MarioGPT
Generate Super Mario levels from text prompts using GPT2
A fascinating research prototype for text-to-level generation, but not a production-ready tool. Best for AI researchers and hobbyists; indie devs should expect to polish outputs heavily.
- Game AI researchers exploring procedural content generation via NLP
- Indie developers prototyping Mario-like level layouts
- ML enthusiasts learning text-conditional generative models
- Educators teaching generative AI in game design
- Commercial game production (outputs need heavy manual polishing)
- Users without Python and ML environment setup experience
- Generating levels outside the Super Mario Bros tile style
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In short
MarioGPT — Generate Super Mario levels from text prompts using GPT2. Best for Game AI researchers exploring procedural content generation via NLP, Indie developers prototyping Mario-like level layouts, ML enthusiasts learning text-conditional generative models. Free to use.
Viability Score
How likely is MarioGPT to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Text-to-level generation from simple prompts
- Fine-tuned on Super Mario Bros and The Lost Levels
- Based on distilgpt2 for efficient inference
- Conditional generation: enemies, pipes, blocks, elevation
- Pretrained model weights on Hugging Face
- Jupyter notebooks for training and sampling
- MIT license for open use
- Outputs tile-based level representations
- Interactive play via play() method (Java required)
- A* agent for automated level testing
- Inpainting functionality from research paper
- Open-ended level generation for diverse outputs
- Hugging Face demo for browser-based generation
- Directly load and play from text file
- Continue generation from seed level
About MarioGPT
MarioGPT is an open-source research project that fine-tunes a GPT2 model (distilgpt2) to generate playable Super Mario Bros and The Lost Levels levels from simple text descriptions. Trained on the Video Game Level Corpus, it outputs tile-based level layouts that can be directly used in game engines. While generations aren't always perfect, MarioGPT represents a significant step toward controllable and diverse procedural content generation via natural language. Users can prompt the model with phrases like 'many pipes and enemies' to influence level structure. The project is primarily aimed at game AI researchers, indie developers, and enthusiasts exploring text-to-level generation. Code, pretrained models, and notebooks for training and sampling are freely available on GitHub under an MIT license. A Hugging Face demo allows browser-based generation and play, and the model supports conditional generation for enemies, pipes, blocks, and elevation. Compared to commercial level editors, MarioGPT offers unprecedented prompt-based control but requires manual curation for polished gameplay.
Behind the Verdict
MarioGPT is a compelling peek into the future of procedural content generation. Its ability to convert natural language prompts like 'many pipes, many enemies' into playable Mario levels is genuinely impressive. However, the output quality is inconsistent—generations often require manual tweaking to be fully playable. This is a tool for researchers and tinkerers, not for shipping a game. If you're an indie dev looking to quickly prototype level ideas, it can be a useful brainstorming aid, but don't expect drop-in assets. Compared to traditional level editors, MarioGPT offers a radically different approach: you iterate on prompts rather than tiles. The codebase is well-documented, with notebooks for training and sampling, and the MIT license makes it easy to experiment. But be prepared for a learning curve: you'll need Python, a basic ML environment, and Java for the interactive play feature. The Hugging Face demo lowers the barrier for non-coders, but it's still limited. In our view, MarioGPT is best for sparking ideas and demonstrating a concept, not for daily game development.
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Use Cases
- Experiment with text prompts to generate varied Super Mario level layouts
- Train the model on custom level datasets for new game styles
- Use generated levels as prototypes or inspiration for game design
- Integrate the generation pipeline into game development workflows via CLI
- Study the capabilities and limits of language models for structured output tasks
Models Under the Hood
as of 2026-07-18
Limitations
- Level generation quality is inconsistent and may produce unplayable sections.
- The model is limited to the tile set and patterns of the original Super Mario Bros games, so creative prompts outside that scope may not work well.
- No API or hosted service is provided; users must run the code locally.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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