1. Computational model: Dask is designed to scale computation beyond what can fit into memory, operating in parallel and distributing work across multiple processors or nodes. On the other hand, NumPy operates on in-memory arrays and is not suited for distributed computing.

2. Lazy evaluation: Dask operates using lazy evaluation, meaning it delays computation until necessary, allowing for the optimization of computational resources. In contrast, NumPy performs immediate computation upon array creation, which may lead to inefficiencies in memory usage.

3. Scalability: Dask offers scalability by enabling parallel processing of large datasets that exceed memory capacity. NumPy, being limited to in-memory operations, lacks the ability to efficiently handle big data computations that require distributed processing.

4. Task graphs: Dask represents computations as task graphs, enabling optimization and parallel execution of complex workflows by breaking them into smaller, independent tasks. NumPy processes computations sequentially without the concept of task graphs, limiting its ability to optimize complex calculations.

5. Backends: Dask supports multiple backends for execution, allowing users to choose between threading, multiprocessing, or distributed computing depending on the nature of the tasks. In contrast, NumPy primarily relies on a single backend, which is implemented in C and optimized for single-threaded operations.

6. Integration with pandas and other libraries: Dask seamlessly integrates with pandas and other Python libraries, enabling easy parallelization of data manipulation tasks. While NumPy and pandas can work together, they do not provide the same level of seamless integration and parallel processing capabilities as Dask.

In Summary, Dask and NumPy differ in computational model, lazy evaluation, scalability, task graphs, supported backends, and integration with other libraries.