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    MiniCPM5-2B is token-efficient for a reasoning model, Artificial Analysis says

    Artificial Analysis reports 19k output tokens per Intelligence Index task for MiniCPM5-2B, compared with 56k for Ling 3.0 Tiny and 33k for Granite 4.2 8B.

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    2 Sources, 23d ago, first seen 23d ago

    TLDR

    Artificial Analysis reports that MiniCPM5-2B uses 19k output tokens per task on its Intelligence Index, including 11k reasoning tokens. That compares with 56k output tokens for Ling 3.0 Tiny and 33k for Granite 4.2 8B. The organization says output token use matters for the on-device and edge deployments the 2.6B model targets.

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    2 Sources, first seen 23d ago

    Combined views

    1.7K

    2 Sources, first seen 23d ago

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    2 Sources

    @ArtificialAnlysMiniCPM5-2B is also token-efficient for a reasoning model. It uses 19k output tokens per Artificial Analysis Intelligence Index task, 11k of them reasoning tokens, against 56k for Ling 3.0 Tiny and 33k for Granite 4.2 8B. Output token use matters for the on-device and edge deployments a 2.6B model targets.

    2 Sources

    @ArtificialAnlysMiniCPM5-2B is also token-efficient for a reasoning model. It uses 19k output tokens per Artificial Analysis Intelligence Index task, 11k of them reasoning tokens, against 56k for Ling 3.0 Tiny and 33k for Granite 4.2 8B. Output token use matters for the on-device and edge deployments a 2.6B model targets.