Introduction
OpenAI has officially released the full GPT-5.6 family: Sol, Terra, and Luna, putting the entire model lineup directly on the desktop. Users can now choose models based on task complexity, cost, and speed, similar to selecting coffee cup sizes. Sol is designed for heavy-duty tasks, Terra for balanced daily work, and Luna for lightweight, cost-effective operations.
Sol: The Flagship Model
Sol is the most powerful model in the lineup, prompting competitors to activate emergency response measures. Anthropic even delayed Fable 5's experience by a week and reset all user quotas, highlighting the intense competition in the AI space.
Practical Testing: Building a 3D FPS Game
We tested Sol's capabilities by creating a fully functional first-person shooter game using Three.js. The task required complete game mechanics, including movement, ammunition, health, enemy AI, and real GLB 3D models for assets.
Prompt Example
Use Next.js + TypeScript + react-three-fiber + Three.js to create a playable first-person FPS game demo. The game should feature complete movement, ammunition, health, enemy AI, and real GLB 3D models for core assets.Results
Sol successfully generated the entire game in 1.5 hours, including:
- Three game concept designs
- Free 3D asset sourcing from open-source communities
- Code generation for missing assets
- Full integration of all components
The game features smooth movement, shooting mechanics, enemy AI, and terminal destruction objectives, with only minor bugs that do not affect gameplay.
Terra: The Balanced Model
Terra is priced at half the cost of GPT-5.5 and offers balanced performance for daily tasks.
Practical Testing: 3D Voxel Rocket
We tested Terra by generating a 3D voxel rocket. The model completed the task in 35 minutes, with improved rocket design and UI compared to GPT-5.5. However, the physics engine still needs improvement, as the rocket tilts immediately after launch.
Luna: The Lightweight Model
Luna is the most cost-effective model, designed for lightweight tasks.
Practical Testing: Annual Report Generation
We tested Luna by generating an annual report from a large Excel dataset. The model completed the task in 49 minutes, producing a 47-page report with comprehensive data analysis, including:
- 13 worksheets and 26,712 transaction records
- Year-over-year and month-over-month comparisons
- Half-year and full-year summaries
- Second-half predictions
- Data integrity checks
The report had only minor issues, such as a Chinese garble, but overall was faster and more detailed than manual creation.
Conclusion
GPT-5.6 offers a model for every need:
- Sol: Best for heavy tasks, but high cost
- Terra: Balanced performance for daily use
- Luna: Cost-effective for lightweight tasks
Users can now choose the model that best fits their requirements, making GPT-5.6 a versatile solution for a wide range of applications.
常见问题
Which GPT-5.6 model should I use for my daily work?
For most daily tasks, Terra is the sweet spot. It matches GPT-5.5's performance at half the cost, which means you get the same quality you're used to while spending less. Use Sol when you need maximum capability: complex coding projects, multi-file refactors, research requiring deep reasoning, or agentic workflows that span many steps. Use Luna for high-volume, simple tasks: summarizing documents, formatting data, drafting emails, or generating basic reports. The cost difference is significant — Luna can handle 5-10x more tasks than Sol for the same token budget.
How does Sol's 1.5-hour FPS game build compare to what a human developer could do?
A human developer building the same FPS game from scratch — with movement, shooting, enemy AI, 3D asset sourcing, and full integration — would typically take 3-5 days. Sol's 1.5 hours includes generating three concept designs, sourcing free 3D assets, writing all the code, and integrating everything. The result had minor bugs but was fully playable. This represents roughly a 16-27x speedup. The caveat: the game is a demo, not a polished product. A human would spend additional time on polish, balance, and edge cases. But for prototyping and proof-of-concept, Sol's speed is transformative.
Is Luna actually useful, or is it too weak for real work?
Luna's 47-page annual report from 26,712 records proves it's not just a toy. The key is using Luna for the right tasks. It excels at data processing, report generation, summarization, and formatting — tasks where the bottleneck is volume, not reasoning depth. For creative writing, complex coding, or strategic analysis, use Terra or Sol. For routine tasks that would take you hours of manual work, Luna is surprisingly capable and dramatically cheaper. The Chinese garble issue in the test is a minor concern for Chinese-language users, but it's the kind of edge case that improves with each model iteration.
Will Terra eventually replace GPT-5.5 entirely?
OpenAI hasn't announced deprecation plans, but Terra's positioning — same performance at half the cost — makes GPT-5.5's continued relevance questionable. In practice, there's no reason to use GPT-5.5 over Terra for new projects. The only reason to keep using GPT-5.5 is if you have existing prompts or workflows that are finely tuned to it and haven't been tested on Terra yet. Given the cost savings, it's worth migrating those workflows sooner rather than later.