tokens using supervised fine-tuning and reinforcement learning with human feedback, it delivers exceptional helpfulness and safety. The model excels in multilingual conversations, supporting numerous languages while maintaining strong performance in text and code generation, making it an accessible yet powerful choice for virtual assistant deployments.
Meta-Llama-3.1-8B-Instruct: Efficient Multilingual Virtual Assistant
Meta Llama 3.1 is a family of multilingual large language models developed by Meta, featuring pretrained and instruction-tuned variants in 8B, 70B, and 405B parameter sizes. This 8B instruction-tuned model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed chat models on common industry benchmarks. The model was trained on over 15 trillion tokens of publicly available data, using techniques like supervised fine-tuning and reinforcement learning with human feedback to enhance helpfulness and safety. Llama 3.1 supports text and code generation, with a knowledge cutoff of December 2023. Its 33K context length and 8B parameter efficiency make it ideal for virtual assistants that require fast responses, multilingual support, and cost-effective deployment. Available on SiliconFlow at just $0.06/M tokens for both input and output, it offers exceptional value for high-volume assistant applications.
Pros
- Highly efficient 8B parameter model for fast inference.
- Strong multilingual dialogue capabilities.
- Excellent benchmark performance vs. larger models.
Cons
- Knowledge cutoff of December 2023 may limit current events.
- Smaller context window (33K) compared to newer models.
Why We Love It
- It offers the best price-to-performance ratio for virtual assistants, delivering strong multilingual dialogue capabilities and safety-aligned responses at a fraction of the cost of larger models, making it perfect for scaling assistant applications.
Virtual Assistant LLM Comparison
In this table, we compare 2025's leading open source LLMs for virtual assistants, each with a unique strength. Qwen3-30B-A3B-Instruct-2507 excels in instruction following and tool usage, GLM-4.5-Air is optimized for AI agent workflows, and Meta-Llama-3.1-8B-Instruct provides efficient multilingual dialogue. This side-by-side view helps you choose the right model for your virtual assistant deployment based on capabilities, context length, and SiliconFlow pricing.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen3-30B-A3B-Instruct-2507 | Qwen | Chat / Assistant | $0.4/$0.1 per M tokens | Enhanced instruction following & 256K context |
| 2 | GLM-4.5-Air | zai | Chat / AI Agent | $0.86/$0.14 per M tokens | AI agent optimization & tool integration |
| 3 | Meta-Llama-3.1-8B-Instruct | Meta | Chat / Multilingual | $0.06/$0.06 per M tokens | Cost-effective multilingual dialogue |
Frequently Asked Questions
Our top three picks for 2025 are Qwen3-30B-A3B-Instruct-2507, GLM-4.5-Air, and Meta-Llama-3.1-8B-Instruct. Each of these models stood out for their innovation, conversational performance, and unique approach to solving challenges in virtual assistant applications—from instruction following and tool integration to multilingual dialogue and cost-effective deployment.
Our in-depth analysis shows several leaders for different needs. Qwen3-30B-A3B-Instruct-2507 is the top choice for production virtual assistants requiring excellent instruction following, tool usage, and long-context conversations with 256K support. For AI agent-based assistants that need to autonomously perform tasks and integrate with external tools, GLM-4.5-Air is the best option. For cost-sensitive deployments requiring multilingual support and high-volume conversations, Meta-Llama-3.1-8B-Instruct offers the best value at just $0.06/M tokens on SiliconFlow.