DistilBERT, ALBERT, and Beyond: Comparing Top Small Language Models

As Large Language Models (LLMs) continue to grow in complexity and computational cost, a new class of efficient, lightweight alternatives is gaining traction — Small Language Models (SLMs). These compact models strike a balance between performance and efficiency, making them ideal for on-device inference, low-latency applications, and deployments in resource-constrained environments.

In this article, we’ll compare several leading SLMs including DistilBERT, ALBERT, TinyBERT, MiniLM, and newer entrants released in 2024–2025. We’ll look at their architectures, strengths, performance metrics, and ideal use cases, with accompanying diagrams and graphs to visualize the trade-offs.

1. Why Small Language Models Matter

With growing concerns around the carbon footprint of training and deploying massive models, SLMs offer:

• Lower computational and memory requirements

• Faster inference speeds

• Improved deployability on edge devices

• Lower cost of ownership for enterprises

2. The Contenders

🧠 DistilBERT (Hugging Face)

• Released: 2019

• Size: ~66M parameters

• Architecture: 6-layer Transformer distilled from BERT-base

• Highlights: Retains 97% of BERT’s performance with 60% fewer parameters and 2x speed boost

• Use Cases: Chatbots, QA systems, mobile NLP

🧠 ALBERT (Google Research)

• Released: 2019

• Size: Varies (ALBERT-base ~12M parameters)

• Architecture: Factorized embedding parameterization + parameter sharing

• Highlights: Extremely parameter-efficient with comparable performance to BERT

• Use Cases: Text classification, intent detection, academic NLP research

🧠 TinyBERT (Huawei)

• Released: 2020

• Size: ~14.5M parameters

• Architecture: Distilled version of BERT with layer-wise distillation

• Highlights: Optimized for speed and mobile deployment

• Use Cases: On-device NLP, customer service bots

🧠 MiniLM (Microsoft)

• Released: 2020

• Size: ~33M parameters

• Architecture: Deep self-attention distillation with small Transformer layers

• Highlights: Outperforms DistilBERT and TinyBERT in many benchmarks

• Use Cases: Embedding generation, search, language understanding

🧠 Newcomers (2024–2025)

• Examples: MobileGPT, LiteLLM, Firefly-Tiny

• Innovations: Use of INT4 quantization, low-rank adapters, edge-optimized training

• Trends: Open-source models tailored for specific hardware (ARM, NPUs)

3. Performance Comparison

Model	Params	Size (MB)	GLUE Score	Inference Speed	Target Platform
DistilBERT	66M	~256 MB	~79.1	Fast	Cloud/Mobile
ALBERT Base	12M	~45 MB	~80.1	Medium	Cloud
TinyBERT	14.5M	~60 MB	~76.5	Very Fast	Mobile/Edge
MiniLM	33M	~120 MB	~81.0	Fast	Cloud/Edge
MobileGPT	8M	~30 MB	~77.3	Very Fast	On-device

4. Key Factors to Consider

• Model Size: Determines whether a model can run on device or needs server-side processing

• Training Cost: Smaller models train faster and cheaper

• Latency & Speed: Crucial for user-facing applications

• Accuracy: Slight trade-offs compared to full LLMs, but still usable in many domains

5. Use Cases by Model

• DistilBERT: Virtual assistants, text summarization in enterprise software

• ALBERT: Academic datasets, semantic search, email classification

• TinyBERT: Language apps, offline translation

• MiniLM: Search engines, recommender systems, data labeling

• MobileGPT / LiteLLM: Smart wearables, automotive assistants, chat features in mobile apps

6. Tools for Working with SLMs

• Hugging Face Transformers & Optimum – Load optimized models with ONNX or TorchScript

• TensorFlow Lite & PyTorch Mobile – Deploy to Android and iOS

• NVIDIA TensorRT & OpenVINO – Accelerate inference for edge computing

7. Training Techniques That Enable SLMs

• Knowledge Distillation: A smaller “student” model learns from a larger “teacher” model.

• Weight Sharing: Reduces parameter count without sacrificing too much performance.

• Quantization: Reducing precision (e.g., FP32 → INT8) to save memory and improve speed.

• Pruning: Eliminating less important neurons or weights from the model.

