Trae Error Fix

Progress Over Time

Interactive timeline showing model performance evolution on Trae Error Fix

State-of-the-art frontier
Open
Proprietary

Trae Error Fix Leaderboard

2 models
ContextCostLicense
1
ByteDance
ByteDance
2
ByteDance
ByteDance
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About this benchmark

What is Trae Error Fix?

Trae Error Fix is a component of Trae Agent Bench that evaluates fixing existing code across multiple programming languages in containerized, runnable repositories.

Trae Error Fix is a text benchmark evaluating models on agents and coding tasks. LLM Stats tracks 2 models on this benchmark, scored on a 0–1 scale. The current average is 0.6, with the leader at 0.6.

Compare leaders on the best AI for agents and best AI for coding leaderboards.

Current leaders

Seed 2.1 Pro from ByteDance currently leads the Trae Error Fix leaderboard with a score of 0.633 across 2 evaluated AI models.

1Seed 2.1 ProByteDance63.3%
2Seed 2.1 TurboByteDance56.7%

FAQ

Common questions about the Trae Error Fix benchmark and leaderboard.

What is the Trae Error Fix benchmark?

Trae Error Fix is a component of Trae Agent Bench that evaluates fixing existing code across multiple programming languages in containerized, runnable repositories.

What is the Trae Error Fix leaderboard?

The Trae Error Fix leaderboard ranks 2 AI models based on their performance on this benchmark. Currently, Seed 2.1 Pro by ByteDance leads with a score of 0.633. The average score across all models is 0.600.

What is the highest Trae Error Fix score?

The highest Trae Error Fix score is 0.633, achieved by Seed 2.1 Pro from ByteDance.

How many models are evaluated on Trae Error Fix?

2 models have been evaluated on the Trae Error Fix benchmark, with 0 verified results and 2 self-reported results.

What categories does Trae Error Fix cover?

Trae Error Fix is categorized under agents and coding. The benchmark evaluates text models with multilingual support.

How recent are the Trae Error Fix leaderboard results?

The Trae Error Fix leaderboard was last updated in June 2026 and currently includes 2 evaluated models.