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# Introduction
Small language models (SLMs) are quickly becoming the practical face of AI. They are getting faster, smarter, and far more efficient, delivering strong results with a fraction of the compute, memory, and energy that large models require.
A growing trend in the AI community is to use large language models (LLMs) to generate synthetic datasets, which are then used to fine-tune SLMs for specific tasks or to adopt particular styles. As a result, SLMs are becoming smarter, faster, and more specialized, all while maintaining a compact size. This opens up exciting possibilities: you can now embed intelligent models directly into systems that don’t require a constant internet connection, enabling on-device intelligence for privacy, speed, and reliability.
In this tutorial, we will review some of the top small language models making waves in the AI world. We will compare their size and performance, helping you understand which models offer the best balance for your needs.
# 1. google/gemma-3-270m-it
The Gemma 3 270M model is the smallest and most ultra-lightweight member of the Gemma 3 family, designed for efficiency and accessibility. With just 270 million parameters, it can run smoothly on devices with limited computational resources, making it ideal for experimentation, prototyping, and lightweight applications.
Despite its compact size, the 270M model supports a 32K context window and can handle a wide range of tasks such as basic question answering, summarization, and reasoning.
# 2. Qwen/Qwen3-0.6B
The Qwen3-0.6B model is the most lightweight variant in the Qwen3 series, designed to deliver strong performance while remaining highly efficient and accessible. With 600 million parameters (0.44B non-embedding), it strikes a balance between capability and resource requirements.
Qwen3-0.6B comes with the ability to seamlessly switch between “thinking mode” for complex reasoning, math, and coding, and “non-thinking mode” for fast, general-purpose dialogue. It supports a 32K context length and offers multilingual support across 100+ languages.
# 3. HuggingFaceTB/SmolLM3-3B
The SmolLM3-3B model is a small yet powerful open-source language model designed to push the limits of small-scale language models. With 3 billion parameters, it delivers strong performance in reasoning, math, coding, and multilingual tasks while remaining efficient enough for broader accessibility.
SmolLM3 supports dual-mode reasoning, allowing users to toggle between extended “thinking mode” for complex problem-solving and a faster, lightweight mode for general dialogue.
Beyond text generation, SmolLM3 also enables agentic usage with tool calling, making it versatile for real-world applications. As a fully open model with public training details, open weights, and checkpoints, SmolLM3 provides researchers and developers with a transparent, high-performance foundation for building reasoning-capable AI systems at the 3B–4B scale.
# 4. Qwen/Qwen3-4B-Instruct-2507
The Qwen3-4B-Instruct-2507 model is an updated instruction-tuned variant of the Qwen3-4B series, designed to deliver stronger performance in non-thinking mode. With 4 billion parameters (3.6B non-embedding), it introduces major improvements across instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage, while also expanding long-tail knowledge coverage across multiple languages.
Unlike other Qwen3 models, this version is optimized exclusively for non-thinking mode, ensuring faster, more efficient responses without generating reasoning tokens. It also demonstrates better alignment with user preferences, excelling in open-ended and creative tasks such as writing, dialogue, and subjective reasoning.
# 5. google/gemma-3-4b-it
The Gemma 3 4b model is an instruction-tuned, multimodal member of the Gemma 3 family, designed to handle both text and image inputs while generating high-quality text outputs. With 4 billion parameters and support for a 128K token context window, it is well-suited for tasks such as question answering, summarization, reasoning, and detailed image understanding.
Importantly, it is highly used for fine-tuning on text classification, image classification, or specialized tasks, which further improves the model’s specialization and performance for certain domains.
# 6. janhq/Jan-v1-4B
The Jan-v1 model is the first release in the Jan Family, built specifically for agentic reasoning and problem-solving within the Jan App. Based on the Lucy model and powered by the Qwen3-4B-thinking architecture, Jan-v1 delivers enhanced reasoning capabilities, tool utilization, and improved performance on complex agentic tasks.
By scaling the model and fine-tuning its parameters, it has achieved an impressive accuracy of 91.1% on SimpleQA. This marks a significant milestone in factual question answering for models of this size. It is optimized for local use with the Jan app, vLLM, and llama.cpp, with recommended settings to enhance performance.
# 7. microsoft/Phi-4-mini-instruct
The Phi-4-mini-instruct model is a lightweight 3.8B parameter language model from Microsoft’s Phi-4 family, designed for efficient reasoning, instruction following, and safe deployment in both research and commercial applications.
Trained on a mix of 5T tokens from high-quality filtered web data, synthetic “textbook-like” reasoning data, and curated supervised instruction data, it supports a 128K token context length and excels in math, logic, and multilingual tasks.
Phi-4-mini-instruct also supports function calling, multilingual generation (20+ languages), and integration with frameworks like vLLM and Transformers, enabling flexible deployment.
# Conclusion
This article explores a new wave of lightweight yet powerful open models that are reshaping the AI landscape by balancing efficiency, reasoning, and accessibility.
From Google’s Gemma 3 family with the ultra-compact gemma-3-270m-it and the multimodal gemma-3-4b-it, to Qwen’s Qwen3 series with the efficient Qwen3-0.6B and the long-context, instruction-optimized Qwen3-4B-Instruct-2507, these models highlight how scaling and fine-tuning can unlock strong reasoning and multilingual capabilities in smaller footprints.
SmolLM3-3B pushes the boundaries of small models with dual-mode reasoning and long-context support, while Jan-v1-4B focuses on agentic reasoning and tool use within the Jan App ecosystem.
Finally, Microsoft’s Phi-4-mini-instruct demonstrates how 3.8B parameters can deliver competitive performance in math, logic, and multilingual tasks through high-quality synthetic data and alignment techniques.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
