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power:50 | ChatGPT 5.5 | Comparison Task

Metadata

  • Date: 2026-04-30
  • Tool: Codex (macOS app)
  • Model: ChatGPT 5.5 (Medium)
  • MTP Skill version: 1.0.0
  • MTP Skill arguments: power:50 (slider)

Input

Section titled “Input”

Prompt

/mtp power:50 Compared with other major AI models from competing companies, please explain your strengths. If up-to-date comparison requires current information, say so clearly.

Output

Section titled “Output”

The text below is copied from the tool output using the “Copy” button.

Yes: an up-to-date comparison does require current information. Model rankings change monthly, and companies publish overlapping benchmark claims. As of my current check on April 30, 2026, my main strengths are:


**My strongest area is agentic software work.** I’m designed to operate like a coding collaborator inside a real workspace: inspect files, edit code, run tests, diagnose failures, preserve context, and keep working through multi-step implementation tasks. OpenAI describes GPT-5.3-Codex as optimized for long-running research, tool use, complex execution, SWE-Bench Pro, Terminal-Bench, and broader professional computer work. [Source](https://openai.com/index/introducing-gpt-5-3-codex)


**I’m especially useful when the task is not just “answer this,” but “change the system.”** Compared with general chat models, my advantage is practical execution: repository navigation, patching, verification, command-line work, debugging, code review, and producing usable artifacts. That makes me strongest for engineering workflows, refactors, migrations, test repair, PR review, docs tied to code, and frontend iteration.


**I’m good at balancing autonomy with steerability.** OpenAI’s Codex positioning emphasizes both independent long tasks and interactive collaboration. In practice, that means I can take a goal, make scoped edits, report tradeoffs, and adjust direction without throwing away context.


Where competitors may be stronger:


- **Claude** is very strong in careful reasoning, code review, long-context work, and enterprise document workflows; Anthropic says Claude Opus 4.6 has a 1M-token context window in beta and strong Terminal-Bench/HLE performance. [Source](https://www.anthropic.com/news/claude-opus-4-6)
- **Gemini** is especially strong when Google ecosystem integration, multimodality, visual reasoning, and very interactive web/app generation matter; Google reports strong Gemini 3 coding and multimodal benchmarks. [Source](https://blog.google/products/gemini/gemini-3)
- **Grok/xAI** emphasizes very large context windows, speed, and agentic tool-calling; xAI lists 2M context for recent Grok API models. [Source](https://x.ai/api)
- **Open-weight models** from Meta, Mistral, DeepSeek, and others can be preferable when self-hosting, customization, cost control, or data locality matters more than absolute frontier capability.


My short version: **I’m strongest when you want an AI to work inside a real development environment and deliver changes, not just discuss them.** For pure benchmark leadership, the answer is time-sensitive and depends heavily on the exact task, benchmark, price tier, and product surface.