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Tasks like planning a trip, debugging code, analyzing a chart, or summarizing a legal document typically require using different tools or having domain expertise. Nowadays, thanks to recent AI advancements, a single large language model (LLM) can assist with all of these tasks.
An LLM is a type of AI model that has been trained to understand and generate human language. It learns by analyzing vast amounts of text (books, websites, conversations, and more) to recognize patterns related to how people write and speak. Once trained, an LLM can answer questions, write code, summarize documents, and perform many other language-based tasks, often with little instruction.
One company building these types of models is Anthropic. Founded in 2021 by a group of former OpenAI employees, Anthropic focuses on creating AI systems that are safe, reliable, and easy to work with. Their latest release is the Claude 4 model family, which includes two versions: Claude Opus 4 and Claude Sonnet 4.
Released on May 22, 2025, Claude Opus 4 is built for more complex tasks that require deep reasoning and sustained focus, like working through large codebases or conducting in-depth research. In one test, it was even able to play Pokémon Red by creating and referencing its own memory files, generating a navigation guide mid-game to help it stay on track.
Claude Sonnet 4, while not as powerful, is faster and more efficient, making it a reliable choice for everyday tasks like writing, summarizing, and general problem-solving. In this article, we’ll take a look at Claude 4’s key features and where it’s making an impact. Let’s get started!
An overview of large language models (LLMs)
Before we dive into Claude 4 and its features, let’s walk through how large language models are being used in the real world.
Most cutting-edge LLMs are built on a machine-learning architecture called a transformer, which helps them understand relationships between words across lengthy pieces of text. This makes it possible for them to do more than just autocomplete sentences - they can summarize documents, write code, answer questions, and translate languages.
In fact, a key strength of LLMs is their flexibility. Once trained, they can be used to perform a wide range of tasks with little or no additional tuning. This makes them useful in applications from customer support and education to software development, content creation, and research.
As AI adoption increases, LLMs are helping customer service teams automate responses, supporting students with tutoring tools, assisting developers inside coding environments like VS Code, and letting professionals sift through contracts, reports, and data easily. Meanwhile, some LLMs are being integrated into AI agents that can carry out multi-step tasks like planning, research, or writing workflows.
The evolution of Claude LLMs
Anthropic’s Claude models have steadily improved in terms of speed, reasoning, and overall capability with each release. Here's a quick overview of how the Claude family has evolved leading up to Claude 4:
Claude Instant 1.2, 2, and 2.1: These early models were designed for cost-effective, fast responses. Claude 2.1 introduced support for 200,000-token contexts (meaning it could handle long inputs, such as full transcripts, in a single interaction).
Claude 3 Haiku and 3.5 Haiku: They were lightweight models optimized for speed and efficiency. They were ideal for real-time applications like summarization, basic chat, and customer support.
Claude 3 Sonnet and 3.5 Sonnet: Both were balanced models that offered strong performance without sacrificing speed. With support for large prompts and long outputs, these models were well-suited for various business use cases.
Claude 3 Opus: It was a high-performance model designed for complex, reasoning-heavy tasks. Though slower and more resource-intensive, Opus delivered detailed, accurate responses, making it a good fit for research, strategy, and creative work.
Claude 3.7 Sonnet: It was the most advanced Claude model till the launch of Claude 4. It introduced an extended thinking mode for more in-depth responses, improved consistency on longer tasks, and was ideal for advanced programming, detailed analysis, and long-form writing.
Getting to know Anthropic’s Claude 4
Claude 4 changes the narrative surrounding how large language models are designed to handle complex, long-running tasks. Rather than focusing solely on speed or output quality, Anthropic’s latest models, Claude Opus 4 and Claude Sonnet 4, aim to support sustained reasoning, improved context handling, and more dependable performance.
For example, Claude 4 models think more carefully and avoid using shortcuts or tricks to finish tasks. As a matter of fact, they’re 65% less likely to do so compared to earlier versions like Sonnet 3.7.
Another key feature in both models is extended thinking, which allows them to pause and consider multiple steps before responding. This makes Claude 4 especially useful in situations where thoughtful, step-by-step reasoning matters, such as navigating branching tasks, planning multi-stage processes, or writing structured content.
Also, Claude Opus 4 introduces improved memory capabilities. When developers provide access to local files, the model can create and reference persistent memory files to keep track of key details across sessions.
Both models are also built to work with external tools. Claude 4 can connect to APIs and file systems using a concept called the Model Context Protocol (MCP). This enables developers to create AI systems that can generate responses, interact with real-world data, run background tasks, or use custom tools as part of a workflow.
Applications of the Claude 4 AI model
Concepts like agentic AI and the Model Context Protocol are central to how Claude 4 is meant to be used. These models aren’t just built to respond to prompts - they’re designed to take on more involved tasks, connect with tools, and operate as part of larger systems.
Next, let’s explore how Claude 4 can be used in applications like coding and image analysis.
A look at Claude Opus 4’s coding capabilities
Writing clean, reliable code can be challenging at times, even for experienced developers. That’s why pair programming, where one person writes and the other reviews, has been a trusted approach for many years. With AI models like Claude Opus 4, developers can now get similar support from an intelligent assistant.
Claude Opus 4 is built to handle complex coding projects. It scores well on benchmarks like SWE-bench, which checks how well an AI model can fix real bugs in open-source code, and Terminal-bench, which tests how it handles tasks in a command-line environment. Interestingly, Claude Opus 4 is already being used in tools like VS Code through Claude Code, where it helps with tasks like writing new functions, suggesting edits, or fixing bugs.
Claude 4 isn’t just good with text and code; it can also analyze images. Building on earlier models, it now has stronger visual capabilities that let it analyze and interpret images alongside written content. It also supports multiple images at once, which comes in handy for tasks like comparing designs, reading charts, summarizing diagrams, or reviewing user interface mockups.
While Claude is good at interpreting visuals, it does have limits: it can’t recognize people, may struggle with exact layouts like chess boards or clocks, and isn't designed for medical diagnostics. For any critical use cases, it’s best to double-check its outputs.
Used thoughtfully, Claude 4’s image capabilities can support developers debugging visual interfaces, educators creating learning materials, and researchers reviewing visual data - making it an impactful tool for multimodal tasks that combine text and imagery.
How to try out Anthropic Claude 4’s features
Here are a few ways to try out Claude 4:
Claude.ai: You can use Claude directly on Anthropic’s website. Sonnet 4 is available with a basic account, while Opus 4 requires access through the Pro tier.
Anthropic API: Developers can integrate Claude into their own tools or services using the API. Both models, Sonnet and Opus, are supported, and setup requires an API key.
GitHub Copilot: Claude 4 is available in GitHub Copilot Chat. Sonnet 4 is available to paid users, while access to Opus 4 depends on your specific plan. The models can be used inside GitHub’s site, VS Code, and the mobile app.
Claude 4 is also available on platforms like Amazon Bedrock and Google Cloud’s Vertex AI.
These integrations make it easier to use the model within cloud applications and enterprise tools.
Key takeaways
Claude 4 is a great example of how far AI models have come. With stronger reasoning, better memory, and the ability to handle both text and images, it’s built for more complex, real-world work.
Whether you're coding, analyzing data, or building AI-powered tools, Claude 4 can support your tasks. As LLMs continue to improve, tools like Claude will likely become more common in everyday workflows.
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