Gemma-4-26B-A4B with 39-69% less thinking at same accuracy

TL;DRThis is the fifth model in the ThinkingCap series. We reduced how much Google's gemma-4-26B-A4B-it thinks, without compromising the quality of its answers. Up to 69.1% reduction in thinking tokens, on average 38.9% measured across twelve benchmarks, for 0.15pp of accuracy The first ThinkingCap on a Google model, and the smallest accuracy cost of any ThinkingCap so far Looping drops from 3.6% of the original model's responses to 0.8% of ours Same sampling settings (thinking on, temperature 1.0, top_p 0.95), same serving stack (vLLM), drop-in replacement ThinkingCap PRO: want even better efficiency? The PRO version of this model uses up to 87.6% fewer thinking tokens, 62% on average. Faster answers and lower inference costs at scale. Contact us at enterprise@bottlecapai.com. Our fifth ThinkingCap We have published four token-efficient models so far: ThinkingCap-Qwen3.6-27B, ThinkingCap-Qwen3.8-27B, ThinkingCap-NVIDIA-Nemotron-3.5-Lightning-30B-A3B and ThinkingCap: Qwen3.8-Flash-Next. gemma-4-26B-A4B is Google's sparse mixture of experts — 26B parameters with about 4B active per token. It is the third model family we have applied this work to, after Qwen and NVIDIA. On this one we put extra effort into keeping the accuracy intact: thinking comes down 38.9% on average at a cost of only 0.15pp of accuracy, which is smaller than the run-to-run spread on most of the suite. Video comparison of the original model and the ThinkingCap model, prompted with the same input. Both models run on the same hardware and use the same inference settings. The training objective We start with gemma-4-26B-A4B-it. The goal is to spend fewer tokens getting to the answer, and change nothing else: preserve the knowledge, reasoning ability, answer quality, instruction following and safety behaviour. Results Accuracy per task family, gemma-4-26B-A4B-it against the ThinkingCap model Accuracy barely moves in any family, and in two it moves up. Instruction following and agentic gains 0.65pp and maths gains 0.39pp; code is flat at 0.02pp; knowledge and reasoning costs 0.51pp and long-context and multimodal 0.92pp. Averaged over all twelve benchmarks the change is −0.15pp. Mean thinking tokens per task family, gemma-4-26B-A4B-it against the ThinkingCap model The thinking is where the change is, and every family gets shorter. Long-context and multimodal reduces the most, 54.4%, then knowledge and reasoning at 44.4%, maths at 36.1% and code at 27.6%; instruction following and agentic …