The increasing complexity of lunar exploration calls for intelligent systems capable of supporting autonomous operations and scientific decision-making under uncertain and resource-limited conditions. Advances in large language models (LLMs) create new opportunities for mission planning, but their reliability in dynamic, safety-critical environments remains insufficiently evaluated. Existing benchmarks focus on static, context-independent reasoning tasks and fail to capture the constraints and dependencies of lunar missions. To address this gap, we introduce Lunar-Bench, a benchmark designed to assess the task-oriented reasoning and decision-making performance of LLMs through 3,000 tasks derived from mission procedures and documentation. We further propose the Environmental Scenario Indicators, a process-based framework that evaluates safety, efficiency, integrity, and alignment beyond conventional accuracy. Experiments on 36 representative models show that the best achieves 47.8% accuracy compared with 65.1% for human experts. Lunar-Bench and ESI together provide a principled foundation for developing reliable systems for future missions.

We present Lunar-R1, an 8B-parameter reasoning model tailored for lunar science and mission operations, developed through continued pre-training, supervised fine-tuning, and reinforcement learning. To optimize the accuracy-efficiency trade-off, we introduce Latent Difficulty Perception (LDP), a lightweight mechanism that leverages final-layer hidden states to estimate query difficulty and regulates Chain-of-Thought generation length via difficulty-aware reward modeling. We construct Lunar-QA, an expert-verified benchmark derived from authentic lunar mission materials, and further evaluate on domain-specific tasks spanning astronomy and astrodynamics. Experimental results demonstrate that LDP reduces average token consumption by 38.9% while improving reasoning accuracy. Profiling on an RK3588 NPU yields 0.54 J/token energy efficiency and 380 ms time-to-first-token latency, validating feasibility for lunar surface edge deployment.