Gorilla Benchmark API Bench

APIBench, a comprehensive dataset of over 11,000 instruction-API pairs from HuggingFace, TorchHub, and TensorHub APIs for evaluating language models' ability to generate accurate API calls.

Llama 3.1 405B Instruct from Meta currently leads the Gorilla Benchmark API Bench leaderboard with a score of 0.353 across 3 evaluated AI models.

Paper

MetaLlama 3.1 405B Instruct leads with 35.3%, followed by MetaLlama 3.1 70B Instruct at 29.7% and MetaLlama 3.1 8B Instruct at 8.2%.

Progress Over Time

Interactive timeline showing model performance evolution on Gorilla Benchmark API Bench

State-of-the-art frontier
Open
Proprietary

Gorilla Benchmark API Bench Leaderboard

3 models
ContextCostLicense
1405B128K$0.89 / $0.89
270B128K$0.20 / $0.20
38B131K$0.03 / $0.03
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FAQ

Common questions about Gorilla Benchmark API Bench.

What is the Gorilla Benchmark API Bench benchmark?

APIBench, a comprehensive dataset of over 11,000 instruction-API pairs from HuggingFace, TorchHub, and TensorHub APIs for evaluating language models' ability to generate accurate API calls.

What is the Gorilla Benchmark API Bench leaderboard?

The Gorilla Benchmark API Bench leaderboard ranks 3 AI models based on their performance on this benchmark. Currently, Llama 3.1 405B Instruct by Meta leads with a score of 0.353. The average score across all models is 0.244.

What is the highest Gorilla Benchmark API Bench score?

The highest Gorilla Benchmark API Bench score is 0.353, achieved by Llama 3.1 405B Instruct from Meta.

How many models are evaluated on Gorilla Benchmark API Bench?

3 models have been evaluated on the Gorilla Benchmark API Bench benchmark, with 0 verified results and 3 self-reported results.

Where can I find the Gorilla Benchmark API Bench paper?

The Gorilla Benchmark API Bench paper is available at https://arxiv.org/abs/2305.15334. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does Gorilla Benchmark API Bench cover?

Gorilla Benchmark API Bench is categorized under code, reasoning, and tool calling. The benchmark evaluates text models.

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