  Despite the advancements and impressive performance of Multimodal Large
Language Models (MLLMs) on benchmarks, their effectiveness in real-world,
long-context, and multi-image tasks is unclear due to the benchmarks' limited
scope. Existing benchmarks often focus on single-image and short-text samples,
and when assessing multi-image tasks, they either limit the image count or
focus on specific task (e.g time-series captioning), potentially obscuring the
performance challenges of MLLMs. To address these limitations, we introduce
MileBench, a pioneering benchmark designed to test the MultImodal Long-contExt
capabilities of MLLMs. This benchmark comprises not only multimodal long
contexts, but also multiple tasks requiring both comprehension and generation.
We establish two distinct evaluation sets, diagnostic and realistic, to
systematically assess MLLMs' long-context adaptation capacity and their ability
to complete tasks in long-context scenarios. Our experimental results, obtained
from testing 22 models, revealed that while the closed-source GPT-4o
outperforms others, most open-source MLLMs struggle in long-context situations.
Interestingly, the performance gap tends to widen with an increase in the
number of images. We strongly encourage an intensification of research efforts
towards enhancing MLLMs' long-context capabilities, especially in scenarios
involving multiple images.
