Best Function Calling LLM: A Practical Comparison

If you're building an agent, a coding assistant, or any app that needs an LLM to call tools reliably, the honest answer is: Claude (specifically Claude 3.5/3.7 Sonnet and Opus) and GPT-4-class models are currently the strongest choices for function calling, with Claude generally edging ahead on multi-step tool chains and instruction-following inside tool schemas. Gemini 1.5/2.0 Pro is a solid third option, especially if you're already inside the Google ecosystem, but it tends to need more prompt scaffolding to match Claude's or GPT-4's consistency.

That said, "best" depends heavily on what you're optimizing for: raw accuracy on picking the right tool, correctness of argument formatting, ability to chain multiple tool calls without losing context, or cost per request at scale. This article breaks down what actually matters when evaluating function calling LLMs and how the major options compare.

What "Best" Actually Means for Function Calling

Benchmarks for function calling are noisy and often don't reflect real workloads. Instead of chasing leaderboard numbers, evaluate models against these practical criteria:

• Schema adherence — does the model reliably output valid JSON matching your tool's parameter schema, including nested objects and enums?
• Tool selection accuracy — when multiple tools are available, does it pick the right one instead of hallucinating a call or defaulting to text?
• Multi-step reasoning — can it chain 3–5 tool calls to complete a task, using the result of one call to inform the next?
• Refusal to fabricate — does it call a tool to get real data instead of guessing an answer?
• Latency and cost — function calling often means multiple round trips; a slower or pricier model multiplies that cost.

How the Major Models Compare

Claude (Anthropic)

Claude's tool use implementation is one of the most consistent in practice. It tends to:

• Stick closely to the provided JSON schema, including required fields and enums
• Handle parallel tool calls in a single turn when appropriate
• Explain its reasoning before invoking a tool, which makes debugging easier
• Degrade gracefully when a tool result is malformed, instead of looping indefinitely

Claude 3.5 Sonnet in particular is a strong default for agentic workflows — it's fast enough for interactive use and accurate enough that you don't need heavy retry logic. Opus trades some speed for slightly better reasoning on complex multi-tool chains.

GPT-4 / GPT-4o (OpenAI)

GPT-4-class models were early leaders in function calling and remain very capable. They're particularly good at:

• Structured output mode (JSON schema enforcement) for cases where you need guaranteed valid JSON
• Broad tool ecosystems and community tooling built around the OpenAI function calling spec

Where they sometimes fall behind is longer tool chains — GPT-4o can occasionally over-call tools or repeat calls unnecessarily compared to Claude on the same task.

Gemini (Google)

Gemini's function calling has improved significantly but still benefits from more explicit prompting to get consistent schema adherence. It's a reasonable choice if you're already using Vertex AI or need tight integration with Google Workspace data, but for pure tool-use reliability it's generally a step behind Claude and GPT-4.

Open-Weight Models (Llama, Mistral, Qwen)

Open models have closed the gap for simple, single-tool tasks, especially when fine-tuned for function calling specifically. For multi-step agentic workflows with several tools, though, they still lag the closed models in consistency. They're worth considering if you need self-hosting, data residency, or cost control at very high volume — just budget extra time for prompt engineering and validation.

Practical Recommendation

For most production use cases — coding agents, customer support bots, data-lookup assistants — Claude is the safest default for function calling due to its schema adherence and multi-step reliability. If you need guaranteed structured JSON output above all else, GPT-4o's structured output mode is a strong alternative. If you're deep in the Google Cloud stack, Gemini is workable but plan for more validation logic.

Getting Claude's Function Calling Into Your Stack Quickly

Anthropic's tool use API is well-documented, but wiring it into an existing app — key management, streaming, usage tracking across a team — takes real setup time. If you already have Claude access and want a clean HTTPS API with application-level keys, streaming, and tool use support without building that infrastructure yourself, SubToAPI exposes it as a straightforward REST endpoint:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet-20241022",
    "max_tokens": 1024,
    "tools": [
      {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "input_schema": {
          "type": "object",
          "properties": {
            "location": {"type": "string"}
          },
          "required": ["location"]
        }
      }
    ],
    "messages": [
      {"role": "user", "content": "What is the weather in Lisbon?"}
    ]
  }'

The response includes a tool_use block with structured arguments you can execute and feed back into the conversation. Full details are in the tool use docs, and the quickstart covers getting your first sub_live_... key. Plans start at €9/month on the Solo tier, with team seats on the Team and Scale plans — see pricing for details.

Testing Function Calling Yourself

Don't take any comparison at face value, including this one. Build a small eval set of 15–20 real tasks from your own domain — including edge cases like ambiguous tool selection and multi-step chains — and run them against candidate models. Track schema validity rate, correct-tool rate, and number of turns needed to complete the task. That will tell you more about "best" for your use case than any generic benchmark.

questions

Which LLM has the most reliable function calling for multi-step agents? Claude 3.5 Sonnet and Opus currently show the most consistent behavior across chained tool calls, with good schema adherence and fewer redundant calls compared to alternatives.

Is GPT-4 or Claude better for function calling? Both are strong. GPT-4o's structured output mode guarantees valid JSON, while Claude tends to be more reliable on longer tool chains and complex multi-tool reasoning — test both against your own tasks.

Can I use Claude's function calling without building my own API infrastructure? Yes — services like SubToAPI expose Claude's tool use as a standard REST API with application keys and usage tracking, so you can integrate function calling without managing that layer yourself.