Tech Report 📄

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters (with 27B activated), excelling at real-time audio-visual interaction, which is attained by leveraging LongCat-Flash's high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, augmented by efficient multimodal perception and speech reconstruction modules. Through an effective curriculum-inspired progressive training strategy, our model achieves comprehensive multimodal capabilities while maintaining strong unimodal capability. Now, we open-source the model to foster future research and development in the community.

LongCat-Flash-Omni is an open-source omni-modal model that achieves state-of-the-art cross-modal comprehension performance. It seamlessly integrates powerful offline multi-modal understanding with real-time audio–visual interaction within a single all-in-one framework.

By leveraging an efficient LLM backbone, carefully designed lightweight modality encoders and decoder, and a chunk-wise audio–visual feature interleaving mechanism, LongCat-Flash-Omni achieves low-latency, high-quality audio–visual processing and streaming speech generation. It supports a context window of up to 128K tokens, enabling advanced capabilities in long-term memory, multi-turn dialogue, and temporal reasoning across multiple modalities.

The model adopts an innovative multi-stage pretraining pipeline that progressively incorporates text, audio, and visual modalities under a balanced data strategy and early-fusion training paradigm, ensuring strong omni-modal performance without degradation in any single modality.

Inspired by the concept of modality decoupling, we propose a Modality-Decoupled Parallelism training scheme that significantly enhances the efficiency of large-scale and highly challenging multimodal training.

We provide a comprehensive overview of the training methodology and data strategies behind LongCat-Flash-Omni, and release the model to accelerate future research and innovation in omni-modal intelligence.

For more detail, please refer to the comprehensive LongCat-Flash-Omni Technical Report.

Omni-modality

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	Gemini-2.5-Flash (non-thinking)	Qwen3-Omni Instruct	Qwen2.5-Omni Instruct
OmniBench	61.38	66.80	54.99	58.41	48.16
WorldSense	60.89	63.96	58.72	52.01	46.69
DailyOmni	82.38	80.61	80.78	69.33	47.45
UNO-Bench	49.90	64.48	54.30	42.10	32.60

Vision

Image-to-Text

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	Gemini-2.5-Flash (non-thinking)	Qwen3-Omni Instruct	Seed-1.6	GPT-4o-1120	Qwen3-VL-235B-A22B-Instruct	Qwen2.5-VL-72B-Instruct
General
MMBench-ENtest	87.5	89.8	89.3	86.8	88.5	83.7	88.3	88.6*
MMBench-ZHtest	88.7	89.2	88.5	86.4	83.8	82.8	89.8	87.9*
RealWorldQA	74.8	76.0	73.9	72.9	74.5	74.1	79.3*	75.7*
MMStar	70.9	78.5*	75.5	68.5*	71.5	63.2	78.4*	68.2
STEM & Reasoning
MathVistamini	77.9	77.7*	77.1	75.9	78.7	62.8	84.9*	74.8*
MMMUval	70.7	80.9*	76.3	69.1*	74.9	69.4	78.7*	70.2*
MMVet	69.0	80.7	79.5	68.9	74.4	76.6	75.9	74.5
Multi-Image
BLINK	63.1	70.0*	65.7	56.1	65.0	65.5	70.7*	60.1
MuirBench	77.1	74.0*	73.7	62.1	74.6	70.5	72.8*	70.7*
Mantis	84.8	83.9	83.4	80.7	81.1	79.3	79.7	82.0
Text Recognition & Chart/Document Understanding
ChartQA	87.6	71.7	77.6	86.8*	82.4	74.5	89.2	89.5*
DocVQA	91.8	94.0*	93.6*	95.7	94.3	80.9	94.6	96.4*
OCRBench	84.9	87.2*	85.6	85.5	85.6	82.3	91.2	88.5
OmniDocBenchEN/ZH↓	22.8/29.0	31.9/24.5	22.8/32.9	28.4/40.5	22.0/27.6	25.9/37.7	13.6/17.5	22.6/32.4*
Grounding & Counting
RefCOCO-avg	92.3	75.4	71.9	89.3	80.2	-	87.1	90.3
CountBench	92.4	91.0*	78.6	90.0*	94.1	85.6*	94.3	93.6*
Graphical User Interface (GUI)
VisualWebBench	78.7	81.1	73.5	79.3	81.1	77.1	80.8	82.3*
ScreenSpot-v2	91.2	75.8	63.9	94.7	91.7	-	93.4	92.9
AndroidControllow	91.2	79.2	79.1	90.5	84.6	65.2	90.0	93.7*
AndroidControlhigh	75.6	60.8	55.5	70.8	55.2	41.7	74.1	67.4*

