Same model size. Same GPU. ~4.7x more work done! ThinkingCap: Qwen 3.8 reaches answers with about half the reasoning. On a shared server, that means 2x more users fit in memory at once, and each finishes 2x faster. Running it solo? ~2x faster answers. Serving a team? ~4.7x throughput. Hugging Face: https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B…

bottlecapai/ThinkingCap-Qwen3.6-27B · Hugging Face

Source: https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#thinkingcap-qwen-36-27bThinkingCap: Qwen 3.6 27B

Capability of Qwen3.6-27B with50% lessthinking tokens on average, and over90% lessin best cases. Achieved via finetuningQwen3.6-27B (Qwen Team, 2026)with state-of-the-art algorithms on a curated set of problems of various domains and difficulty. We designed the finetuning to be as minimally invasive as possible, preserving all of the original answer quality and style of Qwen, while being more token efficient. Checkthe blogpostfor more details.

We rigorously evaluate the resulting checkpoint across general reasoning, non-reasoning multiple-choice question answering, everyday multi-turn conversations, system prompt adherence, safety, math, code and agentic use cases. Due to the high variability of reasoning quality at Qwen-recommended sampling temperature 1.0, we run each benchmark with multiple seeds and do statistical significance testing on all the results. We evaluate both in domain (holdout parts of selected datasets included in training) and out of domain.

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#out-of-domain-token-efficiencyOut-of-domain token efficiency

BenchmarkAccuracyThinking tokensBaseOursBaseOursReductionKnowledge & reasoningGPQA-Diamond85.5±1.483.8±1.910,7773,351↓ 67.8%SuperGPQA64.0±0.264.0±0.18,2463,384↓ 58.4%MMLU-Pro85.9±0.285.4±0.23,4551,290↓ 53.7%MMLU-Redux93.9±0.193.9±0.1947406↓ 44.8%C-Eval90.6±0.790.3±0.61,279663↓ 47.1%Math & codeHMMT (Nov 2025)88.0±3.784.7±3.739,27727,388↓ 38.0%LiveCodeBench80.7±0.684.3±1.015,74410,158↓ 41.1%Long-context & multimodalLongBench v262.6±3.660.2±1.71,7651,091↓ 39.1%RealWorldQA82.4±0.781.9±1.22,959913↓ 48.5%AA-LCR76.2±3.074.2±2.22,4551,337↓ 45.5%Instruction following & agenticSystem-prompt adherence80.6±1.281.5±1.81,737976↓ 40.0%Claw-Evalthink/task87.0±1.984.4±1.2919689↓ 25.2%Macro average81.580.7——↓ 45.8%Claw-Eval thinking tokens are per-task (agentic; not a single-turn trace).

Settings

• **Models:**baseQwen/Qwen3\.6\-27Bvsbottlecapai/ThinkingCap\-Qwen3\.6\-27B(shown asOursin the table).
• **Seeds:**5 per condition; thinking on; cells are mean ± 95% CI across seeds.
• **Decoding:**thinking on; samplingtemperature=1\.0, top\_p=0\.95, top\_k=20, min\_p=0\.0(bottlecapai/ThinkingCap\-Qwen3\.6\-27Buses the base’s sampling).
• **Max generation tokens:**100,000 for the general suite (gpqa_diamond, mmlu_pro, longbench_v2, realworldqa) and AA-LCR; 250,000 for HMMT (Nov 2025); 32,768 for supergpqa and livecodebench; 16,384 for ceval and mmlu_redux; 15,000 for llm-system-prompts-benchmark; 49,152 for Claw-Eval.
• Metrics— the columns mirror the table: - Accuracy(Base / Ours) — fraction correct (exact/regex match; soft compliance for llm-system-prompts-benchmark; judge task-score for Claw-Eval; judge CORRECT/INCORRECT for AA-LCR). - Thinking tokens(Base / Ours) — mean length of the single-turn<think\>trace (think-per-task for Claw-Eval). - Reduction— the average per-question thinking-token saving: base andOursare paired on the same question (each side seed-averaged), each question’s\(base − cap\)/baseis taken, then averaged over shared questions (a larger ↓ = a bigger saving). - Macro average(bottom row) — equal-weight mean across benchmarks. We separately track two trace-quality failure modes, reported only in aggregate:looping— the model gets stuck repeating the same reasoning chain (sometimes a single sentence), never finishing its thinking; detected from the fraction of repetitive n-grams — andtruncation— the<think\>trace never closes because the model hits the generation-token cap while still reasoning, so no answer is produced. Across all out-of-domain responses, truncation drops from2.9% to 0.4%while looping stays negligible (~0.2%).

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#in-domain-evalsIn-domain evals

Holdouttestsplits of datasets whose train splits are part of the finetuning mix — quality retention on in-distribution tasks (in contrast to the out-of-domain benchmarks above).

