A large mixture-of-experts model that activates only a fraction of its parameters per token, making it surprisingly efficient for its capability tier. It handles complex reasoning, coding, and multilingual tasks with depth, while the sparse activation means it can run on less hardware than a dense 80B model would require. The trade-off is that MoE architectures can be less predictable on niche or highly specialized prompts.