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Methodology

This list separates Planning & Research from Implementation to reflect two complementary competencies:
  1. Planning & Research — synthesizing large, messy contexts (repos, docs, tickets) into actionable plans that survive iteration.
  2. Implementation — turning plans into working code across multiple files with safe tool loops (git, shell, tests).
Each item is described with standardized fields:
  • Cost — what you typically pay (or relative to peers if pricing varies).
  • Limits — practical usage caps observed (messages/day, rate limits, or API-based).
  • Uptime — reliability trends in day-to-day work.
  • Output — what it’s best at + notable public benchmark signals.
  • Context — effective / max context (for Planning phase where it most matters).

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Ranking

  • Planning & Research
  • Implementation
Evaluates how well a model ingests large codebases and documentation and turns them into coherent, revisable plans. Performance depends on retaining critical details across many turns and on stable long-context behavior.
1

🔥 Gemini 3 Pro

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
Free in many Google products; usage-based via API and Vertex AI
Best fit when you need maximum planning power on huge, messy contexts. Excels at keeping multi-step strategies coherent over long horizons and complex tool chains, especially in Google-centric workflows.
2

🔥 GPT-5.1 Thinking

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
$20+/month (Plus, Pro, Business)
A strong default for structured research and planning, with reliable tool use and clear, revisable task breakdowns. Ideal if you already live in ChatGPT and want a simple, powerful upgrade path.
3

Opus 4.1 Thinking

  • Cost
  • Limits
  • Uptime
  • Output
  • Context
High-end API pricing (≈$15 / $75 per M tokens)
Well-suited to focused research and deep dives on clearly bounded questions. Caps and reliability issues make it less ideal for long-running, multi-day planning workflows.
NextJS Evals , ArtificalAnalysis

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Final Thoughts

No single tool dominates both planning and implementation. Gemini 3 Pro is now the top option for raw long-horizon planning and agentic research, especially when you can lean on its huge multimodal context and Google-native surfaces, while GPT-5.1 Thinking remains the default paid choice on the $20 tier if you want a single, stable research environment centered on ChatGPT. For hands-on implementation, Claude Code and GPT-5.1 Codex lead thanks to their tight native integrations with their own model stacks, while Kimi K2 Thinking and MiniMax M2 offer frontier-level agentic performance and pricing but shine most when you’re comfortable building your own API-driven tooling around them.