# ThinkingCap Qwen3.6 27B

URL: https://interfaze.ai/models/bottlecapaithinkingcap-qwen36-27b

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ThinkingCap Qwen3.6 27B by bottlecapai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | ThinkingCap Qwen3.6 27B | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | ThinkingCap Qwen3.6 27B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B)

Capability of Qwen3.6-27B with **50% less** thinking tokens on average, and over **90% less** in best cases. Achieved via finetuning [Qwen3.6-27B (Qwen Team, 2026)](https://huggingface.co/Qwen/Qwen3.6-27B) 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.

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.

## Out-of-domain token efficiency

Claw-Eval thinking tokens are per-task (agentic; not a single-turn trace).

**Settings**

-   **Models:** base `Qwen/Qwen3.6-27B` vs `bottlecapai/ThinkingCap-Qwen3.6-27B` (shown as `Ours` in the table).
    
-   **Seeds:** 5 per condition; thinking on; cells are mean ± 95% CI across seeds.
    
-   **Decoding:** thinking on; sampling `temperature=1.0, top_p=0.95, top_k=20, min_p=0.0` (`bottlecapai/ThinkingCap-Qwen3.6-27B` uses 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 and `Ours` are paired on the same question (each side seed-averaged), each question's `(base − cap)/base` is 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 — and **truncation** — 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 from **2.9% to 0.4%** while looping stays negligible (**~0.2%**).
    

## In-domain evals

Holdout **test** splits 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).

**Settings**

-   **Seeds:** 5 per condition; thinking on; cells are mean ± 95% CI across seeds.
-   **Decoding:** sampling `temperature=1.0, top_p=0.95, top_k=20, min_p=0.0` (`bottlecapai/ThinkingCap-Qwen3.6-27B` uses 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**, **Reduction** and **Macro average** are as defined for the token-efficiency table above, as are the **looping** and **truncation** failure modes: across all in-domain responses, truncation drops from **1.6% to 0.03%** while looping is negligible for both (**≤0.01%**).

## Guardrails preservation

Brevity finetuning leaves safety behaviour intact: on both safety sets `bottlecapai/ThinkingCap-Qwen3.6-27B` refuses harmful/jailbreak prompts at the base model's rate (statistically indistinguishable) while still spending fewer thinking tokens.

**Settings**

-   **Seeds:** 5 per condition; thinking on; cells are mean ± 95% CI across seeds.
    
-   **Benchmarks:** `Nemotron-Safety` — a 300-prompt sample of the `safety` split of `nvidia/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 tokens** and **Reduction** are as defined for the token-efficiency table above.
    

## Usage

### HuggingFace 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")
```

Check [https://huggingface.co/Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) for recommended usage, sampling params etc.

### GGUF (llama.cpp)

Quantized [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) builds of this model live in the sibling repo [bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF), for local inference with [llama.cpp](https://github.com/ggml-org/llama.cpp) and compatible runtimes (Ollama, LM Studio, …).

Quantization stores the weights at reduced precision — e.g. ~4.7 bits per weight for `Q4_K_M` instead of 16-bit bf16 — cutting download size and memory severalfold at a small quality cost. `Q4_K_M` is the recommended size/quality balance, `Q8_0` is near-lossless; each quant's divergence from the full-precision model is measured (mean KLD) in the GGUF repo's card.

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

## Where to find us

## Citation

If you use this model, please cite:

```
@misc{,
  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},
}
```

## Want more deterministic results?

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