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Qwen3.8

Qwen's 27B language model, run as ggml-org's Q4_K_M GGUF with llama.cpp. Qwen publishes FP8 and full weights, no GGUF.

Size

Commercial use OK: code MIT, weights Apache-2.0. Measured on our RTX 4090: 43.0 tokens/s, first token in 204 ms, 18.3 GiB VRAM peak at 8K context. We scored it 81.0 on IFEval. GGUF weights, 17.67 GB.

License

Commercial use OK

Code
MITchecked 3 Nov 2026
Weights
Apache-2.0all sizes, checked 3 Nov 2026

Third-party quantization by ggml-org, at the pinned revision.

On our RTX 4090, measured

43.0tokens/s

Q4_K_M, batch 1, 512-token prompt, 256 tokens generated

First token
204 ms
Prompt reading
2514 tokens/s
VRAM peak
18.3 GiB at 8K context
GPU layers
65/65
Environment, 3 Nov 2026

NVIDIA GeForce RTX 4090, driver 580.173.02, llama.cpp v0.7.0 (b11669), CUDA 12.4. Baseline 801 MiB. 2 warm-up + 10 timed requests, median of 3 runs.

Accuracy

81.0IFEvalmeasured

IFEval, 541 items, Q4_K_M, lm-eval==0.4.13 · 3 Nov 2026

96.7GSM8Kmeasured

GSM8K, 8-shot chain of thought, 1,319 items, Q4_K_M, lm-eval==0.4.13 · 3 Nov 2026

Weights

GGUF

Size
17.67 GB
sha256
c600de0300ae…91a747
Download 27b-q4_k_m weights

Answers to the shared prompts

Same prompts for every model of this task, answered greedily with thinking off. Failures are marked by hand.

14 of 14 prompts answered, 2 marked as failures.

  1. Instruction following

    FAIL · Gradient checkpointing saves memory only in training, so the third way does nothing for inference
    The prompt

    List three practical ways to reduce GPU memory use when running a neural network for inference. Answer with exactly three bullet points, each under 15 words, and nothing else.

    A correct answer: Exactly three bullet points, each under 15 words, with no text before or after them. Each way is real, such as lower precision, a smaller batch or a shorter input.

    *   Use mixed precision inference with FP16 or BF16 formats.
    *   Apply quantization to reduce model weights to 8-bit integers.
    *   Enable gradient checkpointing to trade compute for memory savings.
  2. Summarizing

    FAIL · Doesn't say the conditions are written down before observing, and says the reference stars account for viewing quality, which the text doesn't
    The prompt

    Summarize the paragraph below in exactly two sentences. A small observatory on a hill keeps a logbook for every night it opens. Before anyone looks through the telescope, they write down the cloud cover, the temperature and how steady the air is, because a blurry night and a sharp night can make the same star look very different. The staff also measure the same three reference stars at the start and end of each session. If those reference readings drift, every other number from that night is marked as uncertain rather than thrown away, so that later readers can decide for themselves how much to trust it.

    A correct answer: Exactly two sentences. It mentions writing down the conditions before observing, and using reference stars to mark a night's numbers as uncertain. It adds nothing that the paragraph doesn't say.

    The observatory records atmospheric conditions and measures reference stars at the beginning and end of each session to account for varying viewing quality. If the reference readings drift, the data is flagged as uncertain rather than discarded, allowing future researchers to evaluate its reliability.
  3. Extracting data

    The prompt

    Extract these fields from the note below and answer with a JSON object only, without code fences: customer (string), date (YYYY-MM-DD), total_eur (number), items (array of strings). Note: Order for Mara Lindqvist, placed on 3 March 2026. She bought a desk lamp for 34.90 euros, two notebooks at 4.50 euros each, and a USB cable for 9.99 euros.

    A correct answer: Valid JSON with exactly these keys and nothing around it: customer "Mara Lindqvist", date "2026-03-03", total_eur 53.89, and items naming the lamp, the notebooks and the cable.

    {
      "customer": "Mara Lindqvist",
      "date": "2026-03-03",
      "total_eur": 53.89,
      "items": [
        "desk lamp",
        "notebooks",
        "USB cable"
      ]
    }
  4. Arithmetic

    The prompt

    A train leaves at 09:40 and travels 210 km at an average speed of 84 km/h. At what time does it arrive? Explain in at most three short steps, then give the final answer on its own line as "Answer: HH:MM".

