The AI arena is free today

Open Superagent

VIBE iOS

Progress Over Time

Interactive timeline showing model performance evolution on VIBE iOS

State-of-the-art frontier
Open
Proprietary

VIBE iOS Leaderboard

1 models
ContextCostLicense
1230B1.0M$0.30 / $1.20
Notice missing or incorrect data?
About this benchmark

What is VIBE iOS?

VIBE benchmark subset for iOS application generation

VIBE iOS is a text benchmark evaluating models on code tasks. LLM Stats tracks 1 models on this benchmark, scored on a 0–1 scale. The current average is 0.9, with the leader at 0.9.

Compare leaders on the best AI for code leaderboards.

Current leaders

MiniMax M2.1 from MiniMax currently leads the VIBE iOS leaderboard with a score of 0.880 across 1 evaluated AI models.

1MiniMax M2.1MiniMax88.0%

FAQ

Common questions about the VIBE iOS benchmark and leaderboard.

What is the VIBE iOS benchmark?

VIBE benchmark subset for iOS application generation

What is the VIBE iOS leaderboard?

The VIBE iOS leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, MiniMax M2.1 by MiniMax leads with a score of 0.880. The average score across all models is 0.880.

What is the highest VIBE iOS score?

The highest VIBE iOS score is 0.880, achieved by MiniMax M2.1 from MiniMax.

How many models are evaluated on VIBE iOS?

1 models have been evaluated on the VIBE iOS benchmark, with 0 verified results and 1 self-reported results.

What categories does VIBE iOS cover?

VIBE iOS is categorized under code. The benchmark evaluates text models.

What is the best open-source model on VIBE iOS?

MiniMax M2.1 by MiniMax is the top-ranked open-source model on VIBE iOS, with a score of 0.880 (rank #1).

Which model offers the best value on VIBE iOS?

Among models scoring within 10% of the leader, MiniMax M2.1 from MiniMax is the cheapest, at $0.30 per million input tokens with a score of 0.880.

How recent are the VIBE iOS leaderboard results?

The VIBE iOS leaderboard was last updated in August 2026 and currently includes 1 evaluated models.