Sparset/Thinklite-27B

[!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"

Mean reasoning tokens per answer

reasoning_effort="low"

Mean reasoning tokens per answer

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.