ChatGPT has made important upgrades to GPT-5 in the latest version, including three new operating modes (Auto, Fast, Thinking), ultra-large context support, message usage limit adjustments, and model selection regression. This update not only improves user flexibility in different scenarios, but also provides more room for long text processing and AI personalized interaction.
1. Differences between the three modes and applicable scenarios
- Auto mode
- the system automatically determines the speed and depth required by the current task.
- suitable for most daily questioning and work scenarios.
- Fast mode
- prioritizes response speed and reduces complex inference steps.
- is suitable for quick queries, short conversations, and ad-hoc needs.
- Thinking mode
- activates deep reasoning capabilities, which is suitable for complex tasks and multi-step logic analysis.
- supports ultra-long contexts and can process 196,000 tokens at a time.
2. Message limit and performance adjustment
- The Thinking mode can use 3,000 messages per week.
- After exceeding the limit, you will switch to GPT-5 Thinking mini, which has lighter performance, to continue using it.
- The cap may be dynamically adjusted based on the user's overall usage.
3. Model Selection Regression and New Options
- GPT-4o is reopened to all paying users as an optional model.
- A new "Show more models" switch has been added to the settings, which can enable additional models such as o3, 4.1, GPT-5 Thinking mini, etc.
- GPT-4.5 is only available to Pro users due to its high computational cost.
4. AI personalization direction
- GPT-5's personalized tone is being optimized and will be warmer than the current version, but avoid excessive anthropomorphism.
- OpenAI plans to open up user-defined personality functions in the future, allowing different users to customize the AI's communication style.
Frequently Asked Questions
Q: How to choose between Auto, Fast, and Thinking modes?
A: Generally, Auto is fine, Fast is suitable for pursuing response speed, and Thinking is suitable for complex reasoning and long text analysis.
Q: What is the 196k context used for?
A: It can handle very long documents, codebases, or multi-turn conversations without losing context.
Q: Why is there a message cap?
A: The limit is to balance server resources and ensure the stability of the deep inference mode.
