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Qwen3

Qwen's open dense language models, run as Qwen's own GGUF files with llama.cpp.

Size

Commercial use OK for size 0.6b-q8_0: code MIT, weights Apache-2.0. Measured on our RTX 4090: 533 tokens/s, first token in 10.8 ms, 1.9 GiB VRAM peak at 8K context. We scored it 57.3 on IFEval. GGUF weights, 610 MB.

License

Commercial use OK

Code
MITchecked 26 Oct 2026
Weights
Apache-2.0all sizes, checked 26 Oct 2026

On our RTX 4090, measured

533tokens/s

Q8_0, batch 1, 512-token prompt, 256 tokens generated

First token
10.8 ms
Prompt reading
50354 tokens/s
VRAM peak
1.9 GiB at 8K context
GPU layers
29/29
Environment, 26 Oct 2026

NVIDIA GeForce RTX 4090, driver 580.173.02, llama.cpp v0.7.0 (b11669), CUDA 12.4. Baseline 809 MiB. 2 warm-up + 10 timed requests, median of 3 runs.

Accuracy

57.3IFEvalmeasured

IFEval, 541 items, Q8_0, lm-eval==0.4.13 · 26 Oct 2026

Paper 54.5 IFEval strict promptQwen3 technical report, table 20 (non-thinking mode, sampled)

43.3GSM8Kmeasured

GSM8K, 8-shot chain of thought, 1,319 items, Q8_0, lm-eval==0.4.13 · 3 Nov 2026

Weights

GGUF

Size
610 MB
sha256
9465e63a22ad…3bb031
Download 0.6b-q8_0 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, 7 marked as failures.

  1. Instruction following

    FAIL · Vague: none is a concrete way such as lower precision or a smaller batch
    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 efficient data loading and compression.  
    - Optimize model architecture for sparse inputs.  
    - Monitor and adjust GPU memory usage dynamically.
  2. Summarizing

    FAIL · Leaves out that drifting nights are marked as uncertain
    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.

    A small observatory on a hill keeps a logbook for each night it opens, recording cloud cover, temperature, and air stability before the telescope. The staff measures the same three reference stars at the start and end of each session, noting any changes in their readings to determine the reliability of the data.
  3. Extracting data

    FAIL · Wrong date format and total, wrapped in a code fence
    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.

    ```json
    {
      "customer": "Mara Lindqvist",
      "date": "3 March 2026",
      "total_eur": 34.90,
      "items": ["desk lamp", "two 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.

    - The train leaves at 09:40.
    - It travels 210 km at 84 km/h.
    - The time taken is $ \frac{210}{84} = 2.5 $ hours.
    - Adding 2.5 hours to 09:40 gives 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.

    We are given:
    
    - **Ana** owns exactly one pet: a cat, dog, or parrot.
    - **Ben** owns exactly one pet: a cat, dog, or parrot.
    - **Chloe** owns exactly one pet: a cat, dog, or parrot.
    - No two people own the same pet.
    - **Ana does not own the dog.**
    - **Chloe does not own the cat.**
    - **Ben owns neither the cat nor the parrot.**
    
    Let's analyze step by step.
    
    ---
    
    ### Step 1: List the possible pets
    
    We have three pets: **cat**, **dog**, and **parrot**.
    
    Each person owns **one** pet, and **no two own the same**.
    
    ---
    
    ### Step 2: Use the constraints
    
    - **Ana does not own the dog** → So **Ana owns either a cat or a parrot**.
    - **Chloe does not own the cat** → So **Chloe owns either a dog or a parrot**.
    - **Ben owns neither the cat nor the parrot** → So **Ben owns the dog**.
    
    ---
    
    ### Step 3: Assign the remaining pets
    
    We know:
    
    - **Ben owns the dog**.
    - **Chloe does not own the cat**, so she must own either the dog or the parrot.
    - **Ana does not own the dog**, so she must own either the cat or the parrot.
    
    Let’s try assigning:
    
    - **Ben**: Dog
    - **Chloe**: Parrot
    - **Ana**: Cat
    
    Check all constraints:
    
    - **Ana**: Cat ✅ (not dog)
    - **Ben**: Dog ✅ (neither cat nor parrot)
    - **Chloe**: Parrot ✅ (not cat)
    
    All constraints are satisfied.
    