Note: Values marked with * are sourced from public reports. As GPT-4o does not support image grounding, we do not report its results on RefCOCO and ScreenSpot-v2

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Video-to-Text

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	Gemini-2.5-Flash (non-thinking)	Qwen3-Omni Instruct	Seed-1.6	GPT-4o-1120	Qwen3-VL (235B-A22B-Instruct)	Qwen2.5-VL-72B-Instruct
Short Video
MVBench	75.2	66.4	63.0	69.3*	68.4	62.1	71.3	70.4*
NextQA	86.2	84.2	81.4	82.4	84.1	79.7	81.3	82.3
TempCompass	82.2	80.8	80.2	73.5	79.4	76.4	80.5	74.8*
Long Video
VideoMME (w/o audio)	76.2	-	-	70.5*	75.2	73.2	79.2*	73.3*
VideoMME (w/ audio)	78.2	80.6*	78.5	73.0	-	-	-	-
LongVideoBench	69.3	69.4	66.4	65.4	64.8	63.9	-	60.7*
STEM & Reasoning
MMVU	67.1	75.6	72.4	62.4	67.3	67.4	69.3	62.9*
Video-MMMU	67.5	79.4*	76.6	60.3	75.4	68.0	73.7	59.3

Note: Values marked with * are sourced from public reports.

Audio

Table 1: Automatic Speech Recognition (ASR) and Speech-to-Text Translation (S2TT)

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	GPT-4o-Audio	Qwen3-Omni Instruct	Kimi-Audio	Step-Audio-2-mini
ASR
LibriSpeech (test-clean | test-other)	1.57 | 4.01	1.74 | 3.80	30.00 | 41.83	1.22 | 2.48	1.28 | 2.42	1.33 | 2.86
AISHELL-1	0.63	3.11	34.81	0.84	0.60	0.78
AISHELL-2	2.78	5.24	77.73	2.34	2.56	2.16
Fleurs (zh | en)	3.99 | 5.02	2.24 | 4.77	3.91 | 5.56	2.20 | 2.72	2.69 | 4.44	2.53 | 3.05
CommonVoice 15 (zh | en)	4.98 | 13.59	47.30 | 49.86	42.83 | 23.88	4.31 | 6.05	8.46 | 7.92	5.00 | 6.75
WenetSpeech (test-meeting | test-net)	6.69 | 6.09	136.13 | 32.82	54.35 | 67.90	5.89 | 4.69	6.28 | 5.37	4.87 | 4.82
S2TT (BLEU)
CoVost2 en→zh	47.23	41.94	29.32	48.72	-	49.12
CoVost2 zh→en	27.32	25.38	16.01	21.51	-	29.47

Note: ASR results are in CER/WER (lower is better), S2TT results are in BLEU score.

