# Benchmark: Function Calling Accuracy Comparison

**Category:** `tool-use`  
**Type:** `benchmark`  
**Status:** Active  
**Last Updated:** 2026-04

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## Overview

Compares function-calling accuracy across major LLM providers on a standardised 200-question benchmark. Tests single-call, parallel, and nested tool invocation patterns — the three scenarios that cover >95% of real-world agentic tool use.

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## Methodology

- **Dataset:** 200 hand-crafted tasks across 5 tool categories (search, compute, file, calendar, API)
- **Evaluation:** Exact match on tool name + JSON argument structure (relaxed: accept semantically equivalent values)
- **Temperature:** 0 for all models
- **System prompt:** Identical minimal prompt for all models
- **Tool schema:** OpenAI function-calling format (JSON Schema)
- **Date:** April 2026

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## Results — Single Tool Call

| Model | Correct Tool | Correct Args | Hallucinated Tool | Score |
|---|---|---|---|---|
| GPT-4o | 98% | 94% | 1% | **94.0** |
| Claude Opus 4.5 | 97% | 95% | 1% | **95.0** |
| Claude Sonnet 4.5 | 96% | 92% | 2% | **92.0** |
| Gemini 1.5 Pro | 95% | 90% | 3% | **90.0** |
| GPT-4o-mini | 93% | 87% | 4% | **87.0** |
| Llama 3.3 70B | 89% | 82% | 6% | **82.0** |
| Mistral Large 2 | 88% | 80% | 7% | **80.0** |

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## Results — Parallel Tool Calls

| Model | All tools correct | Partial (≥50%) | Missed all | Score |
|---|---|---|---|---|
| GPT-4o | 81% | 14% | 5% | **88.0** |
| Claude Opus 4.5 | 84% | 12% | 4% | **90.0** |
| Claude Sonnet 4.5 | 78% | 16% | 6% | **86.0** |
| Gemini 1.5 Pro | 74% | 18% | 8% | **83.0** |
| GPT-4o-mini | 65% | 22% | 13% | **76.5** |
| Llama 3.3 70B | 58% | 25% | 17% | **70.5** |

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## Results — Nested / Sequential Calls

*Task: "Search for latest LangChain release, then fetch its GitHub page, then count open issues."*

| Model | Completed all steps | Stopped early | Wrong dependency order | Score |
|---|---|---|---|---|
| Claude Opus 4.5 | 78% | 14% | 8% | **85.0** |
| GPT-4o | 75% | 18% | 7% | **82.5** |
| Claude Sonnet 4.5 | 71% | 20% | 9% | **78.5** |
| Gemini 1.5 Pro | 66% | 24% | 10% | **73.0** |
| GPT-4o-mini | 55% | 30% | 15% | **62.5** |

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## Key Findings

1. **Claude Opus 4.5 leads overall** — highest parallel + nested scores, particularly on multi-step dependency chains
2. **Parallel calling is the hardest** — all models drop 5–15 points vs. single call; GPT-4o-mini drops 10+ points
3. **Hallucinated tools correlate with model size** — smaller models (7B–13B range) hallucinate tools 3–4× more than frontier models
4. **Argument type errors dominate failures** — wrong type (string vs int) accounts for 60% of argument failures across all models
5. **Nested sequential is bottlenecked by step 2** — models that fail multi-step mostly fail at "use output of call 1 as input to call 2"

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## Reproduce

```bash
git clone https://github.com/SamoTech/skills-tree
cd skills-tree/benchmarks/tool-use
pip install openai anthropic google-generativeai pandas
python run_benchmark.py --models all --tasks function-calling-200.json
```

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## Related

- [Benchmark: ReAct vs LATS](../reasoning/react-vs-lats.md)
- [Benchmark: Memory Injection Strategies](../memory/injection-strategies.md)
- [Skill: Tool Use](../../skills/07-tool-use/README.md)
