Claude: The Specialized LLM Champion, and How to Choose the Right Model

Selecting the correct Large Language Model (LLM) for each task isn’t simple. Reflecting views of many AI practitioners, Ihave developed model preferences based on experience: Claude for writing and coding, Perplexity (uting Claude as its base) for research, and o3 Mini for day-to-day tasks. This approach reflects a very important idea. No single model is ideal for every operation.

Claude Excels at Writing and Coding

Claude’s proficiency in writing stems from its power to analyze and match writing styles. Unlike other models, Claude accurately matches tones or voices when needed. This makes it helpful for content creation that calls for consistent brand voice or specialist writing styles without sounding generic.

Claude shines when used for coding because of the following:

• Updating existing code snippets with compatible solutions
• Finding and fixing errors in complex programming
• Keeping consistent coding styles throughout projects
• Detailing code logic in understandable terms

This practical coding ability makes Claude really useful, even when other models score higher on benchmarks. As I note in my coding bencmark comparison, Claude 3.7 Sonnet works far better than GPT-4.5 in real-world coding problems regardless of what theoretical benchmarks say.

Research: Why Perplexity (with Claude) is the Best Choice

I use Perplexity with Claude for research. This setup utilizes Claude’s reasoning with Perplexity’s research features to boost productivity:

This setup fixes Claude’s limits when dealing with its older knowledge while still giving it strong analytical abilities. For any problems that need current information, this blended setup produces stronger results than if someone only used Claude alone.

o3 Mini for Normal Tasks: Why it’s Effective

For basic and consistent tasks, I like o3 Mini. This choice shows an important part of using AI: the back and forth between the model’s power and its effectiveness. o3 Mini has:

• Faster response speeds versus stronger models
• Less computational demands
• Good accuracy for simple problems
• Low costs for steady, automatic tasks

This preference showcases that not every problem should be handled by a model’s best reasoning. For basic questions, simple clarifications, or tasks that repeat themselves, a lighter model’s speed and efficiency show themselves as better than those gained from stronger models.

Claude’s Capabilities: What’s Beyond the Basics

Knowing Claude’s advantages needs an examination of its technical abilities:

Reasoning

Claude works best solving problems that need multi-step thought and clear deduction. This creates the ability to:

• Break down difficult problems into smaller units
• Follow chains of logic with the model making fewer errors
• Consider a lot of different angles before coming to conclusions
• Keep clear reasoning through large pieces of context

Analysis of Images

Claude’s sight goes beyond just listing what images show but expands further:

• Makes transcriptions of handwritten notes correctly
• Analyzes charts with the correct contexts
• Pulls data from graphics
• Integrates visual information with data analysis

Speaking Multiple Languages

Though most major LLMs work in multiple languages, Claude does better with it:

• It keeps nuances between languages
• Keeps style and tone in translation
• It works well managing mixed language content
• Supports rare languages well

Why Use Other Models? Why Not Just Claude?

We are leaving behind ‘one model that resolves everything’. Using multiple models has a lot of great things to consider:

Task Optimization

Other models stand above based on differing areas. When you match the model to the problem, you will optimize:

• Good results for important problems
• Speed and efficiency for tasks that have time constraints
• Cost management for high use
• Specialized abilities for unique limits

Risk management

Using many models lowers risks, including:

• Outages from a single source
• Sudden rule changes which can affect model ability
• Price spikes due to providers acting like monopolies
• Odd model traits or limits

Putting all AI use behind one provider can make a user pay high prices for bad performance in specific areas, as seen in OpenAI: The Apple of AI.

Recommendations on Choosing Models

While deciding on a model, remember the parts that create it:

Task Analysis

Break down your requirements to see what fits along these sizes:

• Complexity: How much reasoning is needed for the model to complete the problem?
• Timeliness: Does the problem need current information, or can the state of it during training provide the right conclusion?
• Specificity: Does the problem call for a specialist, or specialist abilities?
• Volume: How often will someone attempt this problem?

Actual Testing of your Problem

Benchmarks often create performance that does not translate from testing. The most reliable way is to test multiple models on tasks. Focus on:

• Output for use cases
• Consistency across retries
• Ability to deal with edge cases
• Response speed and interaction

This matches the result of testing done in AI Model Battle: GPT-4.5 vs Claude 3.7 vs Grok 3 – Who Wins Where?, which tells us that real effectiveness differs from benchmarks.

The Future: Specialist vs. General Models

AI Keeps developing from two directions at the same time:

Specialist Models

The use of specialist models focused on specific problems continues to increase:

• Coding models which understand code completely
• Search models with better factual information
• Writing models with improved control of style
• Field-specific models for things such as medicine, law, and finance

These specialist models beat normal options inside of their niche, like ElevenLabs Scribe does by setting high-score after high-score in speech-to-text.

General Models With Tools

All-purpose models can grow in power with tools.

• Browsing allows models to find content outside of training data
• Environments to run code helps with tests and verification
• API connections helps connect models to specialized jobs/services
• Multi-modal ability which allows models to combine content from written, audio, and visual sources

These areas hint that models of the future will not act using a “best model”, but rather build powerful setups that use the right tools for each stage of use.

Model Selection: Important Parts of Prompting and Use

Picking models is just one stage. Remember what effects results:

Prompting

How problems are listed affects how models perform. You can improve prompts by:

• Listing the tasks needed, and the content format you desire
• Give context along with needed requirements
• Break big problems down into manageable steps
• Add example content when right to do so

Use Examples

AI works best when using multiple models and tools as part of organized setups. These may include:

• Claude can be used to create base content
• Content runs through a specialist model for facts and polish
• The model uses other models for different parts of the problem
• Verify steps inside important operations

This blend of things, picking good models, providing useful prompts, and creating setups, shows some ways of using AI.

Results: Use Many Models

Claude stands out by using thoughts and matching writing output. Still, It’s important to use models for different reasons:

• Claude: Writing and coding
• Perplexity (with Claude): Research to find current information while using thoughts
• o3 Mini: Day-to-day operations which profit from efficiency

Multiple models show the best traits of the moment, no model wins in every situation. To be successful, the user needs to know each model’s limits, and then match them with the correct problems. As models grow in strength, users who can match models precisely with their needs will pull ahead.

The future is not about one model. Users need to build setups that call for the right model at the right step. This can maximize the use of AI while stopping future problems that may happen, the current game plan which I am following myself.