Model Guide

One of our core-values is to build a tool which is model-agnostic. This means we do not want to restrict users to models from just one provider, but rather allow them to choose which model from which provider to use. Each model has different strengths and we encourage you to test the different models to find the best models for your specific need.

Selecting a Model

• Whenever you start a new chat, you can select which model to use from the dropdown in the top left
• You can switch models mid-conversation. For example, start with a fast model for brainstorming, then switch to a more powerful one for the final output
• Set your personal default model in account settings

Auto mode

Auto is Langdock’s intelligent model routing option. Instead of choosing a model for every conversation, Auto analyzes your first message to understand the request and estimate its complexity, and then selects a suitable model for that conversation.

Auto is designed for everyday work: quick questions, drafting, summarizing, rewriting, and analysis. By handling the model choice automatically, it helps teams work faster and avoid spending usage on powerful models for tasks that do not need them. For tasks that require maximum reasoning depth or quality, you can still switch to a specific model at any point.

Once a model is selected, it stays fixed for the rest of the conversation. Switching models mid-conversation would require resending the full conversation history to the new model, which would increase your token usage.

Understanding Model Naming Conventions

AI providers follow consistent naming patterns that help you quickly identify a model’s capabilities. Understanding these patterns lets you choose the right model without memorizing specific versions.

Version Numbers = Capability Level

Higher version numbers generally indicate newer, more capable models. When a provider releases a new generation, they increment the major version number.

Pattern	What it means
GPT-5 vs GPT-4	GPT-5 is the newer generation
Claude 4 vs Claude 3	Claude 4 is the newer generation
Gemini 2.5 vs Gemini 2.0	Gemini 2.5 is newer within the same generation

When in doubt, choose the model with the higher version number. It typically has better reasoning, fewer errors, and more capabilities.

Size Indicators = Speed vs Intelligence Trade-off

Providers offer multiple sizes within each model family. Models without size indicators are the most intelligent but may be slower. Models with size indicators trade some capability for speed and cost efficiency.

Indicator	Intelligence	Speed	Best for
No indicator (e.g., “GPT-5”, “Claude Sonnet”)	Highest	Moderate	Complex tasks, important outputs
mini / nano	Medium-High	Fast	Everyday tasks, quick iterations
flash / fast	Medium	Very Fast	Real-time applications, high volume
haiku (Anthropic)	Good	Very Fast	Simple tasks, cost-sensitive use cases

Pro tip: Start with a faster model for drafts and exploration, then switch to the full model for your final output. This saves time while still getting high-quality results when it matters.

Thinking

Reasoning or Thinking capabilities are available through a toggle directly in the model selector. When enabled, the model reasons through problems step-by-step before responding, producing more accurate results on complex tasks, at the cost of a longer response time.

Thinking is well-suited for:

• Complex multi-step problems
• Mathematical and scientific analysis
• Logical deduction and planning
• Code architecture decisions

Some models are by default thinking models (like GPT-5.1 Thinking Fast or OpenAI’s o-series). Thinking is always active on them, with no separate toggle.

Provider Tiers

Each provider organizes their models into tiers:

OpenAI

Tier	Examples	Use Case
Flagship	GPT-5, GPT-5.x	Most capable, best for complex tasks
Reasoning	o-series (o3, o4)	Deep analytical tasks
Efficient	mini, nano variants	Fast, cost-effective

Anthropic

Tier	Examples	Use Case
Opus	Claude Opus	Most intelligent, complex reasoning
Sonnet	Claude Sonnet	Balanced intelligence and speed
Haiku	Claude Haiku	Fast, efficient for simpler tasks

Google

Tier	Examples	Use Case
Pro	Gemini Pro	Most capable, complex tasks
Flash	Gemini Flash	Fast, real-time applications

Others

Provider	Flagship	Notes
Mistral	Mistral Large	Strong multilingual, coding
Meta	LLaMA	Open-source, efficient
DeepSeek	DeepSeek R1	Strong reasoning, coding

Choosing the Right Model

By Task Type

Task	Recommended Model Type	Why
Quick questions, brainstorming	Fast/mini variants	Speed matters, good enough quality
Writing emails, documents	Standard flagship	Good balance of quality and speed
Complex analysis, research	Flagship with Thinking enabled	Need highest accuracy
Coding and debugging	Anthropic Sonnet	Strong at structured tasks
Creative writing	Anthropic models	Known for natural, authentic tone
Long documents	Google Gemini	Excellent long-context handling
Math and science	Any model with Thinking enabled	Step-by-step problem solving

Quick Decision Guide

Not sure where to start?
└─ Use Auto → Langdock picks the right model for you

Know what you need?
├─ Simple, quick task → Use a fast/mini/flash model
└─ Complex task
    ├─ Deep reasoning required → Use a flagship model with Thinking enabled
    └─ Otherwise → Use the standard flagship model

Our Recommendations

Choose Auto mode

Not sure which model to pick? Select Auto from the model selector. Langdock analyzes your message and routes it to the right model. Fast and efficient for everyday tasks, and more capable when the request demands it.

For Everyday Tasks

Use the current flagship model from OpenAI or Anthropic. These provide the best balance of capability and speed for general use. Look for models without size indicators (no “mini”, “fast”, etc.).

For Coding and Writing

Anthropic’s Sonnet models are consistently praised for natural-sounding text and strong coding capabilities. They have an authentic tone that works well for professional communication.

For Complex Reasoning

Enable Thinking on a flagship model when you need maximum accuracy on analytical tasks. This takes longer but significantly reduces errors on complex problems.

For Speed-Sensitive Tasks

Flash, mini, or nano variants deliver good results much faster. Perfect for real-time applications, iterating on ideas, or processing high volumes.

Your model choice also affects how quickly you use your included usage limits. More powerful models consume more per interaction. See Fair Usage Policy for details.

Image Generation Models

Image models also follow naming patterns:

Provider	Models	Strengths
Black Forest Labs	Flux series	State-of-the-art quality, fast generation
Google	Imagen series	Diverse art styles, photo realism
OpenAI	DALL-E, GPT Image	Text-to-image, integrated with chat

For image generation, “Fast” variants prioritize speed while standard versions prioritize quality. Choose based on whether you need quick iterations or final-quality output.

Staying Current

AI models evolve rapidly. To stay current:

1. Check the model selector - Langdock always shows the latest available models
2. Look for version numbers - Higher numbers = newer capabilities
3. Try new models - When a new version appears, test it on your typical tasks
4. Read release notes - Providers announce major improvements with new releases

Langdock continuously adds new models as they become available. The model selector in your chat always reflects the current options.

FAQ

Which model should I use for long or complex tasks?

Use stronger models for complex reasoning, long context, coding, analysis, or tasks where quality matters more than speed or cost. Use smaller or faster models for routine writing, summarization, and simpler tasks.

Why do model capabilities and limits differ?

Models come from different providers and deployments. They can differ in context size, output length, supported modalities, speed, availability, cost, and regional deployment. These differences can affect the same prompt’s result.

When are newly released models available in Langdock?

Langdock aims to offer new models as soon as they are released by the provider. However, new models may require regional deployment, safety review, pricing setup, and product integration before they appear. Availability can therefore differ from a provider’s public launch date.