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reasoning benchmark

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.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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  • FAQ

Gorilla Benchmark API Bench Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01MELlama 3.1 405B InstructMeta35.3%100.0%3CAug 11, 2026
02MELlama 3.1 70B InstructMeta29.7%50.0%3CAug 11, 2026
03MELlama 3.1 8B InstructMeta8.2%0.0%3CAug 11, 2026

Gorilla Benchmark API Bench Score Distribution

A closer view of the leading scores on this benchmark.

Gorilla Benchmark API Bench

Gorilla Benchmark API Bench Highlights

The leading models and scores on this benchmark.

Rank #1Llama 3.1 405B Instruct35.3%Rank #2Llama 3.1 70B Instruct29.7%Rank #3Llama 3.1 8B Instruct8.2%

What is Gorilla Benchmark API Bench?

What Gorilla Benchmark API Bench measures and how its scores work.

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.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Gorilla Benchmark API Bench
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
gorilla-benchmark-api-bench|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Gorilla Benchmark API Bench.

Which model scores highest on Gorilla Benchmark API Bench?

Llama 3.1 405B Instruct is currently ranked first with 35.3%.

What does Gorilla Benchmark API Bench measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.