Qwen3.8-Flash-Next-AWQ

English | 中文说明

English

This repository is a selective routed-experts-only AWQ derivative of Qwen3.8-Flash-Next. The gate/up/down projections of the 48 main-model routed MoE layers are quantized to asymmetric W4A16 AWQ with group size 128. The vision encoder, attention layers, shared experts, embeddings, normalization layers, LM head, and NEXTN/MTP weights remain in their original precision.

This checkpoint is not a stock Transformers or stock SGLang plug-and-play AWQ model. Correct loading requires the selective-AWQ SGLang patch in runtime/sglang-selective-awq.patch. A runtime that applies AWQ to every module may load the model incorrectly or fail during startup.

Quantization scope and compatibility

• Quantization: W4A16_AWQ_ASYM_G128, main-model routed experts only.
• Group size: 128; asymmetric zero points enabled.
• Native context: 262,144 tokens.
• Multimodal image input is preserved; the vision encoder remains BF16.
• NEXTN/MTP weights are preserved in their original precision.
• Validated SGLang base revision: 99c9362e6685db579c469f6e0e566b08827b3477.
• The included A800 MoeWNA16 Triton configuration is hardware-specific and should not be assumed optimal on other GPU architectures.

Included runtime assets

• runtime/sglang-selective-awq.patch: selective experts_only AWQ loader.
• runtime/start_sglang_a800_tp4.sh: validated TP4 launch example.
• runtime/configs/: expanded A800 MoeWNA16 Triton configuration.
• evaluation/: machine-readable performance and paired regression summaries.

Minimal setup from the matching SGLang revision:

git checkout 99c9362e6685db579c469f6e0e566b08827b3477
git apply /path/to/model/runtime/sglang-selective-awq.patch
bash /path/to/model/runtime/start_sglang_a800_tp4.sh /path/to/model

The launch example uses 4× NVIDIA A800-SXM4-80GB, TP4, BF16 KV, a 262K context window, 16K chunked prefill, and NEXTN with steps=3, draft tokens=4, and top-k=1. Adjust memory and hardware-specific kernel settings for other systems, but keep the selective-AWQ loader semantics.

Local validation summary

Validated on 4× NVIDIA A800-SXM4-80GB:

• Stable production throughput: C1 153.99, C4 420.10, C8 803.60, C16 1371.22, and C32 1829.15 output tok/s.
• Stable long-prefill TTFT: 32K 3.435s, 64K 6.870s, and 131K 14.634s.
• The expanded A800 MoE runtime improved normalized decode throughput by +8.49% at C1, +1.87% at C4, was effectively flat at C8, +1.78% at C16, and +3.27% at C32 versus the previous tuned runtime.
• A 148-case paired regression found zero baseline-only correctness regressions; tool use 25/25, OCR multimodal 10/10, and 30K retrieval 3/3.
• Fresh OpenAI-compatible image-input smoke testing returned HTTP 200 and correctly identified the test image, with image tokens reported by SGLang.

These are route-specific local measurements, not replacements for the upstream benchmark table below.

中文说明

本仓库是基于 Qwen3.8-Flash-Next 制作的选择性AWQ量化版本。仅对主模型 48层路由MoE专家的 gate/up/down 投影执行非对称W4A16 AWQ量化，group size为 128。视觉编码器、Attention、共享专家、Embedding、归一化层、LM Head以及 NEXTN/MTP权重均保持原始精度。

本模型不能视为Transformers或原版SGLang可直接加载的普通AWQ模型。 正确运行必须应用 runtime/sglang-selective-awq.patch 中的选择性AWQ加载补丁。 如果运行时把AWQ配置错误地应用到所有模块，模型可能启动失败或被错误加载。

量化范围与兼容性

• 量化格式：W4A16_AWQ_ASYM_G128，仅主模型路由专家。
• Group size：128，启用非对称zero point。
• 原生上下文长度：262,144 tokens。
• 保留图片输入能力；视觉编码器保持BF16。
• NEXTN/MTP权重保持原始精度。
• 已验证的SGLang基础版本： 99c9362e6685db579c469f6e0e566b08827b3477。
• 仓库附带的A800 MoeWNA16 Triton配置针对A800调优，在其他GPU上不保证最优。

