Show HN: TetrisBench – Gemini Flash reaches 66% win rate on Tetris against Opus

Re:Show HN: TetrisBench – Gemini Flash reaches 66% win rate ... by ykhli on Monday January 26, 2026 @04:42PM (#46771967)

answered this in a comment above! It's not turn or visual layout based since LLMs are not trained that way. The representation is a JSON structure, but LLMs plug in algorithms and keeps optimizing it as the game state evolves

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Re:Show HN: TetrisBench – Gemini Flash reaches 66% win rate ... by vunderba on Monday January 26, 2026 @05:37PM (#46772614)

Thanks for the clarification! Kind of reminds me of the Brian Moore's AI clocks which uses several LLMs to continuously generate HTML/CSS to create an analog clock for comparisons.

https://clocks.brianmoore.com

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Re:Show HN: TetrisBench – Gemini Flash reaches 66% win rate ... by ykhli on Monday January 26, 2026 @06:17PM (#46773101)

Wow this is incredible!!

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Re:Show HN: TetrisBench – Gemini Flash reaches 66% win rate ... by mhh__ on Monday January 26, 2026 @06:22PM (#46773153)

I suppose you could argue about whether it's an LLM at that point but vision is a huge part of frontier models now, no?

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Re:Show HN: TetrisBench – Gemini Flash reaches 66% win rate ... by storystarling on Tuesday January 27, 2026 @04:21AM (#46777500)

Curious how the token economics compare here to a standard agent loop. It seems like if you're using the LLM as a JIT to optimize the algorithm as the game evolves, the context accumulation would get expensive fast even with Flash pricing.

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