LLMs for Documentation

In today’s AI-accelerated world, Large Language Models (LLMs) are revolutionizing how we create, manage, and enhance documentation. Let’s compare some of the leading models and tools that can supercharge your documentation workflow.

The Contestants

Qwen3

Alibaba Cloud’s powerful open language model known for its multilingual capabilities, strong reasoning, and balanced performance at smaller sizes.

Llama4

Meta’s latest open model family that delivers impressive performance across various tasks while being highly efficient, with versions suited for consumer hardware.

Mistral-small3.1

A highly efficient model from Mistral AI that offers exceptional performance relative to its size, with strong reasoning capabilities and instruction following.

Claude 3 Haiku

Anthropic’s lightweight yet capable model designed for efficiency and speed, while maintaining high levels of reasoning and helpfulness.

GPT-4o Mini

OpenAI’s smaller variant of GPT-4o, offering excellent performance with lower compute requirements, optimized for daily assistant tasks.

Key Features Comparison

Feature	Qwen3	Llama4	Mistral-small3.1	Claude 3 Haiku	GPT-4o Mini
Open Source	Yes	Yes	No (API only)	No (API only)	No (API only)
Size Options	0.5B to 72B	8B to 128B	8B	Single size	Single size
Multilingual	Excellent	Good	Good	Good	Excellent
Local Deployment	Yes	Yes	Limited	No	No
API Availability	Yes	Yes	Yes	Yes	Yes
Code Generation	Good	Good	Excellent	Good	Excellent
Markdown Support	Excellent	Excellent	Excellent	Excellent	Excellent
Context Length	32K	128K	32K	200K	128K
Reasoning	Strong	Very Strong	Strong	Very Strong	Very Strong
Specialized for Docs	No (general)	No (general)	No (general)	No (general)	No (general)

AI Documentation Capabilities Deep Dive

Capability	Qwen3	Llama4	Mistral-small3.1	Claude 3 Haiku	GPT-4o Mini
Doc Generation	Very good	Excellent	Very good	Excellent	Excellent
Code Documentation	Good	Very good	Excellent	Very good	Excellent
Technical Accuracy	Good	Very good	Very good	Very good	Very good
Content Structuring	Very good	Excellent	Very good	Excellent	Excellent
Doc Summarization	Excellent	Excellent	Very good	Excellent	Excellent
Multi-format Output	Good	Very good	Good	Very good	Excellent
Customization	Good (fine-tune)	Good (fine-tune)	Limited	Limited	Limited
API Integration	Good	Good	Excellent	Excellent	Excellent

Pros and Cons

Qwen3

Pros:

• Strong multilingual capabilities, great for international documentation
• Excellent performance at smaller sizes
• Full open-source availability for custom deployment
• Good reasoning abilities for complex documentation
• Lower compute requirements than larger models

Cons:

• Less widespread community support than Llama
• Fewer specialized tools and integrations
• Can struggle with complex technical documentation
• Sometimes generates less coherent outputs than larger models
• Requires more prompting guidance for complex tasks

Llama4

Pros:

• Best-in-class open-source model performance
• Strong reasoning capabilities across domains
• Large and active developer community
• Excellent with code documentation and technical content
• Multiple size options for different performance needs

Cons:

• Requires significant resources for larger sizes
• Can generate plausible-sounding but incorrect technical details
• Open nature can lead to inconsistent behaviors across implementations
• Less consistent than proprietary alternatives
• Requires careful prompt engineering for best results

Mistral-small3.1

Pros:

• Exceptional efficiency-to-performance ratio
• Strong technical accuracy in code and technical documentation
• Excellent API reliability and consistency
• Good instruction-following for documentation tasks
• Reliable output formatting

Cons:

• Not fully open-source for complete customization
• Limited size options compared to competitors
• API costs for production usage
• Less context length than some competitors
• Occasionally struggles with highly specialized domains

Claude 3 Haiku

Pros:

• Very fast response times for interactive documentation
• Excellent at understanding and following complex instructions
• Strong reasoning for technical content
• Reliable output quality with fewer hallucinations
• Very long context window (200K)

Cons:

• API-only access, no local deployment
• Higher API costs than some alternatives
• Limited customization options
• Less code-focused than specialized models
• Single model size with no smaller/larger options

GPT-4o Mini

Pros:

• Excellent balance of performance and efficiency
• Best-in-class instruction following
• Strong integration with existing tools
• Consistent output quality across domains
• Advanced JSON and structured output capabilities

Cons:

• Closed source with API-only access
• Higher costs for production usage
• No local deployment options
• Less customizable than open alternatives
• Usage restrictions and rate limits

Getting Started Guide

Using Qwen3 for Documentation

1. Local Setup:

   • Install Python and necessary libraries
   • Download Qwen model from Hugging Face
   • Use with llama.cpp or Ollama for consumer hardware
   • Setup command: ollama pull qwen3

2. Cloud Option:

   • Use Alibaba Cloud’s Qwen API
   • Register for an API key
   • Implement API calls in your workflow

3. Best Documentation Prompt Template:

   You are a documentation expert using Qwen3. Please [create/review/improve] documentation for [topic]. 
   Focus on [specific aspects]. 
   Format the output in markdown with clear headings, code examples, and explanations.
   