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Table 2: Audio Understanding

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	GPT-4o-Audio	Qwen3-Omni Instruct	Kimi-Audio	Step-Audio-2-mini
MMAU	75.90	72.80	68.40	77.50	65.20	73.20
VocalSound	92.76	89.45	82.37	91.60	94.85	87.58
TUT2017	65.43	33.15	20.74	40.74	65.25	30.67
ClothoAQA	72.83	69.67	61.87	75.16	72.21	68.39
Nonspeech7k	93.79	87.59	72.28	80.83	93.93	73.24
CochlScene	70.02	45.34	34.94	43.03	80.42	44.58
MELD	54.60	46.74	39.00	50.80	59.13	31.44

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Table 3: Audio-to-Text Chat

Benchmark	LongCat-Flash-Omni Instruct	Gemini-2.5-Pro (ThinkingBudget128)	GPT-4o-Audio	Qwen3-Omni Instruct	Kimi-Audio	Step-Audio-2-mini
OpenAudioBench
LlamaQuestions	83.33	83.00	86.30	83.30	79.33	69.70
ReasoningQA	79.71	80.30	68.71	84.16	58.02	55.64
TriviaQA	86.20	90.20	76.00	75.90	62.10	45.30
Webquestions	76.00	80.90	81.20	75.20	70.20	54.40
AlpacaEval	75.43	76.58	81.61	85.43	75.73	53.92
VoiceBench
AlpacaEval	4.94	4.70	4.73	4.74	4.46	3.84
CommonEval	4.32	4.11	4.37	4.54	3.97	3.19
OpenBookQA	93.41	95.16	87.90	89.70	83.52	72.97
SDQA	82.46	83.54	90.10	76.90	63.12	44.85
MMSU	81.95	88.32	78.90	69.00	62.17	52.00
AdvBench	100	97.69	99.23	99.30	100	97.00
IFEval	77.99	77.83	66.81	77.80	61.10	29.80

Text

Benchmark	LongCat-Flash-Omni Instruct	LongCat-Flash	DeepSeek V3.1	Qwen3 MoE-2507	Kimi-K2	GPT-4.1	Claude Sonnet-4	Gemini-2.5-Flash
Architecture	MoE	MoE	MoE	MoE	MoE	-	-	-
# Total Params	560B	560B	671B	235B	1043B	-	-	-
# Activated Params	27B	27B	37B	22B	32B	-	-	-
General Domains
MMLU(acc)	90.30	89.71	90.96	90.23	89.86	89.64	91.75	86.33
MMLU-Pro(acc)	82.73	82.68	84.45	84.83	82.06	81.72	83.74	81.95
CEval(acc)	91.68	90.44	89.21	92.70	91.26	79.53	86.63	78.78
CMMLU(acc)	89.39	84.34	88.04	88.14	89.66	77.65	86.51	78.30
Instruction Following
IFEval(acc)	82.44	89.65	86.69	88.54	88.91	85.58	88.35	83.92
COLLIE(acc)	45.69	57.10	43.80	49.71	56.34	50.00	51.22	48.60
Meeseeks-zh(acc)	39.05	43.03	33.83	35.32	42.79	41.54	35.07	34.84
Mathematical Reasoning
MATH500(acc)	97.60	96.40	96.08	98.80	97.60	90.60	93.80	98.40
AIME24(avg@10)	72.92	70.42	66.30*	81.67	69.60*	47.00	47.00	79.67
BeyondAIME(avg@10)	47.40	43.00	36.50	57.60	36.60	22.10	20.50	44.20
General Reasoning
GPQA-diamond(acc)	74.41	73.23	74.90*	77.43	75.76	67.68	70.71	80.30
DROP(f1)	83.53	79.06	84.19	78.57	89.04	66.94	73.06	45.03
ZebraLogic(acc)	86.00	89.30	85.30	94.22	89.11	56.30*	80.10	57.00
GraphWalks-128k(precision)	56.00	51.05	73.54	80.72	47.50	85.02	80.57	64.83
Coding
LiveCodeBench(pass@1)	52.64	48.02	56.40*	46.48	46.70	39.21	45.59	39.65
Humaneval+(pass@1)	90.85	88.41	92.68	94.51	85.98	93.29	94.51	87.80
MBPP+(pass@1)	80.16	79.63	79.89	79.89	81.75	79.37	80.16	76.19

Note: Values marked with * are sourced from other public reports. Note that DeepSeek-V3.1, Qwen3-235B-A22B, Gemini2.5-Flash, and Claude4-Sonnet are evaluated under their non-thinking mode.

