Parameters

81.3B

Context Window

262K

tokens

Max Output

16K

tokens

Input Price

$0.09

/1M tokens

Output Price

$1.10

/1M tokens

Overview

Qwen3 Next 80B A3b Instruct is a chat model by Qwen. It has 81.3B parameters. It supports a 262K token context window. Supports function calling.

Model Card

Qwen3-Next-80B-A3B-Instruct

Name: Qwen3 Next 80B A3B Instruct
Brand: Qwen
SKU: qwen/qwen3-next-80b-a3b-instruct
Price: 0.0900 USD
Availability: InStock

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.

Highlights

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.

Qwen3-Next-80B-A3B-Instruct Benchmark Comparison

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

Model Overview

[!Note]
Qwen3-Next-80B-A3B-Instruct supports only instruct (non-thinking) mode and does not generate ` 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


Performance
|  | 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.
Quickstart

The code for Qwen3-Next has been merged into the main branch of Hugging Face transformers.


pip install git+https://github.com/huggingface/transformers.git@main
With earlier versions, you will encounter the following error:
KeyError: 'qwen3_next'

The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-Next-80B-A3B-Instruct"
load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype="auto",
    device_map="auto",
)
prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)

[!Note]
Multi-Token Prediction (MTP) is not generally available in Hugging Face Transformers.
[!Note]
The efficiency or throughput improvement depends highly on the implementation.
It is recommended to adopt a dedicated inference framework, e.g., SGLang and vLLM, for inference tasks.

[!Tip] Depending on the inference settings, you may observe better efficiency withflash-linear-attention and causal-conv1d. See the links for detailed instructions and requirements.


Deployment

For deployment, you can use the latest sglang or vllm to create an OpenAI-compatible API endpoint.


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

sglang>=0.5.2is required for Qwen3-Next, which can be installed using:

pip install 'sglang[all]>=0.5.2'


See its documentation for more details.

The following command can be used to create an API endpoint at http://localhost:30000/v1with maximum context length 256K tokens using tensor parallel on 4 GPUs.

python -m sglang.launch_server --model-path Qwen/Qwen3-Next-80B-A3B-Instruct --port 30000 --tp-size 4 --context-length 262144 --mem-fraction-static 0.8


The following command is recommended for MTP with the rest settings the same as above:
python -m sglang.launch_server --model-path Qwen/Qwen3-Next-80B-A3B-Instruct --port 30000 --tp-size 4 --context-length 262144 --mem-fraction-static 0.8 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

[!Note] The default context length is 256K. Consider reducing the context length to a smaller value, e.g.,32768, if the server fails to start.


Please also refer to SGLang's usage guide on Qwen3-Next.
vLLM
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vLLM could be used to launch a server with OpenAI-compatible API service.

vllm>=0.10.2is required for Qwen3-Next, which can be installed using:

pip install 'vllm>=0.10.2'


See its documentation for more details.

The following command can be used to create an API endpoint at http://localhost:8000/v1with maximum context length 256K tokens using tensor parallel on 4 GPUs.

vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --port 8000 --tensor-parallel-size 4 --max-model-len 262144


The following command is recommended for MTP with the rest settings the same as above:
vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --port 8000 --tensor-parallel-size 4 --max-model-len 262144 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

[!Note] The default context length is 256K. Consider reducing the context length to a smaller value, e.g.,32768, if the server fails to start.


Please also refer to vLLM's usage guide on Qwen3-Next.
Agentic Use
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)
Processing Ultra-Long Texts
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 therope_scalingconfiguration only when processing long contexts is required. It is also recommended to modify thefactor 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.


Long-Context Performance
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).

Best Practices
To achieve optimal performance, we recommend the following settings:
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.


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

Citation

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}
}

Features & Capabilities

Mode	chat
Context Window	262,144 tokens
Max Output	16,384 tokens
Function Calling	Supported
Vision	-
Reasoning	-
Web Search	-
Url Context	-

Technical Details

Architecture	Qwen3NextForCausalLM
Model Type	qwen3_next
Library	transformers

API Usage

from openai import OpenAI

client = OpenAI(
    base_url="https://api.haimaker.ai/v1",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="qwen/qwen3-next-80b-a3b-instruct",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
)

print(response.choices[0].message.content)

Frequently Asked Questions

What is the context window of Qwen3 Next 80B A3B Instruct?

Qwen3 Next 80B A3B Instruct (qwen/qwen3-next-80b-a3b-instruct) has a 262,144-token context window and supports up to 16,384 output tokens per request.

How much does Qwen3 Next 80B A3B Instruct cost?

Qwen3 Next 80B A3B Instruct is priced at $0.09 per 1M input tokens and $1.10 per 1M output tokens when accessed via the haimaker.ai OpenAI-compatible API.

What features does Qwen3 Next 80B A3B Instruct support?

Qwen3 Next 80B A3B Instruct supports function calling.

How do I use Qwen3 Next 80B A3B Instruct via API?

Send requests to https://api.haimaker.ai/v1/chat/completions with model "qwen/qwen3-next-80b-a3b-instruct" using any OpenAI-compatible SDK. Authentication uses a Bearer API key from https://app.haimaker.ai.