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still:100 | 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: still:100 (slider)

Input

Section titled “Input”

Prompt

/mtp still:100 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.

Up-to-date comparison does require current information. Model lineups, pricing, benchmarks, and product integrations change quickly, so any ranking can go stale.


My main strengths, compared with other major AI models, are:


1. **Coding-agent work**
   I am strongest when the task is not just “answer a question,” but inspect a repo, edit files, run commands, test, debug, and iterate. OpenAI positions GPT-5.1-Codex as optimized for agentic coding, with long-running task support and a large context window. Source: [OpenAI GPT-5.1-Codex-Max](https://openai.com/index/gpt-5-1-codex-max/), [OpenAI model docs](https://platform.openai.com/docs/models/gpt-5.1-codex)


2. **Practical software engineering workflow**
   I am built to work like a coding collaborator: read existing patterns, make scoped changes, avoid unrelated churn, run verification, and explain the result. That is a narrower but more practical strength than general chat quality alone.


3. **Tool use and workspace execution**
   In this environment, I can operate directly on local files, run tests, inspect terminal output, use browser verification for web apps, and integrate with GitHub/Vercel/Figma-style workflows when available. That makes me useful for completing tasks, not just describing how to complete them.


4. **Balanced reasoning plus implementation**
   Compared with models that may be especially strong in prose, vision, or broad consumer assistant tasks, my advantage is the combination of reasoning, code modification, debugging discipline, and execution.


5. **Long-context coding focus**
   OpenAI describes Codex models as built for detailed work across large code contexts, including compaction over multiple context windows. That matters for large repos and multi-step engineering tasks.


Competitor caveat: Anthropic’s Claude line is also very strong for coding and agents, with Opus 4.5 described by Anthropic as a frontier coding/agent model. Google’s Gemini line is especially strong in multimodal and visual/spatial reasoning, with Gemini 3/3.1 emphasizing reasoning, multimodality, and Google ecosystem integration. Sources: [Anthropic Claude Opus](https://www.anthropic.com/claude/opus?s=09), [Google Gemini 3](https://blog.google/products/gemini/gemini-3), [Google Gemini 3.1 Pro](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/)


Short version: my clearest strength is **turning software tasks into concrete repo changes with verification**, rather than only giving polished answers.