    A correct answer: The last line is "Answer: 12:10". The trip takes 210 / 84 = 2.5 hours.

    1. Calculate the travel time by dividing the distance by the speed: $210 \text{ km} / 84 \text{ km/h} = 2.5 \text{ hours}$, which is 2 hours and 30 minutes.
    2. Add the travel time to the departure time: 09:40 + 2 hours = 11:40.
    3. Add the remaining 30 minutes: 11:40 + 30 minutes = 12:10.
    
    Answer: 12:10
  5. Logic

    The prompt

    Ana, Ben and Chloe each own exactly one pet: a cat, a dog or a parrot, and no two of them own the same pet. Ana does not own the dog. Chloe does not own the cat. Ben owns neither the cat nor the parrot. Who owns which pet? Answer in the form "Ana: ..., Ben: ..., Chloe: ...".

    A correct answer: Ana: cat, Ben: dog, Chloe: parrot.

    To determine who owns which pet, we can analyze the constraints step by step.
    
    **The People:** Ana, Ben, Chloe
    **The Pets:** Cat, Dog, Parrot
    
    **Constraints:**
    1. Ana does not own the dog.
    2. Chloe does not own the cat.
    3. Ben owns neither the cat nor the parrot.
    
    **Step 1: Determine Ben's pet.**
    From constraint 3, Ben does not own the cat and does not own the parrot. Since there are only three pets (cat, dog, parrot), Ben must own the remaining one.
    *   **Ben owns the Dog.**
    
    **Step 2: Determine Ana's pet.**
    From constraint 1, Ana does not own the dog. Since Ben already owns the dog, this constraint is consistent but doesn't immediately solve it. However, we know the remaining pets are the Cat and the Parrot.
    Let's look at Chloe first to see if it helps.
    
    **Step 3: Determine Chloe's pet.**
    From constraint 2, Chloe does not own the cat.
    The remaining pets for Ana and Chloe are the Cat and the Parrot (since Ben has the Dog).
    Since Chloe cannot own the Cat, she must own the Parrot.
    *   **Chloe owns the Parrot.**
    
    **Step 4: Determine Ana's pet.**
    The only pet left is the Cat.
    *   **Ana owns the Cat.**
    
    **Verification:**
    *   Ana owns the Cat. (Does not own the dog? Yes.)
    *   Ben owns the Dog. (Owns neither cat nor parrot? Yes.)
    *   Chloe owns the Parrot. (Does not own the cat? Yes.)
    *   All pets are unique? Yes (Cat, Dog, Parrot).
    
    **Final Answer:**
    Ana: Cat, Ben: Dog, Chloe: Parrot
  6. Writing code

    The prompt

    Write a Python function is_palindrome(s: str) -> bool that ignores case, spaces and punctuation. After the function, show two example calls with their expected results as comments.

    A correct answer: A correct function: it keeps only letters and digits, lowercased, and compares them with their reverse. The examples are right, such as "A man, a plan, a canal: Panama" giving True.

    ```python
    def is_palindrome(s: str) -> bool:
        """
        Check if a string is a palindrome, ignoring case, spaces, and punctuation.
        
        Args:
            s: The input string to check.
            
        Returns:
            True if the string is a palindrome, False otherwise.
        """
        # Filter out non-alphanumeric characters and convert to lowercase
        cleaned = ''.join(char.lower() for char in s if char.isalnum())
        # Check if the cleaned string is a palindrome
        return cleaned == cleaned[::-1]
    
    # Example 1: "A man, a plan, a canal: Panama" is a palindrome
    # is_palindrome("A man, a plan, a canal: Panama") -> True
    
    # Example 2: "race a car" is not a palindrome
    # is_palindrome("race a car") -> False
    ```
  7. Fixing code

    The prompt

    This Python function should return the average of a list of numbers, but it gives wrong results. Explain the bug in one sentence, then give the corrected function. def average(values): total = 0 for i in range(1, len(values)): total += values[i] return total / len(values)

    A correct answer: It says the loop skips the first number, because the range starts at 1. The fix sums every number, for example sum(values) / len(values). Handling an empty list is a bonus, not required.

    The bug is that the loop starts at index 1 instead of 0, causing the first element of the list to be excluded from the sum.
    