仓库附带的运行文件

• runtime/sglang-selective-awq.patch：支持 experts_only 的选择性AWQ加载补丁。
• runtime/start_sglang_a800_tp4.sh：经过验证的TP4启动示例。
• runtime/configs/：扩展后的A800 MoeWNA16 Triton配置。
• evaluation/：机器可读的性能和配对回归结果摘要。

使用对应SGLang版本的最小步骤：

git checkout 99c9362e6685db579c469f6e0e566b08827b3477
git apply /path/to/model/runtime/sglang-selective-awq.patch
bash /path/to/model/runtime/start_sglang_a800_tp4.sh /path/to/model

启动示例对应4× NVIDIA A800-SXM4-80GB、TP4、BF16 KV、262K上下文、16K chunked prefill，以及NEXTN steps=3、draft tokens=4、top-k=1。其他硬件可调整 显存比例和kernel配置，但必须保留选择性AWQ加载语义。

本地验证摘要

在4× NVIDIA A800-SXM4-80GB上完成验证：

• 稳态输出吞吐：C1 153.99、C4 420.10、C8 803.60、C16 1371.22、 C32 1829.15 tok/s。
• 长prefill TTFT：32K 3.435秒、64K 6.870秒、131K 14.634秒。
• Expanded A800 MoE配置相对上一版调优runtime的归一化吞吐变化： C1 +8.49%、C4 +1.87%、C8基本持平、C16 +1.78%、C32 +3.27%。
• 148项配对回归中没有发现baseline-only正确样本回退；工具调用25/25、 OCR多模态10/10、30K长上下文检索3/3。
• 最新OpenAI兼容图片请求测试返回HTTP 200，正确识别测试图片，且SGLang明确 返回了image token计数。

以上数据是特定本地部署路径下的实测结果，不能替代下方上游官方benchmark。

---

Upstream Qwen3.8-Flash-Next model card

The remainder of this document is the upstream model card. Framework compatibility statements below refer to the original checkpoint; this AWQ derivative requires the compatibility notes above.

For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud.

In particular, Qwen3.8-Flash is the official version based on Qwen3.8-Flash-Next with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-Flash Overview.

As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next.

This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale.

Highlights

The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:

• Hybrid Attention with QSA: The Gated DeltaNet and Gated Attention pairing has been reworked into Gated DeltaNet and Qwen Sparse Attention (QSA). Rather than selecting individual tokens for processing, QSA operates at the micro-block level. This cuts long-context latency significantly, a critical gain as agentic workloads increasingly dominate real-world usage.
• Gated Residual: Residual streams with normalization are what make deep LLM training manageable. Gated Residual modulates information flowing through widened residual streams via an element-wise, data-dependent read gate and a per-branch scalar write gate. This brings finer-grained expressiveness across layers while preserving training stability and keeping inference overhead low.
• N-gram Embedding: Embeddings provide a unique axis for parameter scaling that requires less computation and is more amenable to offloading than Mixture-of-Experts (MoE). By indexing with short n-grams, this approach makes parameter scaling highly efficient for memory-constrained accelerators without sacrificing quality.
• Tailored Training Recipe: The Muon and AdamW optimizers are applied to specific weight categories to maximize efficiency. Guided by refitted scaling laws, we eliminate traditional batch-size warmups and start directly at the target batch size, substantially reducing total optimizer steps while safely supporting larger learning rates for robust convergence.

For more details, please refer to our blog post Qwen3.8-Flash-Next and the technical report.