Using Llama4 for Documentation

1. Local Setup:

   • Install Ollama or llama.cpp
   • Pull Llama4 model: ollama pull llama4
   • Configure memory settings based on your hardware

2. Cloud Option:

   • Use Meta AI’s API or third-party providers
   • Implement API calls in your workflow

3. Best Documentation Prompt Template:

   You are a technical writer using Llama4. Create comprehensive documentation for [topic] with the following sections:
   1. Overview
   2. Getting Started
   3. Key Concepts
   4. API Reference
   5. Examples
   6. Troubleshooting
   
   Format in clean markdown suitable for technical documentation.
   

Using Mistral-small3.1 for Documentation

1. Setup:

   • Register for Mistral AI API access
   • Obtain API key
   • Implement API calls in your documentation workflow

2. Best Documentation Prompt Template:

   As a documentation specialist using Mistral-small3.1, please create clear, concise, and accurate documentation for [topic].
   Include:
   - Technical details with proper formatting
   - Code examples with explanations
   - Step-by-step instructions
   - Best practices
   Format in professional markdown suitable for developers.
   

Using Claude 3 Haiku for Documentation

1. Setup:

   • Sign up for Anthropic API access
   • Obtain API key
   • Implement Claude API in your documentation system

2. Best Documentation Prompt Template:

   You are a documentation expert using Claude 3 Haiku. Create comprehensive documentation for [topic].
   The documentation should be:
   - Well-structured with clear headings
   - Include relevant examples
   - Be technically accurate
   - Follow best practices for technical writing
   Format in markdown suitable for modern documentation systems.
   

Using GPT-4o Mini for Documentation

1. Setup:

   • Sign up for OpenAI API access
   • Obtain API key
   • Configure API calls to use gpt-4o-mini model

2. Best Documentation Prompt Template:

   You are an expert technical writer using GPT-4o Mini. Create professional documentation for [topic] that follows industry best practices.
   Include:
   - Clear conceptual explanations
   - Well-structured API references
   - Executable code examples
   - Common error handling
   Format in clean, well-organized markdown with proper syntax highlighting.
   

Documentation Tools Integration

All these LLMs can integrate with documentation workflows through:

1. Direct API Integration:

   • Use REST API calls from your authoring environment
   • Implement webhooks for automatic documentation updates

2. CLI Tools:

   • Use Ollama with shell scripts for local models
   • Create custom CLI tools with Python and API wrappers

3. VSCode Extensions:

   • Use Continue.dev for in-editor documentation assistance
   • Implement custom prompts in GitHub Copilot

4. Documentation Platforms:

   • Integrate with Docusaurus, GitBook, or MkDocs through plugins
   • Use LLM-powered search and suggestions

5. Automation Workflows:

   • Set up GitHub Actions to generate and update documentation
   • Implement review bots for documentation PRs

The Verdict: Which LLM Takes the Documentation Crown?

For local documentation workflows with no data sharing concerns, Llama4 offers the best combination of performance, flexibility, and community support.

For API-based documentation automation with balanced cost and performance, Mistral-small3.1 provides excellent efficiency and reliability.

For enterprise documentation with privacy and control requirements, Qwen3 offers strong multilingual support and full open-source capabilities.

For interactive documentation assistance with high-quality outputs, Claude 3 Haiku delivers fast, reliable results with exceptional reasoning.

For comprehensive documentation generation with minimal prompt engineering, GPT-4o Mini provides the most consistent quality across domains.

The best approach may be using multiple models: open models like Llama4 locally for drafting and proprietary APIs for final polishing and technical verification.

Integration with Knowledge Management Tools

When considering which LLM to use for your documentation needs, it’s important to think about how it will integrate with your knowledge management system:

• For Obsidian users: Models like Llama4 and Mistral-small3.1 work well with Obsidian’s plugin ecosystem. Learn more about Obsidian’s AI capabilities.

• For Notion users: While Notion has native AI features, you can enhance your workflow with specialized models for specific tasks. Explore Notion’s AI integration.

• For all knowledge management tools: Consider how the export formats from your chosen tool will work with different LLM systems. The cleaner and more structured your markdown, the better results you’ll get.

Join the 30-Day Challenge Compare Tools