Flamingo Architecture › Model Scale and Variants

The Flamingo paper introduced three model sizes, each using progressively larger frozen language models:

Flamingo-3B pairs an NFNet vision encoder with a 1.4B parameter language model. This variant is the most computationally accessible and shows that the architectural approach works even at smaller scales. The 3B model reaches competitive few-shot performance on most benchmarks, confirming that the Flamingo approach is not exclusively a large-model phenomenon.

Flamingo-9B uses the same vision encoder with a larger 7B parameter language model. The jump from 3B to 9B yields significant performance improvements across all benchmarks, particularly on tasks requiring complex visual reasoning. This improvement is consistent with the language model's increased capacity for multi-step reasoning and richer in-context pattern recognition.

Flamingo-80B is the full-scale model, pairing the NFNet encoder with an 80B parameter Chinchilla language model. This variant reaches the strongest performance across all evaluated tasks and most clearly shows the few-shot learning capabilities. The scaling from 9B to 80B yields smaller relative improvements than from 3B to 9B, consistent with the diminishing returns observed in language model scaling studies.

The parameter efficiency is clear when comparing Flamingo to end-to-end trained models of similar scale. Flamingo-80B, with roughly 10B trainable parameters (out of 80B total), matches or surpasses models with 80B fully trainable parameters on many benchmarks, suggesting that the pretrained representations in the frozen components are substantially more useful than additional randomly initialized parameters would be.