# OpenVINO TensorFlow Frontend Capabilities and Limitations {#openvino_docs_MO_DG_TensorFlow_Frontend}

TensorFlow Frontend is C++ based Frontend for conversion of TensorFlow models and is available as a preview feature starting from 2022.3.
That means that you can start experimenting with `--use_new_frontend` option passed to Model Optimizer to enjoy improved conversion time for limited scope of models
or directly loading TensorFlow models through `read_model()` method.

The current limitations:

* IRs generated by new TensorFlow Frontend are compatible only with OpenVINO API 2.0
* There is no full parity yet between legacy Model Optimizer TensorFlow Frontend and new TensorFlow Frontend so primary path for model conversion is still legacy frontend
* Model coverage and performance is continuously improving so some conversion phase failures, performance and accuracy issues might occur in case model is not yet covered.
Known unsupported models: object detection models and all models with transformation configs, models with TF1/TF2 control flow, Complex type and training parts
* `read_model()` method supports only `*.pb` format while Model Optimizer (or `convert_model` call) will accept other formats as well which are accepted by existing legacy frontend