    ---
    
    ### ✅ 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.

    Here's a Python function `is_palindrome` that ignores case, spaces, and punctuation:
    
    ```python
    def is_palindrome(s: str) -> bool:
        # Convert to lowercase and remove all non-alphanumeric characters
        cleaned = ''.join([c.lower() for c in s if c.isalnum()])
        
        # Check if the cleaned string is equal to its reverse
        return cleaned == cleaned[::-1]
    ```
    
    ### Example Calls with Expected Results:
    
    ```python
    # Example 1
    print(is_palindrome("A man, a plan, a canal: Panama"))  # Expected: True
    
    # Example 2
    print(is_palindrome("Hello, World!"))  # Expected: False
    ```
    
    ### Explanation:
    - The function converts the input string to lowercase.
    - It removes all non-alphanumeric characters (like spaces and punctuation).
    - Finally, it checks if the cleaned string is equal to its reverse.
  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 in the function is that it is adding the values from the list starting at index 1, but it should be adding all the values from the list, not just the ones after the first element.
    
    **Corrected function:**
    
    ```python
    def average(values):
        total = sum(values)
        return total / len(values)
    ```
  8. Answering from a text

    FAIL · Invents an opening year (1920)
    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. When the reference readings drift, every other number from that night is marked as uncertain.  
    2. The observatory opened in 1920.
  9. Translating

    FAIL · Drops the thanks for the measurements and "tomorrow"
    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.

    감사합니다. 더 큰 모델을 다시 실행해 주시겠어요? 아침에 머신이 비워질 때입니다.
  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.

    Your model page is incorrect. The license states Apache, but the weights are non-commercial. Please fix this.
  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

    FAIL · Never says the model's weights must fit in VRAM
    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 (Video RAM) is a type of memory used to store data for graphics cards. It helps keep the GPU running smoothly by storing images and data that the GPU needs to process. Since VRAM is limited, it can't hold too much data, so AI models can't run as efficiently on a graphics card. This means VRAM is important for performance and efficiency in AI applications.
  13. Command line

    FAIL · The command doesn't find the largest files
    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.

    ls -l | grep -v "0" | awk '{print $1, $2, $3, $4, $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°F, and the Fahrenheit scale was proposed by the physicist Daniel Gabriel Fahrenheit.

Use it when

You need a small, commercially usable model that answers simple questions and writes short code.

Avoid it when

You need facts or exact formats from the 0.6B size: it invents details and misses output rules. The 8B size does much better.

Sizes

SizeDecodeVRAMAccuracyWeightsRunsLicense
0.6b-q8_0533 tok/s1.9 GiB57.3 IFEval, measured54.5 IFEval strict prompt paper610 MBAs isCommercial OK
8b-q4_k_m153 tok/s6.0 GiB80.6 IFEval, measured83 IFEval strict prompt paper4.68 GBAs isCommercial OK

From the authors

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

Paper

Qwen3 Technical Report

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng and 54 others

arXiv 2505.09388, 14 May 2025

Abstract

In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities. The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is the integration of thinking mode (for complex, multi-step reasoning) and non-thinking mode (for rapid, context-driven responses) into a unified framework. This eliminates the need to switch between different models--such as chat-optimized models (e.g., GPT-4o) and dedicated reasoning models (e.g., QwQ-32B)--and enables dynamic mode switching based on user queries or chat templates. Meanwhile, Qwen3 introduces a thinking budget mechanism, allowing users to allocate computational resources adaptively during inference, thereby balancing latency and performance based on task complexity. Moreover, by leveraging the knowledge from the flagship models, we significantly reduce the computational resources required to build smaller-scale models, while ensuring their highly competitive performance. Empirical evaluations demonstrate that Qwen3 achieves state-of-the-art results across diverse benchmarks, including tasks in code generation, mathematical reasoning, agent tasks, etc., competitive against larger MoE models and proprietary models. Compared to its predecessor Qwen2.5, Qwen3 expands multilingual support from 29 to 119 languages and dialects, enhancing global accessibility through improved cross-lingual understanding and generation capabilities. To facilitate reproducibility and community-driven research and development, all Qwen3 models are publicly accessible under Apache 2.0.

Alternatives

No alternatives listed yet. See every General model.

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