haze: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:
haze:50(slider)
Prompt
/mtp haze: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.
Up-to-date comparison does require current information. The frontier shifts fast, and benchmark positions can change within weeks. I checked current public pages dated through April 30, 2026, but treat any “best” claim as a snapshot, especially when it comes from a model vendor.
My main strengths, compared with other major models, tend to be:
1. **Agentic coding and real software work** In Codex, OpenAI positions GPT-5.5 as strongest around long-running engineering tasks: reading large codebases, planning edits, using tools, testing, debugging, and iterating. OpenAI reports GPT-5.5 at **82.7% on Terminal-Bench 2.0**, compared with **69.4% for Claude Opus 4.7** and **68.5% for Gemini 3.1 Pro** on its published table. Source: [OpenAI GPT-5.5 announcement](https://openai.com/index/introducing-gpt-5-5/).
2. **Computer-work loops, not just answers** My edge is often less “single brilliant answer” and more the whole loop: understand the goal, use files/tools, inspect results, revise, and keep context alive. That is useful for coding, research, data analysis, docs, spreadsheets, and operational work.
3. **Tool use and execution discipline** In this Codex setting, I can actually work inside a repo, run commands, edit files, inspect failures, and verify changes. Some competing models may reason very strongly, but the practical experience depends heavily on the surrounding agent shell.
4. **Professional knowledge-work breadth** OpenAI claims GPT-5.5 is strong across coding, online research, data analysis, documents, spreadsheets, slides, and computer use. That breadth is a real advantage when a task crosses boundaries: for example, “analyze this CSV, update the app, write the migration notes, and verify the UI.”
5. **Concise collaboration with steerability** My useful center of gravity is often: give a plan, do the work, surface risks, and keep moving. Claude is also notably strong here, especially in writing and careful reasoning; Gemini often has a strong multimodal and Google ecosystem story. But my particular shape in Codex leans toward execution inside an active workspace.
The comparison is not one-way. Anthropic’s latest Claude pages describe Opus 4.7 as stronger across coding, agents, vision, and multi-step tasks, and Google’s Gemini pages emphasize reasoning, multimodality, and product integration: [Anthropic Newsroom](https://www.anthropic.com/news), [Google Gemini updates](https://blog.google/products-and-platforms/products/gemini/). So the honest answer is: my clearest strength is not merely “being smarter,” but being a capable agent in a working environment, especially when the task involves code, tools, files, and sustained execution.