Skip to content
MTP (Mapping the Prompt) MTP Docs

open: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: open:100 (slider)

Input

Section titled “Input”

Prompt

/mtp open: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.

Yes: an up-to-date ranking against Claude, Gemini, Grok, DeepSeek, Llama, Mistral, etc. requires current information, because model releases and benchmark standings change every few weeks. I checked current public sources, but I’d still treat exact “best model” claims as temporary.


My main strengths are:


1. **Agentic software engineering**
   I’m strongest when the task is not just “write code,” but inspect a repo, reason through constraints, edit files, run tests, debug failures, and iterate. OpenAI positions GPT-5.3-Codex as optimized for long-running coding, terminal use, research, and complex execution, with strong results on SWE-Bench Pro, Terminal-Bench, OSWorld, and cybersecurity CTF-style evals. Source: [OpenAI GPT-5.3-Codex](https://openai.com/index/introducing-gpt-5-3-codex)


2. **Practical tool use inside a real workspace**
   In this environment, I can read files, modify code, run commands, inspect outputs, keep track of dirty worktrees, and verify changes. That makes me useful as an engineering collaborator rather than only a chat model.


3. **End-to-end task persistence**
   I’m designed to carry work through planning, implementation, verification, and concise handoff. That is especially useful for migrations, refactors, bug fixes, test repair, documentation updates, and multi-step operational tasks.


4. **Balanced reasoning plus execution**
   Some competing models may be stronger for particular modes: Claude Opus 4.7 emphasizes long-running coding, instruction following, memory, high-resolution vision, and polished enterprise work; Google Gemini 3 emphasizes multimodal reasoning, web/app generation, and Google ecosystem integration. Sources: [Anthropic Claude Opus 4.7](https://www.anthropic.com/news/claude-opus-4-7), [Google Gemini 3](https://blog.google/products/gemini/gemini-3)


5. **Safety and controlled autonomy**
   I work well where you want an agent to act, but with visible reasoning, bounded file edits, sandboxing, approvals for risky actions, and explicit verification. That matters for production codebases.


Where others may be preferable: Claude often has a reputation for polished prose, careful instruction following, and design taste; Gemini is often compelling for multimodal and Google-integrated workflows; Llama/DeepSeek/Mistral-style open or lower-cost models can be better when self-hosting, privacy, or price matters more than top closed-model capability.


So my strongest lane is: **complex software and knowledge-work execution in a real environment, especially when the task requires reading context, changing artifacts, using tools, and validating the result.** Which comparison axis matters most to you: coding, research, writing, multimodal, cost, privacy, or enterprise deployment?