swap_horiz PaLM (540B) Alternatives
Looking for alternatives to PaLM (540B)? Compare the top Accuracy options ranked by our AI scoring system.
PaLM (540B)
Google's PaLM 540B achieves breakthrough accuracy across reasoning, language understanding, and generation tasks.
apps Top PaLM (540B) Alternatives
The top alternative to PaLM (540B) in 2026 is GLaM (Generalist Language Model) with a score of 9.8/10, followed by T5-11B (9.7) and BERT-Large (9.5).
GLaM (Generalist Language Model)
Google's GLaM achieves high accuracy with a sparse mixture-of-experts architecture, surpassing dense models on several b...
T5-11B
Google's T5-11B achieves high accuracy across diverse NLP tasks via a unified text-to-text framework.
BERT-Large
BERT-Large set new accuracy records on eleven NLP tasks, including question answering and language inference.
DINOv2 (Self-Supervised ViT-g)
DINOv2 with ViT-g sets new accuracy records for self-supervised visual feature learning on multiple downstream tasks.
RoBERTa-Large
RoBERTa-Large improves upon BERT with more training data and longer training, achieving higher accuracy on GLUE and othe...
ERNIE 3.0 Titan
Baidu's ERNIE 3.0 Titan achieves high accuracy on Chinese and English benchmarks by incorporating knowledge graph embedd...
ViT-Large (Vision Transformer)
Vision Transformer Large achieves competitive accuracy on ImageNet by applying transformer architecture directly to imag...
Llama 3 70B
Llama 3 70B is a powerful open-source large language model developed by Meta. It distinguishes itself through its massiv...
ConvNeXt-XL
ConvNeXt-XL modernizes the standard ConvNet to achieve accuracy competitive with vision transformers on ImageNet.
Noisy Student (EfficientNet-L2)
Noisy Student training with EfficientNet-L2 achieves state-of-the-art accuracy on ImageNet using self-training.
Swin-L Transformer
Swin-L introduces shifted windows for efficient attention, achieving top accuracy on ImageNet and other vision tasks.
summarize Quick Comparison Summary
| Alternative | Score | vs PaLM (540B) | Action |
|---|---|---|---|
| GLaM (Generalist Language Model) | 9.8 | -0.1 | Compare |
| T5-11B | 9.7 | -0.2 | Compare |
| BERT-Large | 9.5 | -0.4 | Compare |
| DINOv2 (Self-Supervised ViT-g) | 9.7 | -0.2 | Compare |
| RoBERTa-Large | 9.6 | -0.3 | Compare |
| ERNIE 3.0 Titan | 9.6 | -0.3 | Compare |
| ViT-Large (Vision Transformer) | 9.5 | -0.4 | Compare |
| Llama 3 70B | 9.5 | -0.4 | Compare |
| ConvNeXt-XL | 9.4 | -0.5 | Compare |
| Noisy Student (EfficientNet-L2) | 9.7 | -0.2 | Compare |
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