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Methodology

This ranking separates Planning & Research from Implementation to reflect two distinct but complementary competencies: synthesizing large, messy contexts and turning plans into working code with reliable tooling loops. The goal is to surface which choices deliver dependable outcomes for multi-file codebases and real tasks rather than synthetic prompts alone.
  • Planning & Research
  • Implementation
This tab measures how well a model ingests long documents, multifile repos, and mixed artifacts, then produces structured analyses, plans, and tactics that survive iteration and partial ambiguity.Capacity to hold and reason over large contexts, and to preserve salient details across iterative turns, is central to performance here.
1

🔥 Gemini 2.5 Pro (Max Thinking Budget)

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
Free (AI Studio)
Dominates long-context planning and research with massive effective token windows. Unbeatable value proposition for large-scale codebase analysis with no cost or usage barriers.
2

GPT-5 High

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
$20+
Strong at structured task decomposition with reliable uptime. Best choice for iterative planning workflows that require consistent availability over extended sessions.
3

Opus 4.1 Thinking

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
$20+. API is most expensive
Delivers high-quality analysis for well-scoped tasks but severely hampered by restrictive limits and poor reliability. Difficult to recommend given availability issues and premium pricing.

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Final Thoughts

No single tool dominates both planning and implementation. Gemini 2.5 Pro’s unlimited free access and massive context windows make it unbeatable for research and codebase analysis, while premium implementation tools like Auggie and GLM 4.6 deliver the best reliability and value for hands-on coding. The key is matching tool choice to task type: free, long-context models for exploration and understanding, paid agentic CLIs for execution and iteration. As reliability issues plague premium services and usage limits tighten, prioritizing uptime and generous allowances becomes as important as raw output quality.
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