LLaDA2.1-flash

🚀 LLaDA2.1-flash is now live on ZenmuxAI! Try it via API 🛠️ or Chat 💬: https://zenmux.ai/inclusionai/llada2.1-flash

LLaDA2.1-flash is a diffusion language model of the LLaDA series featuring the editing enhancement. It significantly improves inference speed while delivering strong task performance.

Benchmark

Qwen3-30B-

A3B-Inst-2507

(Score)

Ling-flash-2.0

(Score)

LLaDA2.0-flash

(Score | TPF)

LLaDA2.1-flash

(S Mode)

(Score | TPF)

LLaDA2.1-flash

(Q Mode)

(Score | TPF)

Average

73.09

71.52

72.43 | 3.08

72.34 | 5.93

73.54 | 3.64

Knowledge

GPQA

54.14

69.16

62.31 | 3.29

66.67 | 3.95

67.30 | 2.37

MMLU-Pro

74.21

77.55

74.79 | 2.36

75.31 | 4.43

76.59 | 2.62

C-EVAL

88.12

87.54

85.21 | 1.90

86.93 | 2.71

86.71 | 1.75

PHYBench

29.84

27.67

30.06 | 2.70

26.04 | 4.10

28.23 | 2.66

TriviaQA

65.61

69.76

66.88 | 1.94

72.55 | 4.30

72.93 | 2.92

Reasoning

BIG-Bench Hard

85.54

89.36

86.75 | 2.66

87.82 | 5.61

88.69 | 3.28

BIG-Bench Extra Hard

37.80

23.24

27.86 | 4.60

33.51 | 5.04

35.77 | 3.17

bbh-zh

86.18

75.09

87.52 | 3.21

82.55 | 5.78

86.23 | 3.77

MuSR

79.15

82.72

80.48 | 1.70

80.10 | 2.90

79.84 | 1.85

ZebraLogic

90.97

87.60

82.30 | 2.74

84.20 | 5.80

88.90 | 3.26

PrOntoQA

97.12

97.88

96.50 | 2.64

95.00 | 9.23

97.00 | 5.73

PIQA

91.57

91.95

92.76 | 1.43

92.44 | 2.38

92.17 | 1.44

OCNLI

71.59

65.36

71.63 | 1.09

72.17 | 1.83

72.75 | 1.32

HellaSwag

86.31

81.59

84.97 | 1.26

85.60 | 2.31

85.31 | 1.51

KOR-Bench

69.2

69.44

63.04 | 3.44

62.80 | 4.97

65.12 | 2.77

DROP

87.57

88.32

87.90 | 2.26

87.55 | 5.40

87.86 | 2.53

SQuAD 2.0

89.51

81.32

90.00 | 3.10

90.65 | 5.01

90.80 | 3.90

Coding

LiveCodeBench

46.42

52.48

42.51 | 4.23

44.05 | 6.48

45.37 | 3.80

CRUXEval-O

86.75

82.75

85.12 | 3.21

85.25 | 6.54

87.50 | 3.80

MBPP+

78.21

80.89

79.37 | 4.02

76.72 | 10.43

77.25 | 5.96

HumanEval+

87.88

87.58

88.41 | 6.45

89.63 | 13.81

89.63 | 9.18

MultiPL-E

70.67

65.76

74.87 | 3.14

70.89 | 7.77

73.34 | 4.33

BigCodeBench-Full

41.49

40.70

41.58 | 3.33

37.11 | 8.51

39.21 | 4.70

BIRD-SQL

47.75

47.49

45.76 | 2.16

42.18 | 5.09

44.04 | 2.95

Spider

81.79

80.58

82.49 | 4.42

79.18 | 8.74

81.04 | 5.70

Math

AIME 2025

61.88

55.89

60.00 | 4.57

63.33 | 5.36

63.33 | 3.46

OlympiadBench

77.59

76.19

74.07 | 3.70

75.85 | 6.46

76.59 | 3.81

GSM-Plus

89.41

89.71

89.74 | 2.68

89.23 | 7.14

89.69 | 3.83

CMATH

96.58

96.52

96.90 | 2.17

96.54 | 4.84

96.63 | 2.65

Omni-MATH

54.00

53.00

50.30 | 3.39

52.30 | 6.01

54.10 | 3.50

Agent & Alignment

IFEval-strict-prompt

83.73

81.15

82.62 | 1.47

83.36 | 2.24

83.55 | 1.41

BFCL v3

73.41

67.69

74.94 | 4.87

74.86 | 9.24

75.61 | 6.76

Nexus FC

49.93

36.25

50.45 | 5.53

44.83 | 11.29

47.65 | 7.38

🚀 Highlights

Error-Correcting Editable: Structural innovation of editable generation for dLLM
Speedy vs Quality Mode: The 100B flash model achieves ultra-fast inference under Speed Mode while remaining competitive across various tasks and under Quality Mode.
Reinforcement Learning on 100B-scale dLLM: Tailored algorithm and framework to enable reinforcement learning for large dLLM.

