SenseNova

SenseNova (Chinese: 日日新) is the family of foundation models developed by the Hong Kong-listed AI company SenseTime. SenseTime introduced the name on 10 April 2023, when it presented SenseNova as a "foundation model set" covering natural language processing, content generation, automated data annotation and custom model training, with the SenseChat large language model as its conversational front end.^[1][2] Chairman and CEO Xu Li said the name implied "constant renewal, daily renewal, and further renewal".^[1] The brand now spans SenseTime's proprietary flagship generations (4.0 through V6.5, and the 6.x agent models), the products built on them such as the Office Raccoon assistant, and a set of open-weight models (Chinese embedding models from 2023, and a much larger group of research models from late 2025 onward) published under the "sensenova" organization on Hugging Face and the OpenSenseNova organization on GitHub.^[3][4]

Since 2025 the line has moved away from adapter-based multimodal models toward what SenseTime calls native multimodal architectures: NEO (December 2025) and NEO-unify (March 2026), the basis of the open SenseNova U1 and U1.5 models, which handle image understanding and image generation in one network without a separate vision encoder or VAE.^[5][6][7]

History

2023: launch of SenseNova and SenseChat

SenseTime unveiled SenseNova at a Tech Day event; the company later dated the launch to 10 April 2023, and the PR Newswire release carries an 11 April dateline.^[1][2] The release described SenseChat as an LLM "with hundreds of billions of parameters" trained with a focus on Chinese text, and demonstrated a programming assistant, a health consultation assistant and a PDF reading assistant built on it, along with text-to-image creation and 2D/3D digital human generation.^[1] SenseTime presented the models as running on SenseCore, its AI infrastructure, which it said comprised 27,000 GPUs delivering 5,000 petaflops.^[1]

2024: SenseNova 4.0, 5.0, 5.5 and 5o

SenseNova 4.0 was announced on 6 February 2024.^[8] Its general LLM, SenseChat V4, supported 4k, 32k and 128k token context windows. SenseTime said its overall performance "closely matches that of GPT-4" on the OpenCompass evaluation platform and reported a HumanEval one-pass rate of 75.6% against 74.4% for GPT-4.^[2] The same release added SenseChat-Vision V4 (a 30-billion-parameter multimodal model), SenseChat-Medical V4, the code-interpreter model SenseChat-DataAnalysis V4, the 10-billion-parameter text-to-image model SenseMirage V4, a SenseChat Function Call & Assistants API, and the first version of Office Raccoon, a no-code data-analysis tool. SenseTime said SenseNova had attracted more than 3,000 enterprise users since its launch.^[2]

SenseNova 5.0 followed on 23 April 2024 at a Tech Day event in Shanghai. SenseTime said it had been trained on more than 10TB of tokens, including a large amount of synthetic data, used a Mixture of Experts design, and reached an effective context window of about 200,000 tokens at inference. The company claimed first place on the MMBench multimodal benchmark and launched edge-side models at the same event. Partners named in the release included Kingsoft Office (WPS 365), Haitong Securities, and Xiaomi, whose SU7 car uses SenseTime large-model technology in its smart cabin.^[9]

At the World Artificial Intelligence Conference in Shanghai in July 2024, SenseTime presented SenseNova 5.5 and SenseNova 5o.^[10] The company claimed 5.5 was 30% better overall than 5.0, with mathematical reasoning up 31.5%, English up 53.8% and prompt following up 26.8%. It described 5o as China's first real-time multimodal model, taking audio, text, image and video input in conversation, and compared it with GPT-4o. An edge model, SenseChat Lite-5.5, and the Vimi avatar video generator were shown at the same event.^[10]