We are excited to embark on this next chapter with you and welcome your feedback as we build what comes next.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP
  • Hidden Dimension: 2560
  • Token Embedding: 248320 (Padded)
  • N-gram Embedding: 20,000,000 (bigrams/trigrams at layer 2)
  • Number of Layers: 48
  • Hidden Layout: 12 × (3 × (Gated DeltaNet → MoE) → 1 × (Qwen Sparse Attention → MoE))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 48 for V and 16 for QK
    • Head Dimension: 128
  • Qwen Sparse Attention:
    • Number of Attention Heads: 24 for Q and 2 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
    • Indexer Structure: MQA with 4 Query Heads and 1 Shared Key Head
    • Indexer Head Dimension: 128
    • Budget: 512 blocks or 2048 tokens
  • Mixture Of Experts
    • Number of Experts: 512
    • Number of Activated Experts: 10 Routed + 1 Shared
    • Expert Intermediate Dimension: 640
  • Gated Residual:
    • Number of Branches: 4
    • Bottleneck Rank: 320
  • LM Output: 248320 (Padded)
  • MTP: 1 layer, trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,000,000 tokens.

Benchmark Results

Language

	Qwen3.8-Flash-Next	Qwen3.8-27B	Qwen3.7-Plus	DeepSeek-V4-Flash-0731	Claude-Opus-4.6 (Max)
# Params	125B	27B	397B	284B	--
# Activated params	6B	27B	17B	13B	--
# N-gram embedding params	51B	--	--	--	--
Coding
Agentic coding DeepSWE 1.1	58.7	42.2	16.5	54.4	--
Agentic coding SWE-bench Pro	62.5	61.7	55.8	56.0	53.4
Multilingual software engineering SWE-bench Multilingual	81.0	73.8	75.8	--	77.5
Repo-level code generation NL2Repo-Bench	48.1	42.3	41.1	54.2	47.6
Agent
Long-horizon office work CoWorkBench	73.9	70.7	65.1	45.1	68.2
Professional job tasks JobBench	55.7	33.4	27.6	41.3	36.6
Frontier agentic tasks Agents' Last Exam	Pass@1 24.3 Score 51.2	Pass@1 20.4 Score 42.9	Pass@1 13.2 Score 33.6	Pass@1 25.2 Score --	--
Real-world tool use Toolathlon Verified (Pass@1)	73.5	67.1	50.6	70.3	--
General
Instruction following IFBench	81.3	79.5	79.1	79.2	62.5
Scientific reasoning GPQA Diamond	91.7	89.2	90.3	90.8	91.3
Multidisciplinary reasoning HLE	35.9	30.8	34.7	33.8	40.0
Competitive coding LiveCodeBench v6	91.9	90.3	89.6	90.6	88.8

1. DeepSWE 1.1: evaluated with the Claude Code and mini-SWE-agent harnesses, temp=1.0, top_p=0.95, 256K context window. We report the highest score across the two harnesses; notably, Qwen3.8-Flash-Next performs best on mini-SWE-agent.
2. SWE-bench Pro: except for Claude-Opus-4.6 (Max), for which we report the officially published score, all models are evaluated with the Claude Code harness, temp=1.0, top_p=0.95, 256K context window. Problematic tasks were corrected and all baseline models were re-evaluated on the refined benchmark.
3. SWE-bench Multilingual: evaluated with the mini-SWE-agent harness, temp=1.0, top_p=0.95, 256K context window.
4. NL2Repo-Bench: evaluated with the Claude Code harness. To prevent reward hacking, we disable Bash commands that attempt to access the specific repository, such as pip download, pip install and git clone.
5. CoWorkBench: an in-house cowork benchmark for evaluating long-horizon office and productivity agent tasks across computer science, finance, law, medical and other productivity domains.
6. HLE: judged by GPT-4o.
7. The best result in each row is shown in bold.
8. Empty cells (--): scores are not yet available or are not applicable.