LongCat-Flash-Omni is a MoE model, which means that the model weights are distributed across multiple devices. Therefore, during loading in Hugging Face Transformers or vLLM, model weights will be automatically downloaded based on the model name. However, if your runtime environment is not conducive to downloading weights during execution, you can refer to the following commands to manually download the model weights to a local directory:

# Download through Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download meituan-longcat/LongCat-Flash-Omni --local-dir ./LongCat-Flash-Omni

We have implemented basic adaptations in SGLang to support running the Longcat-Flash-Omni model. Currently, the official SGLang does not natively support Longcat-Flash-Omni, so you can temporarily use our development branch for local installation and testing.

Due to its size of 560 billion parameters (560B), LongCat-Flash-Omni requires at least one node (e.g., 8×H20-141G) to host the model weights in FP8 format, and at least two nodes (e.g., 16×H800-80G) for BF16 weights. Detailed launch configurations are provided below.

• python >= 3.10.0 (Recommend to use Anaconda)
• PyTorch >= 2.8
• CUDA >= 12.9

conda create -n longcat python=3.10
conda activate longcat

# install SGLang
git clone -b longcat_omni_v0.5.3.post3 https://github.com/XiaoBin1992/sglang.git
pushd sglang
pip install -e "python"
popd

# install longcat-flash-omni demo
git clone https://github.com/meituan-longcat/LongCat-Flash-Omni
pushd LongCat-Flash-Omni
git submodule update --init --recursive
pip install -r requirements.txt
popd

The model can be served on your cluster using a combination of Tensor Parallelism and Expert Parallelism. Once all dependencies are installed, you can launch the demo using the following command.

• single-node inference

python3 longcat_omni_demo.py \
  --tp-size 8 \
  --ep-size 8 \
  --model-path where_you_download_model_dir \
  --output-dir output

• multi-node inference

python3 longcat_omni_demo.py \
  --tp-size 16 \
  --ep-size 16 \
  --nodes 2 \
  --node-rank $NODE_RANK \
  --dist-init-addr $MASTER_IP:5000 \
  --model-path where_you_download_model_dir \
  --output-dir output

NOTE: Replace $NODE_RANK and $MASTER_IP with the corresponding values of your GPU machines.

All test cases are defined in examples_dict.py, and additional test cases may be added as needed. After model execution, the generated results are saved in the directory specified by the --output-dir parameter.

You can use LongCat-Flash-Omni (web version currently only supports audio interaction features) on https://longcat.ai. The full service will be provided in subsequent updates.

We are excited to announce that the LongCat-Flash-Omni app is now available for both Android and iOS.

For Android, you can download it from the following QR code.

<img src=https://raw.githubusercontent.com/meituan-longcat/LongCat-Flash-Omni/main/figures/android_app_qrcode.jpg width="200px">

For iOS, you can download it by searching "LongCat" at App Store or QR code. Currently, only the Chinese App Store is supported.

<img src=https://raw.githubusercontent.com/meituan-longcat/LongCat-Flash-Omni/main/figures/ios_app_qrcode.jpg width="200px">

The model weights are released under the MIT License.

Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.

See the LICENSE file for the full license text.

This model has not been specifically designed or comprehensively evaluated for every possible downstream application.

Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios. It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.

Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.

We kindly encourage citation of our work if you find it useful.

@misc{
    title={LongCat-Flash-Omni Technical Report}, 
    author={Meituan LongCat Team}, 
    year={2025}, 
    url={https://github.com/meituan-longcat/LongCat-Flash-Omni}, 
}

Please contact us at longcat-team@meituan.com or join our WeChat Group if you have any questions.

<img src=https://raw.githubusercontent.com/meituan-longcat/LongCat-Flash-Omni/main/figures/wechat_qrcode.jpeg width="200px">