Thinkingcap
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- Qwen3.8 27B
Qwen
· Open weights
A 27-billion-parameter vision-language model for text, images, and video. Supports coding and multi-step tasks, with optional thinking and configurable reasoning effort.
1 story · 1 report on this topic
- Qwen3.8 Flash
Qwen
· Open weights
An open-weight preview of Qwen’s next architecture, combining sparse and linear attention. A vision-language model designed to make long-context agent workloads more efficient.
1 story · 1 report on this topic
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Latest stories
Gemma-4-26B-A4B with 39-69% less thinking at same accuracy
research
8 min read
Bottlecap AI released ThinkingCap-gemma-4-26B-A4B-it, a token-efficient version of Google’s Gemma-4-26B-A4B-it reasoning model. Gemma-4-26B-A4B is a sparse mixture-of-experts model with 26 billion parameters and about 4 billion active parameters per token. Bottlecap says its model uses 38.9% fewer thinking tokens on average across 12 benchmarks while accuracy declined by 0.15 percentage points, from 75.18% to 75.03%, under identical vLLM serving and sampling settings. Thinking-token reductions ranged from 13.7% on IFBench to 69.1% on LongBench v2. The model used 54.4% fewer tokens across long-context and multimodal tasks, 44.4% fewer on knowledge and reasoning, 36.1% fewer on math, and 27.6% fewer on code. Accuracy increased on some tests, including IFBench and LongBench v2, but fell 4.00 percentage points on AA-LCR. Bottlecap measured looping in 0.76% of the optimized model’s responses, down from 3.64% for the base model. Bottlecap evaluated both models with thinking enabled, temperature 1.0, top-p 0.95, top-k 64, and a 65,536-token generation cap. The open model and GGUF builds are available under the bottlecapai/ThinkingCap-gemma-4-26B-A4B-it repository. Bottlecap also offers a commercial PRO version, which it says cuts thinking tokens by up to 87.6% and 62% on average.