2025: V6, V6.5 and the NEO architecture

SenseNova V6 was launched at Tech Day events held in several cities, including Shanghai and Shenzhen, in April 2025 (the PR Newswire release is dated 12 April).^[11] SenseTime described it as a native MoE multimodal general foundation model with more than 600 billion parameters, trained on over 200B of high-quality multimodal long chain-of-thought data, with reasoning chains up to 64K tokens. The company said it was the first model in China to understand and reason over videos about 10 minutes long, benchmarked its reasoning against GPT-4.5 and Gemini 2.0 Pro, and claimed the lowest training and inference costs in the industry. The release also covered V6 Reasoner and SenseNova V6 Omni, a lightweight real-time interactive model that SenseTime called China's first commercialized full-modality real-time interactive model.^[11]

SenseNova V6.5 was launched at a SenseTime-organized large-model forum during WAIC 2025 in July 2025.^[12][5] Its main addition was interleaved image-text "multimodal thought chains", which SenseTime described as the first commercial-grade image-text interleaved reasoning in China. The company said a streamlined visual encoder and a deeper, narrower backbone raised pretraining throughput by over 20%, reinforcement-learning efficiency by 40% and inference throughput by over 35%, for a threefold cost-performance gain over V6.0. It claimed text and multimodal reasoning ahead of Gemini 2.5 Pro and Claude 4 Sonnet.^[12]

On 2 December 2025 SenseTime released and open-sourced NEO, a native vision-language architecture co-developed with S-Lab of Nanyang Technological University, which it called the cornerstone of the next-generation SenseNova multimodal models.^[5] The accompanying paper, "From Pixels to Words: Towards Native Vision-Language Primitives at Scale" (arXiv 2510.14979, accepted at ICLR 2026), describes NEO as a family of native vision-language models that learn visual perception from scratch inside "a dense and monolithic model", trained on 390 million image-text examples, instead of attaching a separate vision module to a language model.^[13] SenseTime said that data volume was about one tenth of what modular models of equivalent performance need, and that NEO matched models such as Qwen2-VL and InternVL3 on several visual understanding tasks.^[5]

2026: NEO-unify, U1, U1.5 and the 6.x agent models

In March 2026 SenseTime and Nanyang Technological University described NEO-unify, an architecture that removes both the vision encoder and the VAE and uses a native Mixture-of-Transformers (MoT) backbone so that understanding and generation run in one model.^[6] SenseNova U1, the first models built on it, had weights posted on Hugging Face on 27 April 2026, and SenseTime announced the release on 30 April.^[3][7] SenseTime called this open release the U1 Lite series; it said the U1 Lite models' general image generation was comparable to Qwen-Image 2.0 Pro and Seedream 4.5 and that it could produce "continuous image-text" (interleaved) output in a single call.^[7] A distilled U1 Fast variant dedicated to infographic generation runs on the free SenseNova-Studio playground.^[3]

On 8 May 2026 SenseTime released SenseNova 6.7 Flash-Lite, a lightweight multimodal agent model for office work such as data analysis, slide generation, research reports and infographics. It is offered through an API rather than as open weights. SenseTime said it cut token use by about 60% compared with text-only agents in tasks such as information search. The launch came with a limited-time free SenseNova Token Plan and the open-source SenseNova-Skills office skill set for agent frameworks such as OpenClaw and Hermes Agent.^[14][15]

On 18 July 2026 SenseTime announced SenseNova U1 Pro, an image-creation model that uses an internal interleaved image-text chain of thought and outputs up to native 8K resolution.^[16] A production version became available through the Raccoon app and the SenseNova API in September 2026.^[17]

In August 2026 the company published SenseNova 6.8 Flash Lite Preview through the Token Plan, an early preview aimed at long-horizon "delegated" tasks in which a lead agent dispatches more than ten specialist sub-agents. As of late October 2026 its README still says SenseNova 6.8 Flash Lite and a larger SenseNova 6.8 Flash are "coming soon",^[18] although SenseTime's website FAQ, accessed 29 October 2026, names SenseNova 6.8 Flash Lite as a featured model and one of the models supported by the Token Plan.^[39] SenseTime's first-half 2026 results listed the 2026 model releases as the NEO-unify architecture, SenseNova U1, U1.5 and U1.5-Lite, SenseNova Vision, 6.8-Flash-Lite, U1-Pro, the spatial intelligence model SenseNova SI-8B and the Kairos 3.1 world model co-developed with ACE Robotics.^[19]

