OpenAI's GPT-5.1 is an obsessive researcher, and Cline gives it focus

Over the past several weeks working with GPT-5.1 inside Cline, we watched something unexpected take shape. Coding agents have been evolving for years, progressing from simple code snippet generators to structured, multi-phase collaborators. GPT-5.1 represents a noticeable shift. It is not only a stronger model. It behaves differently, thinks differently, and expands what long-running coding work can reasonably achieve. Once we understood what the model was trying to do, we adapted Cline to help it do it better.

The model maps entire landscapes before acting

Early in our testing, it became clear that GPT-5.1 approaches problems with unusual depth. Earlier models typically gathered just enough context to move forward. GPT-5.1 continued reading, tracing, and synthesizing until it understood the entire terrain, even for tasks that only required a small fix.

Its summaries were exhaustive, sometimes more detailed than strictly necessary, but consistently coherent and grounded in the codebase. What looked at first like overthinking turned out to be systematic investigation with practical benefits for complex changes. However, this depth also meant that the model could drift into unnecessary exploration when left unbounded. This is where structure became essential.

Structure transforms instinct into discipline

GPT-5.1 responds to structure with an engineer's mindset. Specifications, phased workflows, todo lists, and other mechanical guidelines become scaffolding it uses to organize its research and execution.

Focus Chain proved especially valuable. By maintaining a persistent todo list that returns to context every six turns, the model gains a stable anchor point that prevents scope creep while preserving its deep research strengths. Instead of branching into unrelated investigations, it stays aligned with the task at hand.

This blend of strong investigative instinct and reliable structural guidance makes GPT-5.1 unusually capable in long-running, multi-phase engineering work.

Where GPT-5.1 truly excels: Plan Mode and /deep-planning

GPT-5.1's obsessive research nature makes it exceptional for workflows that separate planning from execution. When you use /deep-planning, the model systematically explores your codebase and produces implementation plans that are architectural blueprints—complete with exact file paths, function signatures, and execution sequences.

The key insight: GPT-5.1's thoroughness, which can feel excessive for quick fixes, becomes a superpower for complex features. Start in Plan Mode to let it research and architect, then switch to Act Mode with a comprehensive plan. This workflow turns the model's natural tendencies into structured, reliable execution.

Adapting Cline to make GPT-5.1 shine

Once we understood its tendencies, we reworked Cline's agent architecture to amplify its strengths.

We redesigned the agent prompt to be more explicit and execution focused. The updated persona provides clear guidance on how to use preambles, how to keep users informed, and how to work iteratively instead of attempting monolithic changes. The Plan versus Act split became sharper, with stricter criteria for when to remain in planning mode and when to request execution.

We also strengthened tool specifications to better match the realities of operating in a live workspace. Many long-standing quirks, such as premature terminal use, imprecise parameters, and ambiguous commands, were resolved by tightening the prompting around when and how tools should be invoked. Unlike earlier models, GPT-5.1 needed very few examples to internalize these rules. Often a single sentence with one example was enough to produce consistent, reliable behavior.

To support its deep research tendencies, we expanded Cline's workflow into five phases:

1. Silent reading for big-picture context
2. Silent terminal investigation for fine-grained details
3. Focused clarification questions
4. A written, scoped implementation plan
5. A self-contained execution task

Separating the two investigation phases allowed GPT-5.1 to pursue the breadth it naturally wants without overwhelming users or issuing premature tool calls. The resulting plans are richer, more precise, and more grounded in context than anything we observed with earlier frontier models.

Turning long tasks into building blocks

GPT-5.1 pairs naturally with Cline. Both systems value structure, clarity, and sustained reasoning. With Focus Chain enabled, GPT-5.1 maintains direction across long sequences of work, transitions cleanly from research to implementation, and produces thorough execution plans that hold up over time.

Even as we continue refining the workflow for smaller or routine tasks, GPT-5.1 already delivers some of the strongest coding agent performance we have seen. Its combination of depth, discipline, and stability turns long-running tasks into stepping stones for larger goals. This shift opens the door to ambitious work that previously felt out of reach, and we are excited to see what users build with it.

Getting started

GPT-5.1 and GPT-5.1-codex are available in Cline today through both the Cline and OpenAI providers. We recommend enabling native tool calling for the best results.

• GPT-5.1 offers a 272K context window
• GPT-5.1-codex extends that to 400K and is optimized for coding tasks
• Pricing: $1.25 per million input tokens and $10 per million output tokens