    ```python
    def average(values):
        total = 0
        for i in range(len(values)):
            total += values[i]
        return total / len(values)
    ```
  8. Answering from a text

    The prompt

    Answer two questions using only the text below. If the text doesn't say, answer "Not stated." 1. What happens to a night's numbers when the reference readings drift? 2. In what year did the observatory open? Text: A small observatory on a hill keeps a logbook for every night it opens. Before anyone looks through the telescope, they write down the cloud cover, the temperature and how steady the air is, because a blurry night and a sharp night can make the same star look very different. The staff also measure the same three reference stars at the start and end of each session. If those reference readings drift, every other number from that night is marked as uncertain rather than thrown away, so that later readers can decide for themselves how much to trust it.

    A correct answer: Question 1: they are marked as uncertain, not thrown away. Question 2: "Not stated." Any year it gives is invented, and a failure.

    1. They are marked as uncertain rather than thrown away.
    2. Not stated.
  9. Translating

    The prompt

    Translate into Korean, polite and natural, as you would write to a colleague: "Thanks for sending the measurements. Could you run the larger model again tomorrow morning, when the machine is idle?"

    A correct answer: Natural, polite Korean that keeps every part: thanks for the measurements, run the larger model again, tomorrow morning, when the machine is idle.

    측정값을 보내주셔서 감사합니다. 내일 오전에 머신이 유휴 상태일 때, 더 큰 모델을 다시 실행해 주실 수 있을까요?
  10. Rewriting

    The prompt

    Rewrite this message to be polite and clear, in under 60 words, keeping every fact: "your model page is wrong. the license says apache but the weights are non commercial. fix it"

    A correct answer: Under 60 words and polite. It still says the page lists Apache while the weights are non-commercial, and asks for a correction.

    Please review your model page. The license is listed as Apache, but the weights are restricted to non-commercial use. This discrepancy needs correction to ensure accuracy and compliance. Thank you for your prompt attention to this matter.
  11. Classifying

    The prompt

    Label each review as positive, negative or mixed. Answer with a numbered list of labels only. 1. Setup took five minutes and it has worked perfectly ever since. 2. The battery died after two days and support never replied. 3. Great screen, but the speakers are tinny and quiet. 4. Honestly the best purchase I have made this year.

    A correct answer: 1 positive, 2 negative, 3 mixed, 4 positive, and nothing else.

    1. positive
    2. negative
    3. mixed
    4. positive
  12. Explaining

    The prompt

    Explain to a high-school student what VRAM is and why it limits which AI models can run on a graphics card. Use at most 80 words.

    A correct answer: At most 80 words, and correct: VRAM is the graphics card's own memory, and a model's weights and working data must fit in it for the card to run the model.

    VRAM is your graphics card’s dedicated memory, acting like a workspace for data. AI models are massive files that must fit entirely into this space to run. If a model is too big for your VRAM, it simply won’t load or will crash. This is why high-end cards with more VRAM can handle larger, smarter AI models, while smaller cards are limited to lighter, simpler versions. Think of it as trying to fit a giant book onto a tiny desk.
  13. Command line

    The prompt

    Give one Linux shell command that lists the five largest files under the current directory, searching subdirectories too, with human-readable sizes. Answer with the command only.

    A correct answer: One command that works, such as find . -type f -exec du -h {} + | sort -rh | head -n 5. A command that lists directories instead of files is a failure.

    find . -type f -exec du -h {} + | sort -rh | head -n 5
  14. Knowledge

    The prompt

    In one sentence: what is the boiling point of water at sea level in degrees Fahrenheit, and who proposed the Fahrenheit scale?

    A correct answer: 212 °F, and Daniel Gabriel Fahrenheit.

    The boiling point of water at sea level is 212 degrees Fahrenheit, a scale proposed by Daniel Gabriel Fahrenheit.

Use it when

You want a large, commercially usable Qwen model that fits on one 24 GB card.

Avoid it when

You have a small card: the file alone is about 19 GB, and the smaller Qwen3.5 scored as well on instruction following and only a little lower on GSM8K math. This GGUF is ggml-org's quantization, not Qwen's own.

Sizes

SizeDecodeVRAMAccuracyWeightsRunsLicense
27b-q4_k_m43.0 tok/s18.3 GiB81.0 IFEval, measured17.67 GBAs isCommercial OK

From the authors

In their words, with their numbers. Ours are in the cards at the top of this page.

README

Copied from the authors' repository at commit 2ea10dc on 3 Nov 2026. Numbers and license claims in it are theirs, not measured or checked by us. We removed badges, replaced images with links and adjusted headings.