Vision Language

	Qwen3.8-Flash-Next	Qwen3.8-27B	Qwen3.7-Plus	Claude-Opus-4.6 (Max)
Agentic Multimodal Intelligence
Multimodal tool use ClawEval-MM	Pass@3 64.4 Average 60.4	Pass@3 57.4 Average 56.9	Pass@3 57.4 Average 60.1	Pass@3 52.5 Average 54.7
Application recreation RecreationBench	49.9	47.1	30.2	--
Mobile use AndroidWorld	84.5	81.9	81.0	62.0
Computer use OSWorld 2.0	Binary 19.4 Partial 52.3	Binary 19.4 Partial 48.0	Binary 2.8 Partial 21.5	--
Visual web development Vision2Web	64.0	62.9	42.1	--
General Multimodal Intelligence
Embodied intelligence ERQA	72.3	65.5	69.8	40.8
Long video understanding LVBench	76.6	72.4	76.2	63.0
Real-world perception RealWorldQA	88.5	85.9	86.9	73.9
Visual math problem solving MathVision	Without CI 90.6 With CI 95.7	Without CI 90.0 With CI 94.6	Without CI 90.3 With CI 88.7	Without CI 65.5
Scientific chart analysis CharXiv (RQ)	Without CI 84.6 With CI 90.6	Without CI 83.7 With CI 90.2	Without CI 85.8 With CI 85.9	Without CI 66.0

1. ClawEval-MM: scores are reported as "pass@3 / average score". Pass@3 measures the percentage passed in at least one of three trials, and the average score is the mean score across the three trials.
2. RecreationBench: an in-house long-horizon application-recreation benchmark for evaluating hybrid-agent abilities spanning five platforms — desktop (Ubuntu, macOS, Windows), mobile (Android) and web.
3. OSWorld 2.0: scores are reported as "binary / partial". The binary score is the percentage of tasks that receive the full task reward, while the partial score aggregates the partial rewards obtained across all tasks.
4. Vision2Web: scores are reported as the average over the frontend, webpage and website categories, using the Claude Code harness and judged by gpt-5.4-2026-03-05.
5. MathVision, CharXiv (RQ): scores are reported as "without CI / with CI". A small number of incorrect ground-truth annotations in MathVision were corrected after manual verification. Our model's score is evaluated using a fixed prompt, e.g. "Please reason step by step, and put your final answer within \boxed{}." For other models, we report the higher score between runs with and without the \boxed{} formatting.
6. The best result in each row is shown in bold.
7. Empty cells (--) indicate scores not yet available or not applicable.

Quickstart

For streamlined integration, we recommend using Qwen3.8-Flash-Next via APIs.

Serving Qwen3.8-Flash-Next

Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.

Qwen3.8-Flash-Next can be deployed with popular inference frameworks, e.g.:

• SGLang: Qwen3.8-Flash-Next Cookbook
• vLLM: Qwen3.8-Flash-Next Recipe
• TokenSpeed: Qwen3.8-Flash-Next Recipe

API Usage

Qwen3.8-Flash-Next models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

We recommend using the following sets of sampling parameters for generation:

• Thinking Mode: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

In multi-turn agentic tasks, lower reasoning effort does not always reduce overall task completion time. Although it may produce faster per-turn responses, it can also lead to insufficient analysis, more failures, and repeated retries, which may increase total latency and token consumption.

Qwen3.8-Flash-Next supports controlling thinking behavior via enable_thinking, preserve_thinking, and reasoning_effort.

Chat Completions API

The Chat Completions API can be used with most inference frameworks, as well as Qwen Cloud. Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"

Text-Only Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Write a Python function to merge two sorted linked lists."},
]

completion = client.chat.completions.create(
    model="Qwen/Qwen3.8-Flash-Next",
    messages=messages,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # on by default
            "preserve_thinking": True, # on by default
        },
    },
    reasoning_effort="xhigh",  # xhigh by default; supported levels are xhigh, medium, and low
    stream=True,
    stream_options={"include_usage": True},
)

reasoning_content = ""
answer_content = ""
is_answering = False
print("\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content
    elif hasattr(delta, "reasoning") and delta.reasoning is not None:
        if not is_answering:
            print(delta.reasoning, end="", flush=True)
        reasoning_content += delta.reasoning

