MuJoCo vs Brax: JAX-Based Differentiation

MuJoCo excels at single-instance, high-fidelity contact dynamics because its optimized C engine and convex solver deliver unparalleled per-step speed on CPU. For example, a standard humanoid task runs at over 100,000 steps per second on a single core, making it the gold standard for traditional robotics research where wall-clock time for a single environment is the bottleneck.

Brax takes a fundamentally different approach by reimplementing the physics pipeline entirely in JAX. This results in a trade-off: per-step latency is higher than MuJoCo's native code, but the architecture enables massive parallelism and native differentiability. A single TPU v3-8 can run tens of thousands of environments in parallel, achieving throughput that is impossible for a CPU-bound engine.

The key trade-off: If your priority is minimal single-trajectory latency and established sim-to-real benchmarks, choose MuJoCo. If you prioritize gradient-based optimization, end-to-end differentiability, and scaling RL training to thousands of parallel environments on TPU/GPU clusters, choose Brax.

MuJoCo excels at raw single-instance simulation speed and contact dynamics fidelity because of its highly optimized C-based engine. For example, in standard RL benchmarks, MuJoCo often achieves wall-clock speeds 2-3x faster than Brax on a single CPU core for complex articulated bodies, making it the gold standard for interactive robotics research where a single high-fidelity environment is needed. Its convex optimization-based contact model provides physically realistic force profiles that have been validated across thousands of sim-to-real transfer studies.

Brax takes a fundamentally different approach by reimplementing the physics pipeline entirely in JAX. This results in a trade-off: it sacrifices some single-instance CPU speed but gains massive parallelism and native differentiability on TPU/GPU clusters. A single A100 can run tens of thousands of Brax environments in parallel, achieving throughput that is physically impossible for MuJoCo's CPU-bound architecture. Furthermore, Brax provides first-class gradients through the entire simulation step, enabling direct policy optimization via backpropagation.

The key trade-off: If your priority is maximum single-environment fidelity, interactive rendering, and a mature ecosystem of pre-built robot models, choose MuJoCo. Its C-based engine remains the standard for sim-to-real transfer research. If you prioritize massively parallel RL training on accelerator hardware and need end-to-end differentiability for gradient-based policy learning or system identification, choose Brax. Its JAX-native design eliminates the CPU-GPU transfer bottleneck and unlocks gradient-based optimization workflows that are impractical in MuJoCo.