💜 Qwen Studio | 🤗 Hugging Face | 🤖 ModelScope | 💬 WeChat (微信) | 🫨 Discord

Welcome to the GitHub repository of the Qwen3.5 open model series, including Qwen3.5, Qwen3.6, and the latest Qwen3.8. Here, you can find official information about Qwen3.8, post your questions (Issues), and share your ideas with the community (Discussions).

Introduction

Qwen3.8

For the first time, Qwen3.8 brings a Qwen-Max-class model to open release. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Beyond answering harder questions, Qwen3.8 is designed to carry complex, multi-step tasks through to completion with greater reliability.

Qwen3.8 features the following enhancements:

  • Core Capabilities: Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks.
  • Agent Execution: Stronger autonomous planning and better handling of environment feedback, leading to more reliable end-to-end task completion.
  • Downstream Compatibility: Broader support for popular harnesses and development tools, making it easier to integrate into your existing stack.
  • Flexible Thinking Control: Reasoning depth can be tuned with reasoning_effort, and reasoning context from historical messages is retained via preserve_thinking.
Qwen3.6

Building upon the fundamental breakthroughs of Qwen3.5, Qwen3.6 prioritizes stability and real-world utility. It offers developers a more intuitive, responsive, and genuinely productive coding experience, shaped by direct community feedback. This update delivers substantial upgrades, particularly in:

  • Agentic Coding: The model now handles front-end workflows and repository-level reasoning with greater fluency and precision.
  • Thinking Preservation: A new feature retains thinking context across conversation history, streamlining iterative development and reducing overhead.
Qwen3.5

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 features the following enhancements:

  • Unified Vision-Language Foundation: Early fusion training on trillions of multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
  • Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
  • Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
  • Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
  • Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

News

  • 2026-08-14: Qwen3.8-27B is now available on Hugging Face Hub and ModelScope. Read more on the model card.
  • 2026-08-12: Qwen3.8-2.4T-A95B is now available on Hugging Face Hub and ModelScope. Read more on the model card.
  • 2026-04-22: Qwen3.6-27B is now available on Hugging Face Hub and ModelScope. Read more on our release blog!
  • 2026-04-16: Qwen3.6-35B-A3B is now available on Hugging Face Hub and ModelScope. Read more on our release blog!
  • 2026-03-02: Qwen3.5-9B, Qwen3.5-4B, Qwen3.5-2B, and Qwen3.5-0.8B are now available on Hugging Face Hub and ModelScope!
  • 2026-02-24: Qwen3.5-122B-A10B, Qwen3.5-35B-A3B, and Qwen3.5-27B are released. Check out the model cards on Hugging Face Hub or ModelScope for more information!
  • 2026-02-16: We release Qwen3.5. The first release includes a 397B-A17B MoE model. Read more on our release blog. More sizes are coming & Happy Chinese New Year!
  • 2025-09-11: We release Qwen3-Next-80B-A3B, an ultra-sparse mixture-of-experts model with hybrid attention architecture, designed for extreme efficiency. Read more on our blog.

Models

The official model weights are released on:

  • 🤗Hugging Face Hub: Most LLM frameworks and applications support downloading model files from Hugging Face Hub automatically by specifying the model ID, e.g., Qwen/Qwen3.8-27B, Qwen/Qwen3.6-35B-A3B, and Qwen/Qwen3.5-397B-A17B. You can also download model files manually using huggingface download or git clone. Please follow the instructions on the model page.
  • 🤖ModelScope: For users unable to access Hugging Face Hub, we strongly recommend using ModelScope. For supported frameworks, you can download from ModelScope by setting environment variables, such as SGLANG_USE_MODELSCOPE=true or VLLM_USE_MODELSCOPE=true. You can also download model files manually using modelscope download or git clone. Please follow the instructions on the model page.

Benchmarks

Qwen3.8 Open Models

For detailed results, please check out the Qwen3.8-2.4T-A95B Model Card and the Qwen3.8-27B Model Card.

Qwen3.6 Open Models

Figure Qwen3.6-27B Benchmark Results

Figure Qwen3.6-35B-A3B Benchmark Results

For detailed results, please check out the Qwen3.6-35B-A3B blog and the Qwen3.6-27B blog.

Qwen3.5 Open Models

Figure Qwen3.5-397B-A17B Benchmark Results

Figure Qwen3.5-122B-A10B, Qwen3.5-35B-A3B, and Qwen3.5-27B Benchmark Results

Figure Qwen3.5-9B and Qwen3.5-4B Benchmark Results

For detailed results, please check out the Qwen3.5 blog.

