# OpenVINO TensorFlow Frontend Capabilities and Limitations {#openvino_docs_MO_DG_TensorFlow_Frontend}

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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

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