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agents benchmark

Search and Function-Calling

Search and Function-Calling is an OpenAI internal production benchmark measuring reliable search-tool use and function calling in agentic workflows, reported as a pass rate.

Updated Aug 7, 2026

Published models3
Registry coverage3
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

Search and Function-Calling leaderboard

Sorted by the source-provided rank. Higher score is better according to the registry.

3 rows
Columns

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1OPGPT-5.6 TerraOpenAI94.6%100.0%3CAug 7, 2026
2OPGPT-5.6 SolOpenAI91.0%50.0%3CAug 7, 2026
3OPGPT-5.6 LunaOpenAI89.7%0.0%3CAug 7, 2026

Score distribution

Top published rows on the benchmark's original scale.

Search and Function-Calling

Search and Function-Calling highlights

The top published results on this benchmark's own scale.

Rank #1GPT-5.6 Terra94.6%Rank #2GPT-5.6 Sol91.0%Rank #3GPT-5.6 Luna89.7%

What is Search and Function-Calling?

Definition and scoring fields from the benchmark registry.

Search and Function-Calling is an OpenAI internal production benchmark measuring reliable search-tool use and function calling in agentic workflows, reported as a pass rate.

Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.

Family
Search and Function-Calling
Modality
text
Primary category
agents
Score direction
higher
LLMBoard eligible
No
Evaluation key
openai-search-function-calling|llm-stats-current

Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.

FAQ

Common questions about Search and Function-Calling.

Which model scores highest on Search and Function-Calling?

GPT-5.6 Terra is currently ranked first with 94.6%.

What does Search and Function-Calling measure?

Search and Function-Calling is an OpenAI internal production benchmark measuring reliable search-tool use and function calling in agentic workflows, reported as a pass rate.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 unique published model results are currently shown.

Does this benchmark affect the overall score?

This benchmark is preserved as source-native evidence but is not eligible for the current overall score.

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