What are Open Source LLMs Under 20B Parameters?
Open source LLMs under 20B parameters are lightweight large language models that deliver powerful AI capabilities while maintaining computational efficiency. These models—typically ranging from 7B to 9B parameters—are designed to run on more accessible hardware without sacrificing performance in key areas like reasoning, coding, multilingual understanding, and dialogue. By leveraging advanced training techniques and architectural innovations, they democratize access to state-of-the-art AI, enabling developers and businesses to deploy sophisticated language models in resource-constrained environments. These models foster collaboration, accelerate innovation, and provide cost-effective solutions for a wide range of applications from chatbots to enterprise automation.
Qwen3-8B
Qwen3-8B is the latest large language model in the Qwen series with 8.2B 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, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning.
Qwen3-8B: Dual-Mode Reasoning Powerhouse
Qwen3-8B is the latest large language model in the Qwen series with 8.2B 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, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. Additionally, it supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities. With a massive 131K context length, Qwen3-8B handles long documents and extended conversations with ease, making it ideal for complex reasoning tasks and multilingual applications.
Pros
- Dual-mode operation: thinking mode for complex reasoning, non-thinking for efficiency.
- Superior performance in math, coding, and logical reasoning.
- Supports over 100 languages and dialects.
Cons
- Text-only model without native vision capabilities.
- May require mode switching optimization for specific use cases.
Why We Love It
- It delivers cutting-edge reasoning capabilities with seamless mode switching, making it the most versatile 8B model for both complex problem-solving and efficient everyday dialogue across 100+ languages.
GLM-Z1-9B-0414
GLM-Z1-9B-0414 is a small-sized model in the GLM series with only 9 billion parameters that maintains the open-source tradition while showcasing surprising capabilities. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent performance in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size.
GLM-Z1-9B-0414: Compact Mathematical Reasoning Expert
GLM-Z1-9B-0414 is a small-sized model in the GLM series with only 9 billion parameters that maintains the open-source tradition while showcasing surprising capabilities. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent performance in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. The research team employed the same series of techniques used for larger models to train this 9B model. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment. The model features deep thinking capabilities and can handle long contexts through YaRN technology, making it particularly suitable for applications requiring mathematical reasoning abilities with limited computational resources. With a 33K context length and competitive pricing at $0.086/M tokens on SiliconFlow, it offers exceptional value.
Pros
- Exceptional mathematical reasoning for a 9B model.
- Deep thinking capabilities with YaRN technology.
- Leading performance among same-size open-source models.
Cons
- Slightly higher pricing than some alternatives at $0.086/M tokens on SiliconFlow.
- More specialized for reasoning than general-purpose dialogue.
Why We Love It
- It punches above its weight with mathematical reasoning capabilities that rival much larger models, making it the go-to choice for computational tasks in resource-constrained environments.
Meta-Llama-3.1-8B-Instruct
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.
Meta-Llama-3.1-8B-Instruct: Industry Benchmark Leader
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. With its 33K context length and competitive $0.06/M token pricing on SiliconFlow, this model represents Meta's commitment to open-source AI excellence. It excels in multilingual conversations, code generation, and instruction-following tasks, making it ideal for chatbots, content generation, and multilingual applications.
Pros
- Outperforms many open-source and closed models on benchmarks.
- Trained on over 15 trillion tokens for robust performance.
- Optimized for multilingual dialogue and instruction-following.
Cons
- Knowledge cutoff of December 2023 may limit recent information.
- 33K context length is smaller than some competitors.
Why We Love It
- Backed by Meta's extensive resources and trained on a massive dataset, it delivers benchmark-leading performance for multilingual dialogue and instruction-following tasks at an unbeatable price point.
LLM Model Comparison
In this table, we compare 2025's leading open source LLMs under 20B parameters, each with a unique strength. For advanced reasoning with dual-mode capability, Qwen3-8B provides unmatched versatility. For mathematical reasoning in constrained environments, GLM-Z1-9B-0414 offers specialized deep thinking capabilities, while Meta-Llama-3.1-8B-Instruct excels in multilingual dialogue with industry-leading benchmarks. This side-by-side view helps you choose the right lightweight model for your specific development or deployment goal.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen3-8B | Qwen3 | Chat | $0.06/M Tokens | Dual-mode reasoning, 131K context |
| 2 | GLM-Z1-9B-0414 | THUDM | Chat with Reasoning | $0.086/M Tokens | Mathematical reasoning expert |
| 3 | Meta-Llama-3.1-8B-Instruct | meta-llama | Chat | $0.06/M Tokens | Benchmark-leading multilingual |
Frequently Asked Questions
Our top three picks for 2025 are Qwen3-8B, GLM-Z1-9B-0414, and Meta-Llama-3.1-8B-Instruct. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in reasoning, multilingual dialogue, and resource-efficient deployment while staying under 20B parameters.
Our in-depth analysis shows several leaders for different needs. Qwen3-8B is the top choice for versatile reasoning with its dual-mode capability and 131K context length, ideal for complex problem-solving and long-form content. GLM-Z1-9B-0414 excels in mathematical reasoning and deep thinking tasks. Meta-Llama-3.1-8B-Instruct is the benchmark leader for multilingual dialogue and instruction-following, making it perfect for chatbots and conversational AI applications.