BC-VL

Progress Over Time

Interactive timeline showing model performance evolution on BC-VL

State-of-the-art frontier
Open
Proprietary

BC-VL Leaderboard

1 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
1.0M$0.32 / $1.28
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About this benchmark

What is BC-VL?

BC-VL is a vision-language benchmark for knowledge-grounded multimodal question answering.

BC-VL is a multimodal benchmark evaluating models on knowledge, multimodal, and vision tasks. LLM Stats tracks 1 models on this benchmark, scored on a 0–1 scale. The current average is 0.5, with the leader at 0.5.

Compare leaders on the best AI for knowledge, best AI for multimodal and best AI for vision leaderboards.

Current leaders

Qwen3.7-Plus from Alibaba Cloud / Qwen Team currently leads the BC-VL leaderboard with a score of 0.511 across 1 evaluated AI models.

1Qwen3.7-PlusAlibaba Cloud / Qwen Team51.1%

FAQ

Common questions about the BC-VL benchmark and leaderboard.

What is the BC-VL benchmark?

BC-VL is a vision-language benchmark for knowledge-grounded multimodal question answering.

What is the BC-VL leaderboard?

The BC-VL leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Qwen3.7-Plus by Alibaba Cloud / Qwen Team leads with a score of 0.511. The average score across all models is 0.511.

What is the highest BC-VL score?

The highest BC-VL score is 0.511, achieved by Qwen3.7-Plus from Alibaba Cloud / Qwen Team.

How many models are evaluated on BC-VL?

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

What categories does BC-VL cover?

BC-VL is categorized under knowledge, multimodal, and vision. The benchmark evaluates multimodal models.

Which model offers the best value on BC-VL?

Among models scoring within 10% of the leader, Qwen3.7-Plus from Alibaba Cloud / Qwen Team is the cheapest, at $0.32 per million input tokens with a score of 0.511.

How recent are the BC-VL leaderboard results?

The BC-VL leaderboard was last updated in July 2026 and currently includes 1 evaluated models.