Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion

We present a systematic evaluation of large language model families – spanning both proprietary cloud APIs and locally-hosted open-source models – on two purpose-built benchmarks for System Dynamics AI assistance: the $\mathbf{\text{CLD}}\mathbf{\text{Leaderboard}}$ (53 tests, structured causal loop diagram extraction) and the $\mathbf{\text{Discussion}}\mathbf{\text{Leaderboard}}$ (interactive model discussion, feedback explanation, and model building coaching). On CLD extraction, cloud models achieve 77–89% overall pass rates; the best local model reaches 77% (Kimi~K2.5~GGUF~Q3, zero-shot engine), matching mid-tier cloud performance. On Discussion, the best local models achieve 50–100% on model building steps and 47–75% on feedback explanation, but only 0–50% on error fixing – a category dominated by long-context prompts that expose memory limits in local deployments. A central contribution of this paper is a systematic analysis of $\mathit{\text{model}}\mathit{\text{type}}\mathit{\text{effects}}$ on performance: we compare reasoning vs.\ instruction-tuned architectures, GGUF (llama.cpp) vs.\ MLX (mlx_lm) backends, and quantization levels (Q3 / Q4_K_M / MLX-3bit / MLX-4bit / MLX-6bit) across the same underlying model families. We find that backend choice has larger practical impact than quantization level: mlx_lm does not enforce JSON schema constraints, requiring explicit prompt-level JSON instructions, while llama.cpp grammar-constrained sampling handles JSON reliably but causes indefinite generation on long-context prompts for dense models. We document the full parameter sweep ($t$, $p$, $k$) for all local models, cleaned timing data (stuck requests excluded), and a practitioner guide for running 671B–123B parameter models on Apple~Silicon.