Claude Sonnet 4's Tool Calling vs. GPT-4 & Gemini: A Deep Dive
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Zack Saadioui
8/12/2025
The Ultimate Showdown: A Deep Dive into Claude Sonnet 4's Tool Calling vs. The Competition
Alright, let's talk about something that's REALLY changing the game in AI development: tool calling. If you've been in the trenches building anything with large language models, you know that the real magic happens when you can connect them to the outside world. It's the difference between a clever chatbot & a powerful, autonomous agent that can actually get things done.
For a while, OpenAI's function calling felt like the gold standard. It was a reliable way to get a model like GPT-4 to structure its output so you could call an API, query a database, or trigger a workflow. But honestly, the landscape is shifting. Anthropic's new Claude 4 models, especially Sonnet 4, are making some serious waves, & Google's Gemini is right there in the mix.
So, here’s the deal. I’ve been digging deep into the docs, benchmarks, & developer guides to give you a complete analysis of how Claude Sonnet 4's tool-calling capabilities stack up against its biggest rivals. We're going beyond the marketing hype to look at what it's actually like to build with these things.
What is Tool Calling & Why Should You Care?
First off, let's get on the same page. "Tool calling" or "function calling" is basically the ability of an LLM to recognize when it needs external information or to perform an action that it can't do on its own. Instead of just making up an answer (hallucinating), the model can say, "Hey, I need to use the
1
get_weather_forecast
tool for that," & then output a structured piece of data, usually JSON, that your application can use to actually run the function.
This is a HUGE deal. It means you can build AI that can:
Access real-time information: Think stock prices, weather updates, or the latest news.
Interact with your own systems: Your AI could pull customer data from your CRM, check inventory in your database, or even send an email on a user's behalf.
Create powerful agents: Imagine an AI that can not only answer a customer's question but also process their return, update their shipping address, & create a support ticket, all by using your internal tools.
This is where things get really exciting for businesses. Instead of a static, one-way conversation on your website, you can have a dynamic, interactive experience. For instance, this is the core idea behind platforms like Arsturn. Arsturn helps businesses create custom AI chatbots trained on their own data. These aren't just FAQ bots; they're sophisticated agents that can provide instant, personalized customer support, answer complex questions by accessing company knowledge bases, & engage with website visitors 24/7. The ability for the underlying AI to use tools is what makes this level of interaction possible.
Anthropic's Claude Sonnet 4: The New Contender
Anthropic's release of the Claude 4 models, both the powerhouse Opus 4 & the more balanced Sonnet 4, was a clear signal that they are targeting developers head-on. And a major part of that strategy is their revamped tool-calling functionality.
What makes Claude's approach so interesting? It's not just that it can call tools; it's how it does it.
Interleaved Thinking & Parallel Tool Use
This is probably the most significant innovation from Anthropic. Traditionally, the process with models like GPT was:
Model reasons that it needs a tool.
Model stops reasoning & outputs a JSON object for the tool call.
Your application executes the tool.
You feed the result back to the model.
The model continues reasoning with the new information.
Claude 4 can do something called "interleaved thinking." This means the model can alternate between its own internal reasoning process & tool use within a single turn. It’s like the model can "think out loud" about the problem, decide to use a tool, get the result, & then continue its thought process without having to completely stop & restart. This creates a much more fluid & powerful problem-solving loop.
On top of that, Claude 4 supports parallel tool execution. If a user's request requires information from multiple sources (say, checking a customer's order history & the current shipping status from a courier's API), Claude can identify & call both tools simultaneously. This can dramatically reduce latency & make the user experience feel much snappier.
How It Works in Practice
Getting started with tool use in Claude Sonnet 4 is pretty straightforward. You define your tools, describe what they do & what their inputs are, & then pass them to the model in your API call. Here’s a super simplified look at the process:
Define Your Tools: You create a list of your available functions, including their name, a clear description (this is SUPER important for the model to know when to use it), & a JSON schema for the expected inputs.
Make the API Call: You send your user's prompt along with the list of tools to the Claude API.
Handle the Response: If Claude decides to use a tool, the API response will include a
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tool_use
content block. Your code then needs to parse this, run the actual function with the provided arguments, & send the result back to Claude in a
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tool_result
block.
Get the Final Answer: Claude takes the tool's output & uses it to generate its final, human-readable response.
For more advanced use cases, like the interleaved thinking or the new native code execution tool, you might need to include a specific beta header in your API request. This code execution tool is particularly powerful, allowing the model to write & run Python code to solve complex problems, like analyzing data or generating visualizations.
Performance & Benchmarks
Here's where it gets really interesting. On paper, Claude Sonnet 4 is an absolute beast, especially when it comes to tasks that require complex reasoning & tool use. On the SWE-bench Verified benchmark, which tests a model's ability to resolve real-world GitHub issues, Sonnet 4 scores an impressive 72.7%, outperforming competitors like GPT-4.1. It also performs very well on TerminalBench (for command-line based coding) & TAU-bench (which specifically measures agentic tool use).
This suggests that Claude Sonnet 4 is not just good at following instructions to use a tool; it's exceptionally good at understanding when & how to use tools to solve complex, multi-step problems.
OpenAI's GPT-4: The Established Veteran
OpenAI's function calling has been the go-to for developers for a while now, & for good reason. It’s robust, well-documented, & has a huge community of users. It set the standard for what developers expect from tool-calling capabilities.
The OpenAI Workflow
The process with GPT-4 is very similar to Claude's at a high level, but the devil is in the details. The core idea is to describe your functions to the model & let it intelligently choose when to output a JSON object containing the arguments to call those functions.