SenseNova U1.5 Lite went on the Token Plan API on 25 August 2026 with support for output up to 4096 x 4096.^[20] Open SenseNova-U1.5-8B-MoT weights, preceded by a preview checkpoint, were published on Hugging Face, and the technical report (arXiv 2609.11929) was submitted on 10 September 2026.^[21][22] In October 2026 the sensenova Hugging Face organization added an embodied-agent model, SenseNova-RoboRSI-27B-Preview, and SenseNova 6.8 Code2Video, a code-driven video creation model.^[23][24]

Flagship and API releases

Open-weight models

SenseTime's open releases sit on the Hugging Face organization "sensenova" and the GitHub organization OpenSenseNova, and are mostly research models from SenseTime Research, often with co-authors at Nanyang Technological University or Tsinghua University.^[4][25][26] The table lists the main lines; dates are the earliest dated record found (arXiv submission, model-card news entry, or Hugging Face repository creation), which can precede general availability by a few days.

SenseNova-U1 and U1.5

The U1 model card describes "8B-MoT" as about 8 billion understanding parameters plus about 8 billion generation parameters. SenseNova-U1-8B-MoT uses a dense backbone and SenseNova-U1-A3B-MoT a mixture-of-experts backbone; the technical report says they are built on dense 8B and 30B-A3B understanding baselines respectively.^[3][33] Released checkpoints include SFT and final versions (the final models add a round of text-to-image reinforcement learning), an 8-step distilled preview, LoRAs and infographic-tuned variants. The card lists limits: a 32K-token context for visual understanding, unstable fine human details, occasional text-rendering errors, and interleaved generation that is still experimental.^[3] The authors also show preliminary vision-language-action and world model uses.^[33]

U1.5 keeps the 8B-MoT, encoder-free and VAE-free design. Its report describes spatially coherent patch reconstruction, training at native resolutions up to 4K, separate expert models for aesthetics, bilingual text rendering, infographics and image editing, and merging of those experts through multi-expert on-policy distillation. The authors say they will release training code for supervised fine-tuning, reinforcement learning and on-policy distillation.^[22]

SenseNova-SI

SenseNova-SI is a series of models fine-tuned for spatial reasoning on SenseNova-SI-8M, a curated set of eight million samples organized by a taxonomy of spatial capabilities. The base models include InternVL3, Qwen3-VL, Qwen2.5-VL and BAGEL.^[31][25] According to the model zoo on the card, later InternVL3-8B releases scaled the spatial training data to 10 million (1.2), 14 million (1.3) and 29 million (1.4) samples, and version 1.5 added 1.5 million samples on top of 1.4.^[25] The GitHub repository lists the work as a CVPR 2026 paper.^[4]

SenseNova-MARS, InteractiveOmni and SenseNova-Vision

SenseNova-MARS (Multimodal Agentic Reasoning and Search) trains a vision-language model to interleave image search, text search and image cropping with its reasoning. It uses a reinforcement-learning algorithm the authors call Batch-Normalized Group Sequence Policy Optimization (BN-GSPO), and the paper introduces the HR-MMSearch benchmark of high-resolution, search-driven questions. The 8B model is fine-tuned from Qwen3-VL-8B-Instruct.^[26][36] InteractiveOmni combines a vision encoder, audio encoder, LLM and speech decoder in a single model; its authors report that the 4B version keeps 97% of the 8B version's performance at half the size.^[30] SenseNova-Vision expresses detection, OCR, keypoints, segmentation, depth, surface normals, point maps and camera pose estimation as outputs of a unified generation model, with no task-specific heads.^[34]

SenseNova-RoboRSI and Code2Video

SenseNova-RoboRSI-27B-Preview is an open-weight 27B model for embodied AI agents trained through what SenseTime calls joint model and harness recursive self-improvement on validated interaction experience; the card covers benchmark reproduction on LIBERO-Pro and RoboCasa365, with a technical report still to come.^[23] SenseNova 6.8 Code2Video builds and renders short videos by writing executable code and is designed to run for more than 10 hours and 2,000 turns inside an agent harness such as Claude Code, with a 512K-token context. The release includes weights, a production skill and example trajectories. It is covered in more detail at SenseNova 6.8 Code2Video.^[24]