🗺️ What's Next

Powerful Agentic/Tool Use Capability with LLaDA: Next update will be equipped with powerful Agentic and long-distance tool-use capability.
Extreme Editing: Next update will feature stronger and more extensive editing capabilities, aimed at correcting more errors in parallel reasoning.
Explore More Training Paradigms: We want to explore more training paradigms than SFT and RL for dLLM.

📦 Model Variants

| Model ID | Description | Hugging Face Link |

| --- | --- | --- |

| inclusionAI/LLaDA2.1-mini | Instruction-tuned model, ready for downstream applications. | 🤗 Model Card |

| inclusionAI/LLaDA2.1-flash | Instruction-tuned model, ready for downstream applications. | 🤗 Model Card |

🔍 Model Overview

LLaDA2.1-flash has the following specifications:

Type: Mixture-of-Experts (MoE) Diffusion Language Model
Total Parameters (Non-Embedding): 100B
Number of Layers: 32
Attention Heads: 32
Context Length: 32,768 tokens
Position Embedding: Rotary (RoPE)
Vocabulary Size: 157,184

🤗 Hugging Face Transformers

Make sure you have transformers and its dependencies installed:

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "/path/to/LLaDA2.1-flash"
device = "auto"
model = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, device_map=device,
)
model = model.to(torch.bfloat16)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

prompt = """Calculate 1+5-28*0.5-200=?"""
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
)
generated_tokens = model.generate(
    inputs=input_ids,
    eos_early_stop=True,
    gen_length=512,
    block_length=32,
    threshold=0.5,
    editing_threshold=0,
    temperature=0.0,
)
generated_answer = tokenizer.decode(
    generated_tokens[0],
    skip_special_tokens=True,
)
print(generated_answer)

Multi-block Editing inference comming soon.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

We recommend the following general sampling parameters: block_length=32, temperature=0.0, top_p=None and top_k=None. We are currently exploring more diverse sampling configurations.

2. Denoising Thresholds:

There are three denoising params: threshold, editing_threshold and max_post_steps. We recommend threshold=0.7, editing_threshold=0.5 for Quality Mode and threshold=0.5, editing_threshold=0.0 for Speed Mode. For both modes, we suggest setting max_post_steps to a value greater than 5. We recommend 16 as a balanced default, which was used for most of our internal testing.

Note: Low threshold may causes stuttering in trade-off for quick inference.

3. Adequate Output Length:

We recommend using an output length of 16384 tokens for most scenarios.

🤖ModelScope

If you're in mainland China, we strongly recommend you to use our model from 🤖ModelScope

Deployment

SGLang

SGLang enables dLLM inference either through offline batching or by launching an HTTP server for online requests. You can start the SGLang dLLM using the following commands:

python3 -m sglang.launch_server \
	  --model-path inclusionAI/LLaDA2.1-flash \
	  --dllm-algorithm JointThreshold \
	  --tp-size 4 \
	  --trust-remote-code \
	  --mem-fraction-static 0.8 \
	  --max-running-requests 1 \
	  --attention-backend flashinfer

Enviroment Preparation

Pull Request (PR) has been submitted and merged to the SGLang community, please prepare the environment with the lateset version

🌐 License

This project is licensed under the terms of the Apache License 2.0.

🤝 Contact & Collaboration

For questions, collaborations, or feedback, please reach out via Hugging Face or open an issue in the repository.

👉 Join us in advancing open, efficient, and intelligent language models!

Citation

@misc{bie2026llada21speedingtextdiffusion,
      title={LLaDA2.1: Speeding Up Text Diffusion via Token Editing}, 
      author={Tiwei Bie and Maosong Cao and Xiang Cao and Bingsen Chen and Fuyuan Chen and Kun Chen and Lun Du and Daozhuo Feng and Haibo Feng and Mingliang Gong and Zhuocheng Gong and Yanmei Gu and Jian Guan and Kaiyuan Guan and Hongliang He and Zenan Huang and Juyong Jiang and Zhonghui Jiang and Zhenzhong Lan and Chengxi Li and Jianguo Li and Zehuan Li and Huabin Liu and Lin Liu and Guoshan Lu and Yuan Lu and Yuxin Ma and Xingyu Mou and Zhenxuan Pan and Kaida Qiu and Yuji Ren and Jianfeng Tan and Yiding Tian and Zian Wang and Lanning Wei and Tao Wu and Yipeng Xing and Wentao Ye and Liangyu Zha and Tianze Zhang and Xiaolu Zhang and Junbo Zhao and Da Zheng and Hao Zhong and Wanli Zhong and Jun Zhou and Junlin Zhou and Liwang Zhu and Muzhi Zhu and Yihong Zhuang},
      year={2026},
      eprint={2602.08676},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2602.08676}, 
}

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