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**We recommend `ThinkingCap-gemma-4-26B-A4B-it-MXFP4_MOE.gguf` over `Q4_K_M` at this size**: it is the file evaluated below, and it held up better on our math benchmarks. Pull it with `-hf bottlecapai/ThinkingCap-gemma-4-26B-A4B-it-GGUF:MXFP4_MOE`. GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-gemma-4-26B-A4B-it — ThinkingCap's shorter-thinking post-training applied to gemma-4-26B-A4B-it, a mixture-of-experts model with image input. ➡️ Full model description, evaluation results, the measured thinking-token reduction and recommended sampling: see the main model card at bottlecapai/ThinkingCap-gemma-4-26B-A4B-it.

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GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B — the ThinkingCap finetune of Qwen3.8-27B that cuts reasoning tokens by 37% on average while holding 85.8% average accuracy against the base model's 86.6%. ➡️ Full model description, evaluation results (multi-seed, statistically tested), the measured thinking-token reduction, recommended sampling params, and citation: see the main model card at bottlecapai/ThinkingCap-Qwen3.8-27B. The low-bit files are built with an **importance matrix** (activation statistics from a chat-templated calibration corpus) and a **per-tensor precision layout**: the attention projections of the full-attention layers and the linear-attention output projections stay at 6–8 bit while the feed-forward weights take the 4-bit hit.

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A **14.3 GB** GGUF of bottlecapai/ThinkingCap-Qwen3.8-27B that runs on an **18 GB GPU with image input, MTP speculative decoding and a 32k-token context all on the card**, and matched the bf16 weights on all five benchmarks we evaluated (2,863 questions: 81.00% against 81.03%). The standard GGUFs (ThinkingCap-Qwen3.8-27B-GGUF) start at IQ4_XS, 15.5 GB, which with the vision projector and a 32k context does not fit 18 GB. Every weight tensor was quantized with llama.cpp's own types (IQ2_S up to Q8_0, all with one importance matrix computed on the model's own reasoning traces). Which type each of the 401 language-model weight matrices gets was then picked by an RCO search: it minimizes the KL divergence between the bf16 model's and the mixed model's next-token distributions on 256 of the model's own traces, under an exact file-size budget. The same idea as ISTA-DASLab's GSQ-RCO GGUFs, searched on this finetune rather than the base model; the rounding itself is llama.cpp's.

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好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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