The Rise of Small Language Models

The Rise of Small Language Models

In this week’s issue, we’re exploring the fascinating trend of small language models (SLMs) - AI models that pack serious capability into compact packages.

Why Small Models Matter

The AI industry is witnessing a significant shift:

Model Size (params)  |  Performance  |  Deployment Cost
---------------------|---------------|-------------------
7B                   |  Good         |  $0.50/hour
70B                  |  Great        |  $5.00/hour
1.8T                 |  Excellent    |  $50.00/hour

Smaller models are:

  • Cheaper to run - No need for expensive GPU clusters
  • Faster to deploy - Can run on consumer hardware
  • More energy efficient - Lower carbon footprint
  • Easier to fine-tune - Great for specialized tasks

Notable SLMs in 2026

Here are some standout small models making waves:

  1. Phi-4 - Microsoft’s efficient reasoning model
  2. Qwen2.5-Coder - Excellent for code generation at 14B params
  3. DeepSeek-R1 - Open source reasoning model

The Trade-off

It’s not all smooth sailing. SLMs have limitations:

  • Less emergent reasoning capabilities
  • Smaller knowledge cutoff windows
  • Struggle with highly complex multi-step tasks

When to Use What

Use CaseRecommended Model
Code completionSLM (7-14B)
Chatbot UIMedium (70B)
Complex reasoningLarge (400B+)
ResearchWhatever you can afford 😅

Our Take

The future isn’t just about bigger models. The real innovation is in making AI accessible and efficient. We’re excited to see where this goes.

Next week: Open source AI regulation and what it means for developers