Gemma 4 dropped a 12B.
I put it on RTX 5090 against its 31B sibling.
when you cut a model from 31B to 12B, what do you actually lose?
~ reasoning barely moves
GSM8K (math) 97.5 > 96.4 (−1.1)
ARC-C (sci reasoning) 97.6 > 94.0 (−3.6)
~ knowledge falls off a cliff
MMLU (world knowledge) 87.8 > 78.9 (−8.9)
HellaSwag (commonsense) 92.0 > 81.6 (−10.4)
~~~
parameters store facts, not thinking. the 19B you delete is mostly where the model kept its trivia and world-priors, cut it and recall collapses, while the reasoning machinery stays nearly whole.
a 12B reasons almost like its big brother. It just knows less.
122 tok/s vs 53 (2.3x faster generation), ~10GB instead of ~24, meaning that you get 20GB+ free on a 32GB card for long context or a second model.
so it depends of your workload:
reasoning / math / agentic loops = the 12B is nearly free
broad-knowledge Q&A with no retrieval = that's the one job worth paying for the 31B.
OpenClaw 2026.3.28 🦞
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🩹WhatsApp echo loop, Telegram splitting, Discord reconnect fixes
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OpenClaw 2026.3.22 🦞
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🤖 MiniMax M2.7, GPT-5.4-mini/nano + per-agent reasoning
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This release is so big it needs its own table of contents. https://t.co/XvRbXEduGC
✨ BotDrop v0.2.11: New Dashboard + Offline-Ready
🎨 New Dashboard
Card-based layout with status, model, channels at a glance. New Settings page for version management.
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OpenClaw and QQ Bot plugin now ship with the APK. Works out of the box, no network issues. Switch versions anytime in Settings.
⚡ Fixes
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OpenClaw 2026.3.13 🦞
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📱 android redesigned & down to 7MB, iOS gets welcome pager
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