8. Energy and Cost Comparison

Model	Training Cost Estimate	Inference Cost (per 1M tokens)	Energy Usage
GPT-3	$4.6M+	$0.005/token	Very High
DistilBERT	~$50K	$0.0003/token	Low
TinyBERT	~$35K	$0.0002/token	Very Low

9. Case Study: MobileGPT in Healthcare

A European healthtech startup deployed MobileGPT for offline medical query handling in rural clinics with no internet access. The SLM delivered 85% accuracy in field trials and reduced dependency on cloud APIs, cutting monthly operational costs by 40%.

10. Deployment Environments

• DistilBERT: iOS, Android, Raspberry Pi (via PyTorch Mobile)

• MiniLM: Browser-based apps using ONNX.js

• LiteLLM: NPU-accelerated chips (e.g., Apple M-series)

11. Roadmap of Small Language Model Evolution

2018: BERT
2019: DistilBERT, ALBERT
2020: TinyBERT, MiniLM
2022: MobileBERT
2024: MobileGPT, LiteLLM
2025: Firefly-Tiny, Whisper-Tiny

12. Future Trends in SLMs

Trend	Description	Expected Impact
Domain-specific SLMs	Fine-tuned for legal, medical, or finance tasks	Higher accuracy, fewer hallucinations
Local inference agents	Embedded in apps without internet dependency	Greater privacy, low latency
Self-updating models	Edge models that retrain using local data	Personalization at scale

13. Right SLM based on Use Case

Use Case	Recommended SLMs
Document Summarization	Phi-3 Mini, Qwen 2
Text Generation & Translation	TinyLlama, Qwen 2
Conversational AI	Gemma-2, StableLM Zephyr 3B
Instructional Content Creation	StableLM Zephyr 3B
Resource-Constrained Environments	Phi-3 Mini, Qwen 2

14. Leading small language models in 2025 for tasks like summarization

Several small language models are leading in 2025 for summarization tasks, offering a balance of efficiency, speed, and accuracy suitable for both cloud and on-device applications. The most prominent models include:

• Qwen2 (7B): The 7B parameter version of Qwen2 is highlighted as particularly robust for summarization and text generation, providing scalable performance while remaining efficient enough for many practical applications. There are also lighter variants (0.5B, 1B) for even more resource-constrained environments, but the 7B model is preferred for higher-quality summarization.

• Phi-3.5 (3.8B): Known for its exceptionally long 128K token context window, Phi-3.5 can handle summarization of lengthy documents and multi-turn conversations without losing context. Its multilingual capabilities also make it suitable for summarizing content in various languages.

• StableLM-Zephyr (3B): This model is optimized for fast inference and accuracy, performing well in environments where quick summarization is needed, such as edge devices or real-time systems.

• Llama 2 (7B): Meta’s Llama 2 (7B) is widely used for summarization, comprehension, and general text generation. It features a doubled context length compared to its predecessor and is trained on a vast dataset, making it highly effective for summarization tasks.

• Falcon Lite (7B): Falcon Lite is praised for its speed and cost-effectiveness, leveraging advanced inference techniques and a large training set to deliver strong summarization performance, especially in deployment scenarios where efficiency is critical.

• Mistral 7B: While specialized for STEM and complex reasoning, Mistral 7B’s long context window (32K tokens) also makes it a strong choice for summarizing technical or scientific content.

• LaMini-GPT (774M–1.5B): Designed through knowledge distillation, LaMini-GPT is compact and efficient, excelling at instruction-following and multilingual summarization in resource-constrained environments.

• MiniCPM (1B–4B): MiniCPM offers a strong balance of performance and efficiency, particularly for English and Chinese summarization, and is optimized for use in limited-resource settings.

• Llama-3.2-1B: The smallest Llama model, Llama-3.2-1B, is specifically noted for general-purpose NLP tasks, including summarization, and benefits from a longer context window and a robust fine-tuning ecosystem.

• FLAN-T5-Small (60M): While much smaller, FLAN-T5-Small is recognized for its few-shot learning abilities and can be fine-tuned for summarization, especially in domain-specific or low-resource scenarios.

These models are open source or available under permissive licenses, making them accessible for a wide range of applications. Their strengths lie in their ability to deliver high-quality summarization without the computational demands of large language models, making them ideal for real-time, on-device, or resource-limited use cases.

Conclusion

Small Language Models like DistilBERT and MiniLM offer an efficient middle ground between performance and deployability. As AI pushes further into mobile, embedded, and privacy-conscious spaces, the importance of SLMs will only grow.