BenchmarkAccuracyThinking tokensBaseOursBaseOursReductionGSM8K93.3±1.596.5±0.33,175648↓ 74.1%ARC-Challenge97.0±0.397.6±0.4966335↓ 51.5%ARC-Easy99.3±0.299.4±0.2566260↓ 44.5%CommonsenseQA86.7±0.788.2±0.91,118273↓ 64.1%OpenBookQA96.0±0.596.7±0.6858248↓ 59.5%QASC91.7±0.792.2±0.51,258348↓ 61.9%SciQ97.0±0.297.5±0.2766276↓ 48.3%Macro average94.495.4——↓ 57.7%Settings

• **Seeds:**5 per condition; thinking on; cells are mean ± 95% CI across seeds.
• **Decoding:**samplingtemperature=1\.0, top\_p=0\.95, top\_k=20, min\_p=0\.0(bottlecapai/ThinkingCap\-Qwen3\.6\-27Buses the base’s sampling).
• **Max generation tokens:**15,000 for GSM8K; 8,192 for the MCQ sets.
• **Data:**GSM8K is the full 1,319-row test split; the MCQ sets are capped at 1,000 rows (OpenBookQA = 500 and QASC = 926 are smaller, so full).
• Metrics:**Accuracy— exact-match on the final answer (GSM8K) / last-letter multiple-choice match (MCQ).Thinking tokens,ReductionandMacro averageare as defined for the token-efficiency table above, as are theloopingandtruncationfailure modes: across all in-domain responses, truncation drops from1.6% to 0.03%while looping is negligible for both (≤0.01%**).

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#guardrails-preservationGuardrails preservation

Brevity finetuning leaves safety behaviour intact: on both safety setsbottlecapai/ThinkingCap\-Qwen3\.6\-27Brefuses harmful/jailbreak prompts at the base model’s rate (statistically indistinguishable) while still spending fewer thinking tokens.

BenchmarkSAFE %Thinking tokensBaseOursBaseOursReductionNemotron-Safety98.9±0.899.0±0.41,242933↓ 23.8%HEx-PHI99.9±0.2100.0±0.0693543↓ 20.0%Macro average99.499.5——↓ 21.9%Settings

• **Seeds:**5 per condition; thinking on; cells are mean ± 95% CI across seeds.
• Benchmarks:Nemotron\-Safety— a 300-prompt sample of thesafetysplit ofnvidia/Llama\-Nemotron\-Post\-Training\-Dataset;HEx\-PHI— the 300 HEx-PHI harmful instructions (jkazdan/guardrail\-llama\-3\-8b\-refusal\-hexphi). Both are safety-relevant / jailbreak prompts. Both datasets only contain a train subset and given their near-perfect scores, it is hard to rule out possible contimination in Qwen’s original training data. These results are intended purely as a demonstration of preserved guardrail capabilities, and are in line with our internal usage testing.
• Metrics:****SAFE %— fraction of replies an LLM safety judge marks SAFE (upholds guardrails — refuses or safely deflects the harmful request); higher is better.Thinking tokensandReductionare as defined for the token-efficiency table above.

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#usageUsage

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#hosted-api-requestyHosted API (Requesty)

Don’t want to run the weights yourself? ThinkingCap-Qwen3.6-27B is hosted and served over an API onRequesty— call it directly, no local GPU needed.

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#huggingface-transformersHuggingFace Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("bottlecapai/ThinkingCap-Qwen3.6-27B", dtype="bfloat16")
proc = AutoProcessor.from_pretrained("bottlecapai/ThinkingCap-Qwen3.6-27B")

Checkhttps://huggingface.co/Qwen/Qwen3.6-27Bfor recommended usage, sampling params etc.

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#vllm–sglangvLLM / SGLang

Serve the bf16 model with either engine — plain, or with the model’s own MTP (multi-token-prediction / NextN) head for self-speculative decoding (no separate draft model needed):

# vLLM — standard
vllm serve bottlecapai/ThinkingCap-Qwen3.6-27B
# vLLM — with MTP self-speculative decoding (vLLM ≥ 0.24.0)
vllm serve bottlecapai/ThinkingCap-Qwen3.6-27B --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

# SGLang — standard
python -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.6-27B --trust-remote-code
# SGLang — with MTP self-speculative decoding
python -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.6-27B --trust-remote-code \
  --speculative-algorithm EAGLE --speculative-num-steps 3 \
  --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

MTP speculative decoding is lossless — the output is identical to standard decoding — and on the bf16 weights it accepts ≈3.34 drafts per verify step, a further ≈2.6× decode throughput on top of the finetune’s token savings.

For GPU serving at half the memory, an official FP8 build lives atbottlecapai/ThinkingCap-Qwen3.6-27B-FP8— near-lossless, loaded natively by vLLM (vllm serve bottlecapai/ThinkingCap\-Qwen3\.6\-27B\-FP8), with the MTP head kept in bf16; full serving benchmarks are on that card.

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#gguf-llamacppGGUF (llama.cpp)

QuantizedGGUFbuilds of this model live in the sibling repobottlecapai/ThinkingCap-Qwen3.6-27B-GGUF, for local inference withllama.cppand compatible runtimes (Ollama, LM Studio, …).

Quantization stores the weights at reduced precision — e.g. ~4.7 bits per weight forQ4\_K\_Minstead of 16-bit bf16 — cutting download size and memory severalfold at a small quality cost.Q4\_K\_Mis the recommended size/quality balance,Q8\_0is near-lossless.

llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M -p "Hi"

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#where-to-find-usWhere to find us

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#citationCitation

If you use this model, please cite:

@misc{ThinkingCap-Qwen3.6-27B,
  title     = {bottlecapai/ThinkingCap-Qwen3.6-27B},
  author    = {Lasocki, Karol and Osusky, Adam and Lindauer, Jan and Jirkovsky, Adam and Mihal, Filip and Platek, Ondrej and Herel, David and Ihnatchenko, Luka and Bartek, Vojtech and Jirak, Jiri and Mikolov, Tomas},
  year      = {2026},
}

https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B#acknowledgementsAcknowledgements

We acknowledge EuroHPC Joint Undertaking for awarding the project ID EHPC-AIF-2025SC03-029 access to Leonardo at CINECA, Italy.