[KongchangAI]
Model

Le Chonk

A model for visual grounding tasks, focused on object localization in dense scenes. It achieves 42% on the Dense 200 benchmark and is claimed to surpass frontier closed-source models.

Timeline (last 90 days)

Oct 13

Le Chonk achieved a score of 42% on the Dense 200 benchmark.

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Oct 13

Le Chonk is claimed to surpass frontier closed-source models.

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Oct 11

Le Chonk scored 10/10 on the local fine-tuning task, completing the full pipeline of generating a panda knowledge dataset, locally fine-tuning a Gemma 2B model, training a LoRA adapter for 400 steps, and reducing validation loss from approximately 7.97 to 0.31.

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Oct 11

In the default OpenCode run, Le Chonk used up the entire reasoning round on the contact lens case task without creating any files, scoring zero points.

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Oct 11

Le Chonk scored a total of 42/80, or 52.5%, equivalent to 5.25/10, across the eight questions in KingBench3.

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Oct 11

When browser error messages were passed back to Le Chonk verbatim, it successfully fixed the module path issue in the 3D wristwatch project, scoring 6/10 after the fix.

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Oct 11

The tester recommends Le Chonk for controlled visualization prototyping with confidence when paired with a browser and testing loop, but does not recommend it as a default coding model for longer, less error-tolerant work.

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Oct 11

When the contact lens case task was re-run using OpenCode's "no reasoning" mode, Le Chonk successfully produced a box whose left and right lids can open independently.

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Oct 11

Le Chonk scored 9.5/10 on the elevator simulation task, delivering a functional three-elevator setup with genuinely working queue logic.

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