Language models are changing quickly in the world of AI. They are no longer just text generators; they are now smart agents that can connect with people in the real world. One of the most transformative features enabling this shift is function calling, also known as tool use or API calling. This powerful capability allows LLMs to trigger external systems or services, effectively making decisions, fetching data, and even taking action based on user intent.

If you've ever wished your chatbot could check the weather, book a flight, retrieve stock prices, or even manage internal business workflows—function calling makes it possible. It extends LLMs beyond traditional chat applications and opens the door to real-time, dynamic AI integrations. This post will explore 6 top-performing LLMs that support function calling, making them ideal for building autonomous AI agents, dynamic apps, and real-time data-driven systems.

What is Function Calling in LLMs?

At its core, function calling allows a language model to communicate with external tools or APIs. Rather than just responding in plain text, an LLM with this ability can identify when a user query requires external data, invoke the appropriate function, and integrate the response into its final output.

For example, if a user asks, "What's the weather in Tokyo?", a function-calling-enabled model will recognize this requires live data. It then formats a request to a weather API, retrieves the data, and responds appropriately. It means you're no longer limited to a model’s training data—you now have access to real-time, contextual insights.

1. OpenAI GPT-4o

Standout feature: Human-like interaction + advanced tool orchestration

The latest from OpenAI, GPT-4o, takes function calling to the next level. This model can intelligently determine when a user’s prompt requires external data, select the appropriate API, and format a request autonomously. It supports structured API calls and offers detailed integration capabilities for developers.

Example Use Cases:

• Personal Assistants – Fetch recent orders, schedule meetings
• Mathematical Computation – Solve equations using external tools
• UI Interaction – Dynamically update maps or dashboards
• Data Retrieval – Pull stock prices, weather, or flight info in real-time

GPT-4o not only performs accurate function calls but also shines in multi-step workflows, making it ideal for complex applications like customer service bots, analytics dashboards, and automated task handlers.

2. Gemini 1.5 Flash

Standout feature: Lightweight, fast, and optimized for responsiveness

Google DeepMind’s Gemini 1.5 Flash introduces a slick, low-latency experience with structured function outputs. Rather than directly calling functions, the model generates structured data indicating which function to call and with what parameters—offering a secure, predictable integration pattern.

Example Use Cases:

• E-commerce – Product recommendations and order tracking
• Customer Support – Auto-create or update tickets from CRM tools
• Healthcare – Access appointment scheduling and patient history
• Finance – Retrieve account balances or execute transactions

With support for custom function definitions, Gemini 1.5 Flash is great for enterprises looking for tailored, scalable integrations.

3. Anthropic Claude Sonnet 3.5

Standout feature: Built with safety and transparency in mind

Anthropic’s Claude models have always prioritized safety and interpretability, and Claude Sonnet 3.5 is no different. Function calling is seamlessly integrated, with Claude identifying when to use external tools and how to extract and utilize responses securely.

Example Use Cases:

• Weather Forecasting – Fetch real-time weather via external APIs
• Currency Conversion – Calculate exchange rates on demand
• Reminder Scheduling – Automate tasks in calendar apps
• Financial Insights – Get live updates on stock prices or crypto

Claude handles function output with clarity, ensuring the results are communicated in an easy-to-understand way. It makes it particularly valuable in regulated industries or use cases involving sensitive data.

4. Cohere Command R+

Standout feature: Single-step tool use optimized for production workflows

Cohere’s Command R+ excels in real-time API interaction, using a single-step tool use to deliver rapid responses. It intelligently chooses tools, forms requests, and integrates output without requiring extensive developer intervention.

Example Use Cases:

• Weather Queries – Calls weather APIs with appropriate parameters
• Database Lookups – Retrieve customer details or order history
• Search Integration – Query search engines and summarize results

Command R+ has been fine-tuned for function calling with specialized prompt formats. It's ideal for automated workflows, where consistency and precision matter.

5. Mistral Large 2

Standout feature: Complex parallel and sequential function orchestration

Mistral Large 2, with its 123 billion parameters, is a computational powerhouse. But what makes it exceptional is its ability to execute multi-step function calls, even in parallel. It excels in applications that involve report generation, data analysis, and scientific simulations.

Example Use Cases:

• Business Automation – Schedule appointments, escalate tickets
• Data Analytics – Risk assessments or market predictions
• Scientific Research – Perform real-time climate simulations or computations
• Logistics – Track shipments, forecast delivery routes

Its advanced sequencing ability makes Mistral Large 2 a go-to choice for enterprise-scale systems and high-load environments.

6. Meta LLaMA 3.2

Standout feature: Open-source flexibility with function calling support

Meta’s LLaMA 3.2 is the only fully open-source model in this list, and it introduces function calling in a way that developers can completely customize. While still maturing in terms of benchmarks, it’s a developer’s playground—especially for academic research and custom AI systems.

Key Advantages:

• Custom Function Integration – Tailor the entire calling logic to your app
• Internal Enterprise Tools – Build AI on proprietary systems
• Flexible Infrastructure – Run on your hardware or open platforms

Though it lags slightly behind in hallucination reduction or multi-turn summary tasks, LLaMA 3.2’s openness and adaptability give it a strong niche in research and innovation-driven projects.

Conclusion

Function calling is no longer a futuristic concept—it’s the present reality of AI. From automating tasks to executing workflows and fetching real-time data, LLMs with function-calling support are redefining what AI can do. These 6 models—GPT-4o, Gemini 1.5, Claude Sonnet 3.5, Command R+, Mistral Large 2, and LLaMA 3.2—offer unique strengths and possibilities. Whether you're building the next intelligent assistant, automating enterprise workflows, or just experimenting with what’s possible, the future is bright—and callable.