The typical workflow looks like this:
Define Your Functions: You define your functions in your code (e.g., in Python).
Describe Them for the Model: You create a JSON schema that describes each function, its purpose, & its parameters. This is what you'll pass to the API.
Call the ChatCompletion API: You send the user's message along with the list of function descriptions. You can set
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function_call="auto"
to let the model decide when to call a function, or force it to call a specific function.
Execute the Function: Your code checks the API response. If it contains a
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function_call
, you parse the JSON, execute your local function with the provided arguments.
Send the Result Back: You make a second API call, appending the function's return value as a new message, so the model has the context to formulate its final answer.
This multi-step process is reliable but can feel a bit more rigid compared to Claude's interleaved thinking. It's a very explicit back-&-forth conversation between your app & the model.
Strengths & Developer Experience
The biggest strength of OpenAI's approach is its maturity. There are countless tutorials, guides, & community examples out there. Developers know it, they trust it, & it generally works very well. GPT-4's instruction-following is top-notch, so if you provide a clear description of your tool, it’s very likely to use it correctly.
However, some developers find the manual creation of JSON schemas for functions to be a bit tedious. Tools like Mirascope have emerged to help automate this by generating the schemas directly from Python docstrings, which simplifies the process significantly.
When building conversational AI for a business, this reliability is key. This is where a solution like Arsturn comes in handy. It abstracts away the complexity of managing these API calls. Instead of hand-coding the entire function-calling workflow, Arsturn provides a no-code platform that lets you train an AI on your own data. This means you can easily create a chatbot that not only understands user intent but can also tap into your business's unique knowledge & tools to provide personalized, actionable responses, effectively boosting conversions & customer engagement.
Google's Gemini: The Versatile Challenger
Google is a heavyweight in the AI world, & its Gemini models are incredibly capable. Their approach to function calling is very similar to OpenAI's, which is actually a smart move, as it makes it easier for developers to adopt.
Flexible Implementation
One of the coolest things about Gemini's function calling is its flexibility. You have a few different ways to implement it:
OpenAPI JSON Schema: Just like with OpenAI & Claude, you can define your tools using a standard JSON schema. This is a language-agnostic approach that works well in any environment.
Python Functions: If you're working in Python, you can define your tools as regular Python functions with docstrings. The Gemini SDK can automatically generate the necessary schema from your code, which is a fantastic developer-friendly feature.
OpenAI-Compatible API: Google provides an OpenAI-compatible API for Gemini. This is a game-changer for teams that have existing codebases built on OpenAI's APIs. You can often switch to using a Gemini model with minimal code changes, which lowers the barrier to entry significantly.
The process itself mirrors the others: define your tool, send it to the model with a prompt, the model returns a
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FunctionCall
object, you execute the function, & you send the result back for the final response.
Control & Configuration
Gemini offers some nice configuration options to control the model's tool-calling behavior. For example, you can set the
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FunctionCallingConfig
to:
AUTO: The default mode, where the model decides whether to call a function or not.
ANY: This forces the model to call a function.
NONE: This prevents the model from calling any functions.
This level of control can be useful for constraining the model's behavior in specific applications.
The Head-to-Head Comparison
So, how do they all stack up? Let's break it down.
Highly capable, with performance on par with competitors
Developer Experience
Feels powerful & fluid, especially for complex tasks. Requires some handling of beta features.
Well-understood but can be verbose. Large ecosystem of support.
Very developer-friendly, especially for Python users. Easy to migrate to.
So, Who Wins?
Honestly, "winning" depends entirely on your use case.
Choose Claude Sonnet 4 if:
You're building complex, multi-step agents that need to perform sophisticated reasoning.
Your application can benefit from reduced latency through parallel tool calls.
You want the most "human-like" problem-solving process with interleaved thinking.
You're heavily focused on coding or software development tasks, where it currently leads in benchmarks.
Choose OpenAI GPT-4 if:
You need a battle-tested, highly reliable solution with a massive amount of documentation & community support.
Your application relies on precise instruction-following for a wide variety of tasks.
You're already invested in the OpenAI ecosystem & want a stable, predictable experience.
Choose Google Gemini if:
You're a Python developer who loves the idea of auto-generating tool schemas from your code.
You want an easy migration path from an existing OpenAI-based application.
You need fine-grained control over when & how the model calls functions.
The Bottom Line
The competition in tool-calling capabilities is FIERCE, & that's a fantastic thing for developers. Claude Sonnet 4 has seriously raised the bar with features like interleaved thinking & parallel tool use, making it an incredibly powerful choice for building the next generation of AI agents. It feels less like you're just commanding a model & more like you're collaborating with a reasoning partner.
At the same time, you can't discount the sheer reliability & ecosystem of OpenAI or the developer-friendly flexibility of Google's Gemini.
Ultimately, the best way to connect your business data & workflows to an AI is to use a platform that handles the nitty-gritty for you. When you're looking to build a conversational AI platform that can truly engage with your audience, you don't want to be bogged down in the minutiae of API headers & JSON schemas. That's the value of a solution like Arsturn. It helps businesses leverage the power of these advanced models to build meaningful connections with their audience through personalized chatbots, all without needing to write a single line of code. It takes the best of what these models offer—their reasoning, their tool-use capabilities—& packages it into a solution focused on business outcomes like lead generation & customer satisfaction.
The race is far from over, but one thing is clear: AI is breaking out of the chatbox & starting to interact with the world in a meaningful way. It's a pretty exciting time to be building.
Hope this was helpful! Let me know what you think.