Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models Qwen3-Next.

Qwen3-Next-80B-A3B is the first installment in the Qwen3-Next series and features the following key enchancements:

• Hybrid Attention: Replaces standard attention with the combination of Gated DeltaNet and Gated Attention, enabling efficient context modeling for ultra-long context length.
• High-Sparsity Mixture-of-Experts (MoE): Achieves an extreme low activation ratio in MoE layers, drastically reducing FLOPs per token while preserving model capacity.
• Stability Optimizations: Includes techniques such as zero-centered and weight-decayed layernorm, and other stabilizing enhancements for robust pre-training and post-training.
• Multi-Token Prediction (MTP): Boosts pretraining model performance and accelerates inference.

We are seeing strong performance in terms of both parameter efficiency and inference speed for Qwen3-Next-80B-A3B:

• Qwen3-Next-80B-A3B-Base outperforms Qwen3-32B-Base on downstream tasks with 10% of the total training cost and with 10 times inference throughput for context over 32K tokens.
• Qwen3-Next-80B-A3B-Instruct performs on par with Qwen3-235B-A22B-Instruct-2507 on certain benchmarks, while demonstrating significant advantages in handling ultra-long-context tasks up to 256K tokens.

For more details, please refer to our blog post Qwen3-Next.

[!Note] Qwen3-Next-80B-A3B-Instruct supports only instruct (non-thinking) mode and does not generate <think></think> blocks in its output.

Qwen3-Next-80B-A3B-Instruct has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining (15T tokens) & Post-training
• Number of Parameters: 80B in total and 3B activated
• Number of Paramaters (Non-Embedding): 79B
• Hidden Dimension: 2048
• Number of Layers: 48
  • Hybrid Layout: 12 * (3 * (Gated DeltaNet -> MoE) -> 1 * (Gated Attention -> MoE))
• Gated Attention:
  • Number of Attention Heads: 16 for Q and 2 for KV
  • Head Dimension: 256
  • Rotary Position Embedding Dimension: 64
• Gated DeltaNet:
  • Number of Linear Attention Heads: 32 for V and 16 for QK
  • Head Dimension: 128
• Mixture of Experts:
  • Number of Experts: 512
  • Number of Activated Experts: 10
  • Number of Shared Experts: 1
  • Expert Intermediate Dimension: 512
• Context Length: 262,144 natively and extensible up to 1,010,000 tokens

	Qwen3-30B-A3B-Instruct-2507	Qwen3-32B Non-Thinking	Qwen3-235B-A22B-Instruct-2507	Qwen3-Next-80B-A3B-Instruct
Knowledge
MMLU-Pro	78.4	71.9	83.0	80.6
MMLU-Redux	89.3	85.7	93.1	90.9
GPQA	70.4	54.6	77.5	72.9
SuperGPQA	53.4	43.2	62.6	58.8
Reasoning
AIME25	61.3	20.2	70.3	69.5
HMMT25	43.0	9.8	55.4	54.1
LiveBench 20241125	69.0	59.8	75.4	75.8
Coding
LiveCodeBench v6 (25.02-25.05)	43.2	29.1	51.8	56.6
MultiPL-E	83.8	76.9	87.9	87.8
Aider-Polyglot	35.6	40.0	57.3	49.8
Alignment
IFEval	84.7	83.2	88.7	87.6
Arena-Hard v2*	69.0	34.1	79.2	82.7
Creative Writing v3	86.0	78.3	87.5	85.3
WritingBench	85.5	75.4	85.2	87.3
Agent
BFCL-v3	65.1	63.0	70.9	70.3
TAU1-Retail	59.1	40.1	71.3	60.9
TAU1-Airline	40.0	17.0	44.0	44.0
TAU2-Retail	57.0	48.8	74.6	57.3
TAU2-Airline	38.0	24.0	50.0	45.5
TAU2-Telecom	12.3	24.6	32.5	13.2
Multilingualism
MultiIF	67.9	70.7	77.5	75.8
MMLU-ProX	72.0	69.3	79.4	76.7
INCLUDE	71.9	70.9	79.5	78.9
PolyMATH	43.1	22.5	50.2	45.9

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

Check out our llama.cpp documentation for more usage guide.

We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository llama.cpp.

./llama-cli -hf Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF:Q8_0 --jinja --color -ngl 99 -fa on -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 262144 -n 256000 --no-context-shift

Qwen3 natively supports context lengths of up to 262,144 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the YaRN method.

To enable YARN in llama.cpp:

./llama-cli ... -c 1010000 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 262144

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-Next-80B-A3B-Instruct',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Qwen3-Next natively supports context lengths of up to 262,144 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 1 million tokens using the YaRN method.

YaRN is currently supported by several inference frameworks, e.g., transformers, vllm and sglang. In general, there are two approaches to enabling YaRN for supported frameworks:

• Modifying the model files: In the config.json file, add the rope_scaling fields:

  {
      ...,
      "rope_scaling": {
          "rope_type": "yarn",
          "factor": 4.0,
          "original_max_position_embeddings": 262144
      }
  }
  

• Passing command line arguments:

  For vllm, you can use

  VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144}' --max-model-len 1010000  
  

  For sglang, you can use

  SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144}}' --context-length 1010000
  

[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

We test the model on an 1M version of the RULER benchmark.

Model Name	Acc avg	4k	8k	16k	32k	64k	96k	128k	192k	256k	384k	512k	640k	768k	896k	1000k
Qwen3-30B-A3B-Instruct-2507	86.8	98.0	96.7	96.9	97.2	93.4	91.0	89.1	89.8	82.5	83.6	78.4	79.7	77.6	75.7	72.8
Qwen3-235B-A22B-Instruct-2507	92.5	98.5	97.6	96.9	97.3	95.8	94.9	93.9	94.5	91.0	92.2	90.9	87.8	84.8	86.5	84.5
Qwen3-Next-80B-A3B-Instruct	91.8	98.5	99.0	98.0	98.7	97.6	95.0	96.0	94.0	93.5	91.7	86.9	85.5	81.7	80.3	80.3

• Qwen3-Next are evaluated with YaRN enabled. Qwen3-2507 models are evaluated with Dual Chunk Attention enabled.
• Since the evaluation is time-consuming, we use 260 samples for each length (13 sub-tasks, 20 samples for each).

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

   • We suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
   • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

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

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

@article{qwen2.5-1m,
      title={Qwen2.5-1M Technical Report}, 
      author={An Yang and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoyan Huang and Jiandong Jiang and Jianhong Tu and Jianwei Zhang and Jingren Zhou and Junyang Lin and Kai Dang and Kexin Yang and Le Yu and Mei Li and Minmin Sun and Qin Zhu and Rui Men and Tao He and Weijia Xu and Wenbiao Yin and Wenyuan Yu and Xiafei Qiu and Xingzhang Ren and Xinlong Yang and Yong Li and Zhiying Xu and Zipeng Zhang},
      journal={arXiv preprint arXiv:2501.15383},
      year={2025}
}