Capable coding models need less behavioral prompting and more precise tool rules. Trimming the prompt cut cost and latency, and inference provider variance is a real reliability risk for open models.
Key takeaways · AI-distilled
Cline cut its GLM-4.6 system promptThe standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.Full definition → from 56,499 to 24,111 characters by dropping generic AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → guidance the model already followed, such as 'use tools to complete the task efficiently', and keeping parameter definitions, execution order and canonical examples.
Early GLM-4.6 tests showed tool calls in improper contexts or with hallucinated parameters. Cline says tighter rules on invocation scope largely fixed this, and an explore, summarize, implement sequence made the model gather context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → before editing.
The same GLM-4.6 weights behaved very differently by host: some providers produced tool calls inside reasoning traces, malformed parameters or stray <think> tags. Cline's comparison pointed to endpoint quality and suspected quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition → as the main differences.
After Cline switched to OpenRouter's :exacto endpoint, which routes to backends with measurably better tool-use success, those structural failures disappeared in its tests, though Cline notes :exacto does not say what caused the poor endpoints.
Cline asks hosting providers to publish quantization settings and throughput trade-offs, and recommends turning off auto-approve for the first few tasks with any new model to watch how it interprets requests.
Terms in this piece · Glossary
system prompt — The standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
