Thinklite-27B

license: apache-2.0 base_model: Qwen/Qwen3.8-27B pipeline_tag: image-text-to-text library_name: transformers tags:

thinklite
reasoning
efficient-reasoning
qwen3.8

[!NOTE] Qwen3.8-27B that thinks with half the tokens and keeps 99.6% of its accuracy. At reasoning_effort="xhigh", Thinklite-27B uses 47% less reasoning than Qwen3.8-27B on average over six benchmarks and writes half as many output tokens, so its time-to-final-output (TTFO) is about 2x shorter, while it keeps 99.6% of the base model's average accuracy (89.8 against 90.2). At reasoning_effort="low" it uses 28% less reasoning and keeps 97.5% of the base model's accuracy (2.2 points below).

Check out the official Thinklite-27B-GGUF, a 16.5 GB Q4_K_M for llama.cpp and Ollama that uses 48% less reasoning than Qwen3.8-27B quantized the same way at 99.3% of its accuracy.

Reasoning vs base: the change in mean reasoning tokens per answer against Qwen3.8-27B at the same effort, averaged over the six benchmarks below.
TTFO speed-up: TTFO (time-to-final-output) is how long a user waits until the model's complete final output is there, thinking included. The speed-up is Qwen3.8-27B's TTFO divided by the model's, over all 4,291 requests for one user. All four models share the Qwen3.8-27B architecture and decode at the same speed (Thinklite-27B 26.3 vs base 26.2 tokens/s for a single user on the same GPU), and the prompts are identical, so TTFO falls in proportion to the tokens a model writes.

Side-by-side comparisons

Debugging

Qwen3.8-27B and Thinklite-27B get the same debugging prompt (retry code plus two logs: find the duplicate charges to refund and the fix) with the same seed, each model on one NVIDIA RTX PRO 6000 serving 20 requests at once. Both answer correctly; Thinklite-27B is done in 2:36.9 with 3,381 tokens, the base model in 11:04.9 with 14,313 tokens (4.2x). The video plays at 22x speed and the clocks show the measured time. This is the largest speed-up of 20 paired runs of this prompt. Thinklite-27B was correct in 19 of the 20 runs and the base model in all 20; over the 19 correct runs the median speed-up was 2.7x.

Building a 3D physics lab

Both models get the same objective: build an interactive 3D science lab as a single HTML file in which everything has to follow realistic physics. Objects have real sizes and masses and fall under real gravity (9.81 m/s²): a hinged door swings with its momentum and stops at its frame, glassware tips over when bumped and shatters when it hits something hard, a rubber ball bounces high while a heavy steel ball barely bounces, a block tower topples and a pendulum swings. Both models run at reasoning_effort="xhigh" on the same NVIDIA RTX PRO 6000. The numbers are the total tokens and generation time each model needed to reach a working version of the lab: Thinklite-27B 87,134 tokens in 56.7 min, the base model 127,661 tokens in 87.6 min (1.5x). The video shows one working game per model; it is not a measure of how often each model succeeds.

How it works

Reasoning models spend much of their output on thinking that does not change the answer: restating the question, repeating a step, re-checking a result that was already checked. Thinklite-27B is fine-tuned from Qwen3.8-27B on its own verified-correct answers to reach the same answers by a shorter route. Only the thinking gets shorter; the visible answer keeps its full length and format. It is a drop-in replacement for Qwen3.8-27B with the same chat template, tokenizer and reasoning_effort control.

Benchmarks

Setup:

We ran all four models ourselves on one frozen suite: identical prompts, chat template, sampling, token caps and scoring. Compared models, both also built on Qwen3.8-27B and run in bf16: bottlecapai/ThinkingCap-Qwen3.8-27B (revision 7891e03) and ukisai/Swift-1.5-Qwen3.8-27b (revision b4c84d42).
vLLM 0.30.0, bf16 weights, FP8 KV cache, temperature 1.0, top_p 0.95, top_k 20, 64 concurrent requests, one NVIDIA RTX PRO 6000 Blackwell (96 GB). All answers were scored together on one machine.
One sample per question (two on AIME 2025). Answers that hit their cap were rerun once with a 131,072-token cap; truncated or unparseable answers count as incorrect.
GSM8K: 1,319 problems
AIME 2025: 30 problems x 2 samples, avg@2
MMLU-Pro: 1,000-question stratified subset
IFEval: 541 prompts, prompt-level strict
LiveCodeBench v6 (LCB): 131 problems (2025-02 to 2025-04), pass@1
BFCL v4: 1,240 non-live function-calling cases