Quickstart

Official

You can try Qwen3.8 on our official sites and enjoy the native experience with extra features, such as deep research, web dev, and adaptive tool use.

Qwen Studio

If you simply want to try Qwen3.8, Qwen Studio is an AI assistant for everyone. It’s free to use, open to all, and ready to help with creativity, collaboration, and endless possibilities.

Qoder

Qwen3.8 is now available directly on Qoder. Qoder is an agentic coding platform designed for real software development. Qoder is available as a standalone application. Follow its documentation to get started!

QwenWork

Qwen3.8 is now available directly on QwenWork. QwenWork is a one-stop AI working platform launched by Alibaba. Follow its documentation to get started!

Qwen API

QwenCloud provides first-class support for Qwen3.8, which is compatible with various API specifications, including OpenAI and Anthropic, making it simple for you to try Qwen3.8 in your own applications.

Qwen Code

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster. Follow its documentation to get started!

Local Use
Hugging Face Transformers

transformers acts as the model-definition framework in the current open-weight LLM landscape. It also includes functionalities for LLM inference and training. The addition of serving capabilities in transformers makes it much easier to integrate new models in your development.

To launch a server, simply use the transformers serve command:

transformers serve Qwen/Qwen3.8-27B --port 8000 --continuous-batching

An OpenAI-compatible API will be available at http://localhost:8000/v1. See the Serve CLI guide for more information.

llama.cpp

llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. llama.cpp supports the Qwen3.5 open model series (text & vision). Look for models ending with GGUF on Hugging Face Hub.

MLX (Apple Silicon)

If you are running on Apple Silicon, both mlx-lm (text-only) and mlx-vlm (vision + text) support the Qwen3.5 open model series. Look for models ending with MLX on Hugging Face Hub.

Unsloth

Unsloth contains a local UI to run and train LLMs and diffusion models, including Qwen3.8 and more. See the Qwen3.8 guide for running Qwen3.8 quants with Unsloth.

Deployment

The Qwen3.5 open model series is supported by multiple inference frameworks. Here we demonstrate the usage of SGLang, vLLM, and TokenSpeed.

SGLang

SGLang is a fast serving framework for large language models and vision language models. SGLang can be used to launch a server with an OpenAI-compatible API service.

sglang serve --model-path Qwen/Qwen3.8-27B --port 8000 --tp-size 4 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

An OpenAI-compatible API will be available at http://localhost:8000/v1.

Also see SGLang Cookbook on serving Qwen3.8.

vLLM

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM can be used to launch a server with an OpenAI-compatible API service.

vllm serve Qwen/Qwen3.8-27B --port 8000 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

An OpenAI-compatible API will be available at http://localhost:8000/v1.

Also see vLLM Recipes on serving Qwen3.8.

TokenSpeed

TokenSpeed is a speed-of-light LLM inference engine. TokenSpeed can be used to launch a server with an OpenAI-compatible API service.

tokenspeed serve Qwen/Qwen3.8-27B --port 8000 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

An OpenAI-compatible API will be available at http://localhost:8000/v1.

Also see TokenSpeed Recipes on serving Qwen3.8.

Finetuning

We advise you to use training frameworks, including Unsloth, Swift, Llama-Factory, to finetune your models with SFT, DPO, GRPO, etc.

License Agreement

Please find the license file released with the model weights on Hugging Face Hub or ModelScope.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3.8,
    title  = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},
    author = {{Qwen Team}},
    year   = {2026},
    month  = {August},
    url    = {https://qwen.ai/blog?id=qwen3.8}
}

@misc{qwen3.6-27b,
    title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
    author = {{Qwen Team}},
    year   = {2026},
    month  = {April},
    url    = {https://qwen.ai/blog?id=qwen3.6-27b}
}

@misc{qwen3.6-35b-a3b,
    title  = {{Qwen3.6-35B-A3B}: Agentic Coding Power, Now Open to All},
    author = {{Qwen Team}},
    year   = {2026},
    month  = {April},
    url    = {https://qwen.ai/blog?id=qwen3.6-35b-a3b}
}

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    year   = {2026},
    month  = {February},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

Contact Us

If you are interested in leaving a message to either our research team or product team, join our Discord or WeChat groups!

Alternatives

Model output
Input test photo
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InputOutput