    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Answer" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

messages.append({
    "role": "assistant",
    "content": answer_content,
    "reasoning_content": reasoning_content,
    "reasoning": reasoning_content,
})

Image Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"
                }
            },
            {
                "type": "text",
                "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-Flash-Next",
    messages=messages,
)
print("Chat response:", chat_response)

Video Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"
                }
            },
            {
                "type": "text",
                "text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-Flash-Next",
    messages=messages,
)

# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,
# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).
# This feature is currently supported only in vLLM.
#
# By default, `fps=2` and `do_sample_frames=True`.
# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.
# chat_response = client.chat.completions.create(
#     model="Qwen/Qwen3.8-Flash-Next",
#     messages=messages,
#     extra_body={
#         "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
#     }, 
# )

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

Qwen3.8-Flash-Next will think by default before responding. You can obtain a direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"
                }
            },
            {
                "type": "text",
                "text": "Where is this?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-Flash-Next",
    messages=messages,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)
print("Chat response:", chat_response)

If you are using APIs from Qwen Cloud, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Disable Preserved Thinking

By default, Qwen3.8-Flash-Next retains thinking blocks from all historical messages, maintaining a complete reasoning trace across the conversation. This behavior, known as preserved thinking, ensures full context continuity and is especially beneficial for agent scenarios where decision consistency and reduced redundant reasoning are critical. It also improves KV cache utilization, optimizing inference efficiency in both thinking and non-thinking modes.

If you prefer to retain only the thinking blocks from the latest user message, you can disable this behavior by setting preserve_thinking to False:

from openai import OpenAI

# Configured by environment variables
client = OpenAI()
messages = [...]
chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-Flash-Next",
    messages=messages,
    extra_body={
        "chat_template_kwargs": {"preserve_thinking": False},
    },
)
print("Chat response:", chat_response)

If you are using APIs from Qwen Cloud, in addition to changing model, please use "preserve_thinking": False directly instead of wrapping it in chat_template_kwargs.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters: We suggest using the following sets of sampling parameters:

   • Thinking Mode: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
   • Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

   For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetition. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: To optimize performance on agentic tasks, we recommend allocating sufficient output length to allow the model to generate detailed and comprehensive responses. For frameworks that support separate token limits for internal reasoning and final outputs, we suggest the following configuration within the 1M context length:

   • Reasoning Content: Set the maximum output length to 262,144 tokens.
   • Final Response: Set the maximum output length to 131,072 tokens.

   These settings provide the necessary capacity for complex reasoning while ensuring ample space for high-quality final deliverables.

3. Processing Ultra-Long Texts: Qwen3.8-Flash-Next natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively, e.g., YaRN.

   YaRN is currently supported by several inference frameworks, e.g., vLLM, SGLang, and TokenSpeed. In general, there are two approaches to enabling YaRN for supported frameworks:

   • Modifying the model configuration file:

     In the config.json file, change the rope_parameters fields in text_config to:

     {
         "mrope_interleaved": true,
         "mrope_section": [
             11,
             11,
             10
         ],
         "rope_type": "yarn",
         "rope_theta": 10000000,
         "partial_rotary_factor": 0.25,
         "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 ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000  
     

     For SGLang, you can use

     SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1000000
     

     For TokenSpeed, you can use

     TOKENSPEED_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 tokenspeed serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000  
     

   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 modifying the rope_parameters 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.

4. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

   {"longest_edge": 469762048, "shortest_edge": 4096}
   

   Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

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

@techreport{qwen2026design,
    title       = {On the Design of {Qwen3.8-Next} Architecture: Evaluation, Efficiency, and Training Stability},
    author      = {{Qwen Team}},
    institution = {Alibaba Group},
    month       = {August},
    year        = {2026}
}

@misc{qwen3.8flashnext,
    title  = {{Qwen3.8-Flash-Next}: A New Architecture, Towards Ultimate Cost-Efficiency},
    author = {{Qwen Team}},
    month  = {August},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.8-flash-next}
}