Cline & Our Commitment to Open Source - zAI GLM 4.6

Large language models designed for coding vary in how much structural guidance they require to operate effectively within agentic frameworks. Frontier, general-purpose models have historically needed extensive prompting to understand what it means to behave as an AI coding agent. This is because the native environment for general-purpose models is the world. Contrastingly, for specialized coding models, it’s your codebase. For models like GLM-4.6, the context surrounding coding-agent behavior is largely implicit, and would be redundant to include in a system prompt.

At Cline, we found GLM-4.6 performed best when given shorter, more explicit, and mechanically precise instructions. As part of our commitment to open-source ecosystems, we invested time and tokens to tune Cline’s system prompt for compatibility, enabling users to explore high-performance open models with the same reliability they expect from proprietary ones.

Go big by going small

GLM-4.6 exhibited a strong innate understanding of code-editing workflows and tool semantics. This allowed us to remove general behavioral guidance and concentrate on technical precision:

• Concise structure. Redundant narrative text was eliminated. The retained content focused on parameter definitions, execution order, and canonical examples.
• Reduced behavioral overhead. We removed generic behavioral instructions such as “Use tools to complete the task efficiently,” which GLM-4.6 already demonstrated understanding of. This allowed the prompt to focus solely on task-specific execution details rather than reiterating basic agent behavior.
• Explicit invocation rules. Early tests revealed a tendency to invoke tool calls in improper contexts or with hallucinated parameters. Tightened prompting around invocation scope largely resolved this behavior.
• Strict sequence adherence. To ensure the model always gathered sufficient context before modification, we emphasized a structured workflow: explore → summarize → implement. This reinforced consistent reasoning prior to code changes.

These and related adjustments reduced Cline’s GLM system prompt from 56,499 to 24,111 characters- a 57% reduction. All while improving latency, lowering token costs, and increasing task success rates.

Open source inference isn’t easy: variance in provider quality

During the initial evaluation of GLM-4.6, we encountered several breaking behaviors that prevented stable execution within Cline. Tool calls occasionally failed due to hallucinated or malformed parameters, and in other cases were emitted inside reasoning traces rather than completions. Some responses also contained spurious <think> tags and other content the model was not prompted to use.

While these artifacts helped expose a minor bug in Cline’s own tool-execution layer (the silver lining), they also highlighted a deeper issue: provider-level inference variance. When hosted by different providers, the same model produced markedly different outputs, ranging from fully functional to completely unusable generations. In the case of GLM-4.6, these differences were not minor variations in accuracy; they determined whether the model could operate at all within Cline’s agentic framework.

While addressing these issues, OpenRouter introduced the new :exacto endpoint, described as routing requests to inference backends “that have measurably better tool-use success rates.” At the time, we were midway through refining Cline’s GLM-4.6 prompt, so the release was well-timed and its impact was immediate. Before :exacto, running GLM-4.6 through OpenRouter frequently resulted in tool calls emitted within reasoning traces, hallucinated parameters, and other structural failures. After switching to :exacto, those issues disappeared: tool calls executed correctly, and the model maintained focus on the coding task itself. GLM-4.6 shifted from an intermittently broken, often unusable state to one that was stable and performant.

Members of the Cline team conducted a comparative analysis across multiple providers and found that unknown endpoint quality and suspected quantization strategy were the primary differentiators. Lower-quantization or aggressively optimized endpoints frequently introduced structural corruption in tool calls. Model lab Moonshot released analysis on the topic when this was reported with their (also open source) models. Similar findings have been observed by others in the community, and some emerging providers even advertise routing strategies designed to direct requests to higher-quality inference endpoints. OpenRouter’s :exacto claims to address poor endpoint performance, yet leaves the cause unattributed. Yet another solution is to run your own inference locally or in the cloud, an attractive option for security and quality conscious teams.

Responsibility

Supporting open-source models in enterprise-grade coding agents requires more than choosing which model to run. It depends on precise prompting, rigorous evaluation, and high-integrity inference infrastructure. The integration of GLM-4.6 in Cline demonstrates that open models can deliver stable, high-throughput coding performance when the surrounding system (prompt design, routing, and validation) is engineered with care.

However, inconsistent or misleading performance from some inference endpoints poses a material risk to the open-source AI ecosystem. When users encounter wide variability across providers hosting the same model, it erodes confidence in the model itself rather than in the infrastructure. Over time, this undermines the collective credibility and perceived maturity of open systems competing with proprietary alternatives.

Cline’s position is that reliability must be a shared responsibility among model developers, hosting providers, and downstream integrators. Transparent reporting of quantization settings, throughput trade-offs, and observed behavioral differences should become standard practice. Excessive quantization or optimization that degrades inference quality may yield greater margins for some, but weakens the broader trust required for open models to compete, and hopefully thrive for all.

Our goal is to see both open and proprietary research efforts succeed. Healthy competition between frontier and open initiatives accelerates technical progress and ensures diversity in model design and governance. Sustaining that balance depends on consistent, verifiable inference quality. When developers choose an open model, they should trust not only the weights, but the infrastructure that serves them.

Maximizing results with open source models & Cline

Provide the model with clear direction by using file mentions, deep-planning, and well-defined task plans to ensure it has sufficient context to succeed. Although Cline is capable of exploring autonomously, your own understanding of the project’s structure and intent can significantly improve how effectively it reasons about the codebase. By steering the model toward relevant files and implementation details early on, you give it a stronger foundation for accurate edits and reduce the likelihood of misaligned changes later in the process.

When using a new model (whether open source or proprietary), it is best to temporarily disable auto-approve for the first few tasks and review each action closely. This allows you to observe how the model interprets your requests, what assumptions it makes, and how it handles unfamiliar patterns or tool behaviors. Becoming familiar with its response style and decision-making tendencies will help you identify potential issues before they affect your codebase. Over time, this understanding enables you to tailor your prompting more effectively, improve reliability, and make better use of each model’s strengths.

Try GLM-4.6 (and other open source models) yourself and share constructive feedback with the Cline community. Model-specific prompting is an ongoing area of development, and insights from real users are essential to refining how Cline interacts with open source models. Each new model introduces its own response patterns, reasoning style, and sensitivities to instruction phrasing, all of which become clearer only through repeated, real-world use. By reporting successes, failures, and unexpected behaviors, users help shape a more robust integration pipeline for everyone. Identifying and sharing these nuances not only accelerates the improvement of Cline’s model adapters, but also contributes to a broader understanding of how open source models can be optimized for agentic workflows.

To achieve the best performance from open source models in any use case, use endpoints with verified quantization quality or preferred routing, and avoid those that consistently produce poor results. Or even easier, let us do it for you or your team.

Results: open models inching towards parity

• Stable tool execution across all GLM-4.6 inference routes
• Reduced latency and token usage without loss of reasoning quality
• Significantly higher success rates on multi-file, tool-heavy tasks

Through refined system prompts, structured workflow enforcement, and the use of verified inference endpoints like OpenRouter’s :exacto, we achieved measurable improvements in stability and throughput using GLM-4.6. Similarly, enhanced support for Qwen3 Coder, DeepSeek, and additional open models is on the way. These changes significantly improve the reliability of open models, bringing them closer to parity with proprietary alternatives. Cline inference users can take advantage of these improvements immediately.

As supporters of open source and advocates for accessible, evolving AI, we believe the future of intelligent tools depends on a strong and sustainable open ecosystem. Progress in capability, affordability, and transparency all stem from the health of that community. We’re committed to doing our part, and you can too. Use open models, share your experiences, and help refine the technology through conversation and feedback. Together, we can push open AI forward and keep its benefits within everyone’s reach.