`reasoning_effort="xhigh"`

Benchmark	Thinklite-27B	ThinkingCap-Qwen3.8-27B	Swift-1.5-Qwen3.8-27b	Qwen3.8-27B
GSM8K	163 (-63%)	141 (-68%)	267 (-40%)	443
AIME 2025	11,236 (-43%)	13,228 (-33%)	12,961 (-34%)	19,642
MMLU-Pro	1,343 (-69%)	1,785 (-58%)	2,404 (-44%)	4,295
IFEval	1,082 (-41%)	891 (-51%)	1,031 (-44%)	1,830
LiveCodeBench v6	17,079 (-38%)	21,936 (-20%)	18,770 (-32%)	27,589
BFCL v4	143 (-28%)	113 (-44%)	159 (-20%)	200
Average change	-47.0%	-45.8%	-35.6%	-
All output tokens, 4,291 requests	5,822,599 (-50%)	6,724,080 (-42%)	7,319,314 (-37%)	11,533,518

`reasoning_effort="low"`

Benchmark	Thinklite-27B	ThinkingCap-Qwen3.8-27B	Swift-1.5-Qwen3.8-27b	Qwen3.8-27B
GSM8K	144 (-21%)	160 (-12%)	173 (-5%)	183
AIME 2025	6,521 (-46%)	9,324 (-23%)	9,416 (-22%)	12,125
MMLU-Pro	854 (-23%)	908 (-18%)	963 (-13%)	1,103
IFEval	386 (-41%)	537 (-19%)	557 (-15%)	659
LiveCodeBench v6	8,882 (-17%)	9,183 (-15%)	9,341 (-13%)	10,741
BFCL v4	78 (-19%)	85 (-12%)	90 (-7%)	96
Average change	-28.0%	-16.4%	-12.7%	-
All output tokens, 4,291 requests	3,748,880 (-23%)	4,121,634 (-16%)	4,303,835 (-12%)	4,896,249

Image understanding

Thinklite-27B accepts images like Qwen3.8-27B. Its training data was text only and its vision encoder is byte-for-byte the base model's, so we checked that the fine-tuned language model still reads what it sees: 88 questions from six public image datasets, each answerable only from the picture (a chart, a scanned document, an infographic, a math figure, an exam diagram or a photo). Both models got the same image and question at reasoning_effort="low", with the same seeds and sampling as the benchmarks above.

16 questions per dataset (12 for InfographicVQA and MMMU), a seeded random sample of each validation or testmini split. Scores: ChartQA relaxed accuracy (numbers within 5%), DocVQA and InfographicVQA ANLS, multiple-choice or exact accuracy for MathVista, MMMU and A-OKVQA. No answer was cut off or unparseable.
Both models answered the same 83 of 88 questions correctly; the only score difference is partial ANLS credit on one InfographicVQA answer.
18 of the questions (3 per dataset) also ran at reasoning_effort="xhigh": Thinklite-27B answered 17 correctly and Qwen3.8-27B 16, with 52% less reasoning per answer (758 vs 1,565 tokens).
This is a check, not a full benchmark: one question moves a dataset's score by 6 to 8 points. It shows that image understanding is kept, not that it improved.

Usage

vLLM

vllm serve Sparset/Thinklite-27B --reasoning-parser qwen3 --max-model-len 135168 --kv-cache-dtype fp8

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
reply = client.chat.completions.create(
    model="Sparset/Thinklite-27B",
    messages=[{"role": "user", "content": "How many primes are there below 100?"}],
    temperature=1.0, top_p=0.95,
    extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True, "reasoning_effort": "xhigh"}},
)
print(reply.choices[0].message.content)

Set reasoning_effort to "low", "medium" or "xhigh", exactly as with Qwen3.8-27B. The benchmarks used temperature 1.0, top_p 0.95 and top_k 20, as recommended for the base model.

Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("Sparset/Thinklite-27B", dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained("Sparset/Thinklite-27B")

Files

Merged bf16 safetensors with the tokenizer, chat template and processor files of Qwen3.8-27B.

Limitations

One benchmark run per effort; the small benchmarks (AIME 2025 with 60 answers, LiveCodeBench with 131) carry wide confidence intervals.
At xhigh, accuracy is below the base model on IFEval (89.1 vs 91.5) and MMLU-Pro (84.4 vs 86.3).
At low effort, the average accuracy is 2.2 points below the base model, mostly from AIME 2025 (81.7 vs 90.0).
Long single-answer code was less reliable than the base model's: in the game test above, 1 of 5 games fully worked against 3 of 5.
The vision encoder is unchanged from Qwen3.8-27B and training data was text only; image understanding was checked on 88 questions, not a full vision benchmark.
No safety tuning beyond the base model's. Check the model on your own tasks before relying on it.

License

Apache-2.0, the license of the base model Qwen/Qwen3.8-27B.