GoKawiilBottleCap AI releases ThinkingCap for Google's Gemma-4-26B-A4B model
BottleCap AI has published a fifth ThinkingCap model, this time trained on Google's Gemma-4-26B-A4B-it, cutting the number of 'thinking' tokens the model uses by up to 69.1% and an average of 38.9% across twelve benchmarks. The company says accuracy drops by only 0.15 percentage points, and that looping behavior fell from 3.6% of responses to 0.8%. A PRO version is also offered, claiming up to 87.6% fewer thinking tokens (62% average) for enterprise customers.
GoKawiil's interpretation of the reporting above, not reported fact.
This is BottleCap AI's first ThinkingCap applied to a Google model, following earlier work on Qwen and NVIDIA models, suggesting the company is building a broader portfolio of efficiency-focused fine-tunes across major model families. Lower thinking-token counts could translate into faster responses and reduced inference costs for teams running reasoning models at scale, which the company is explicitly marketing toward enterprise buyers via its PRO tier. The claimed accuracy impact is self-reported and measured by BottleCap AI itself, so independent verification would be needed to confirm the tradeoffs hold across broader use cases.
- ThinkingCap-Gemma-4-26B-A4B cuts thinking tokens by up to 69.1%, averaging 38.9% across twelve benchmarks.
- Reported accuracy cost is just 0.15 percentage points, the smallest of BottleCap AI's five ThinkingCap releases so far.
- A PRO version offers steeper token reductions (up to 87.6%) and is being pitched to enterprise customers for cost and speed gains.
Source: bottlecapai.com, 2026-10-23
Published there as: “Gemma-4-26B-A4B with 39-69% less thinking at same accuracy”
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