Key finding: For simple single-function calls, all models perform similarly. The gaps widen with complex multi-function routing and chained calls — where GPT-5 and Claude pull ahead.

Cost Per Function Call

Function calling costs add up fast in agent workflows where each user interaction may trigger 2-5 function calls. Here's the monthly cost at different call volumes:

The cost spread is enormous. DeepSeek V4 Pro at $390/mo is 11.5x cheaper than Claude Sonnet 4.6 at $4,500/mo for the same workload — with only a 3.4% accuracy difference.

Latency Comparison

Function calling adds latency because of the extra round-trip. Time-to-first-function-call matters for user experience:

DeepSeek V4 Pro is the fastest, likely due to its aggressive inference optimization. Claude models are consistently slower for function calling, which compounds across multi-step agent workflows.

When to Use Each Model

The Hybrid Strategy: Best Accuracy at Lowest Cost

Here's the strategy that saves 70-80% on function-calling costs while maintaining high accuracy:

By using DeepSeek for the 85% of simple calls and escalating only the complex 15% to GPT-5, you get 97%+ effective accuracy at 73% lower cost than using GPT-5 alone.

Implementation

Most LLM providers expose a tool_choice parameter and confidence scores. Route based on:

• Number of tools defined — If >5 tools, use the more capable model
• Query complexity — Simple queries (<50 words) go to DeepSeek; complex ones to GPT-5
• Function call confidence — If the model returns low-confidence scores, escalate
• Chained calls — Always use the better model for multi-step workflows

— See if you're overpaying for AI APIs

Provider Support for Function Calling

Not all providers implement function calling the same way:

Optimization Tips

1. Minimize tool definitions — Fewer tools = faster routing and lower cost. Only expose tools relevant to the current conversation.
2. Use parallel function calls — When multiple independent functions are needed, parallel calls reduce latency by 40-60%.
3. Cache function results — If the same function is called with the same arguments repeatedly, cache the result to avoid redundant API calls.
4. Batch similar queries — Group function-calling requests to reduce per-request overhead.
5. Set max tokens carefully — Function calls are typically short (50-200 tokens). Cap output to avoid wasted tokens on verbose responses.

Related Reading

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