Converting TensorFlow models to PyTorch remains a challenging and largely unsolved problem, despite growing industry demand. While PyTorch has become the dominant deep learning framework, many organizations still rely on legacy TensorFlow models. The two most commonly explored automated conversion methods—using ONNX and Keras3—each come with significant trade-offs. The ONNX approach involves converting a TensorFlow model to the Open Neural Network Exchange format, then translating it to PyTorch using tools like onnx2torch. This method can produce functionally correct inference results, as demonstrated by a small numerical difference in outputs (around 9.39e-7). However, the resulting model structure is drastically altered. The number of trainable parameters drops from over 85 million in the original model to just 589,824, indicating that many weights have been baked into the model. This makes the converted model unsuitable for training or fine-tuning. Additionally, the model is composed of low-level, unoptimized operations, such as GPU-resident shape tensors in reshape layers, which hurt performance. Even with PyTorch compilation and mixed precision, the ONNX-converted model runs slower than the original TensorFlow version and far behind a native PyTorch implementation. The Keras3 method offers a more promising alternative. By redefining the model using Keras3’s high-level API and switching the backend to PyTorch, the model’s structure and parameter count are preserved. This allows for full compatibility with PyTorch training workflows and enables direct optimization, such as replacing the attention mechanism with PyTorch’s efficient scaled_dot_product_attention (SDPA). Benchmarks show a 22% performance improvement over the original TensorFlow model. However, this approach requires significant code refactoring to make the model Keras3-compatible, which may not be feasible for complex or heavily customized models. Moreover, the model remains a hybrid of Keras3 and PyTorch components, which can interfere with certain PyTorch tooling, such as torch.compile, due to internal recompilation issues. In summary, neither method provides a perfect solution. ONNX conversion is quick but results in a structurally broken, non-trainable, and suboptimal model. Keras3 conversion preserves model integrity and enables advanced optimizations but demands substantial code changes and may not integrate smoothly with all PyTorch pipelines. As of now, no fully reliable, automated, and production-ready tool exists for converting TensorFlow models to PyTorch. The best path forward depends on the model’s complexity, the need for training, and the willingness to invest in code refactoring. For many, the most practical option may still be a combination of careful manual conversion for critical models and continued use of TensorFlow for others, at least until more robust tools emerge.