LLM Comparison: Summary and Use Cases
With so many large language models (LLMs) available, selecting the right one depends on your specific needs. Whether you're coding, analysing documents, working within a team, or managing costs, each model offers unique strengths. Here's a quick guide to help you decide which LLM best fits your use case.
Quick Comparison
LLM | Best For | Standout Feature | Pricing Value |
ChatGPT | Code generation and plugin ecosystem | Plugin versatility | Moderate |
Claude | Document processing | Computer use simulation | Moderate |
DeepSeek | Customization and budget-conscious users | Open-source flexibility | High |
Gemini | Google ecosystem integration | Real-time data integration | Moderate |
Detailed Strengths
ChatGPT
- Excellent for developers needing code assistance
- Strong plugin ecosystem extends functionality
- Good balance of features for general purpose use
- Shared chats enable team collaboration
Claude
- Strong processing of various document formats
- Computer use simulation for automation workflows
- Clear and contextual understanding of complex documents
- Good at parsing academic and technical content
DeepSeek
- Highly customizable due to open-source nature
- Most cost-effective API pricing
- Community contributions enhance capabilities
- Flexibility for specialized applications
Gemini
- Best integration with Google workspace
- Superior handling of spreadsheets and Office files
- Real-time web search provides current information
- Strong visual content processing
When to Choose Each LLM
Choose ChatGPT when: - You need extensive code assistance - You want access to a wide range of plugins - Team collaboration is important
Choose Claude when: - You work with complex document formats - You need to process academic or technical content - You require computer use simulation capabilities
Choose DeepSeek when: - Budget constraints are a primary concern - You need to customize the model for specific applications - Open-source flexibility is important
Choose Gemini when: - You work primarily in the Google ecosystem - You process Excel spreadsheets frequently - Real-time information access is critical
Recent AI Posts
The conversation around AI security has, until now, been dominated by one major theme: data privacy. Business leaders are rightly concerned about whether their confidential data will be misused or leaked by AI providers. As we've discussed previously, this risk is manageable with the right contracts and deployment models.
But a new, more insidious threat is emerging, and it has nothing to do with a provider's privacy policy.
What if the biggest risk isn't the AI model itself, but the data you ask it to read? This new class of vulnerability, known as Indirect Prompt Injection, can turn your trusted AI assistant into an unwitting insider threat. This guide explains the risk in simple business terms and outlines the practical steps you need to take to protect your organisation.
For any business leader exploring AI, data privacy is a primary concern. Headlines about security risks can create significant Fear, Uncertainty, and Doubt (FUD), making you hesitate to use powerful Large Language Models (LLMs) with your company's confidential information.
Let's be direct: for businesses, the widely discussed fear of a major provider like Microsoft or OpenAI misusing your data is largely a myth, backed by strong legal and technical protections. However, this doesn't mean there are no risks. Real, serious risks do exist—they just aren't the ones the headlines focus on.
We understand that the perception of risk among your team and customers is a business challenge in itself. This guide provides a practical framework to address those fears, separate the myths from reality, and focus on mitigating the risks that truly matter.
You've decided that using AI will be useful to your business. Now you face a critical and confusing decision: which Large Language Model (LLM) should power your project? In a landscape dominated by names like ChatGPT, Claude, Gemini, and DeepSeek, choosing the right engine is crucial for success. Selecting the wrong one can lead to budget overruns, poor performance, or a solution that simply doesn’t meet your needs.
The technical choice is actually a strategic business decision. The guide below provides a clear comparison, focusing on the practical differences that matter most to your project's outcome and its ROI.
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