Architecture: NEO and NEO-unify

Most multimodal LLMs, including earlier SenseNova generations, connect a pretrained vision encoder to a language model through a projector or adapter. SenseTime's NEO work drops that modular design. According to SenseTime, NEO uses a patch embedding layer that maps pixels directly to tokens, a native 3D rotary position embedding (Native-RoPE) with separate frequency allocation for visual and text dimensions, and attention that is autoregressive for text tokens and bidirectional for visual tokens within one framework.^[5][13]

NEO-unify extends this to generation. It removes the variational autoencoder used by most image generators as well as the vision encoder, uses a near-lossless visual interface for both input and output, and splits work between understanding and generation branches inside a Mixture-of-Transformers backbone. In a SenseTime experiment, a 2B NEO-unify model reached 31.56 PSNR and 0.85 SSIM on MS COCO 2017 image reconstruction after 90,000 pretraining steps, against 32.65 and 0.91 for the Flux VAE.^[6] SenseNova U1 and U1.5 are the production models built on this design.^[7][22]

Products and access

• SenseChat: the conversational LLM introduced with SenseNova in 2023 and versioned with each generation (for example SenseChat V4 and SenseChat Lite-5.5).^[1][2][10]
• Office Raccoon: launched in February 2024 as a natural-language data-analysis tool built on SenseNova.^[2] With V6.5 it was upgraded into an office agent with education and finance editions, and SenseTime said in July 2025 that the Raccoon product suite had more than 10 million users.^[12] SenseTime's other figures count users differently: its first-half 2025 results said the Raccoon series had surpassed 3 million users, and the December 2025 Raccoon 3.0 announcement cited more than 3 million registered users serving more than 15 million individuals.^[40][41] In its first-half 2026 results, SenseTime named Lenovo, Ping An Technology, China's three major telecom operators, JD.com and Kylin Software as Raccoon customers.^[19]
• API platform and Token Plan: SenseNova models are offered through the platform.sensenova.cn developer platform. The Token Plan, launched with 6.7 Flash-Lite in May 2026, initially gave developers 1,500 calls every five hours in the first month.^[14] SenseTime said in August 2026 that average daily Token Plan usage had passed 500 billion tokens.^[20]
• SenseNova-Skills and SenseNova-Studio: an open-source skill library that packages SenseNova models for agent frameworks, and a free browser playground for the U1 models.^[3][15]

Benchmark claims

The OpenCompass and SuperCLUE rows are third-party leaderboard results as relayed by SenseTime; all other figures are self-reported by SenseTime or the model's authors and have not been independently replicated.

On OpenCompass, SenseTime said V6.5 Pro outscored Gemini 2.5 Pro and GPT-5; on SuperCLUE it said V6.5 Pro was ahead of Doubao-seed-1.6-vision, ERNIE-5.0 and Qwen3 but behind leading international models.^[37][38] The MARS card says the 32B model surpassed Gemini-3-Pro and GPT-5.2 on its two search benchmarks, and the RoboRSI card ranks the model second to GPT-6 Astra on LIBERO-Pro among the models it compared.^[36][23]

Business context

SenseTime reports SenseNova-driven sales within its generative AI business. For the first half of 2026 it reported generative AI revenue of RMB2.33 billion, nearly 80% of total revenue of RMB2.91 billion, and its first IFRS net profit since listing (RMB620 million), which it attributed in part to fair-value gains on ecosystem investments.^[19] The same announcement said SenseNova U1 had been adapted on day zero to more than ten Chinese domestic chip platforms and that, less than four months after U1 was open-sourced, three related GitHub projects had collectively passed 11,000 stars.^[19] Company-level financials, sanctions history and corporate details are covered in the SenseTime article.

Continue reading

• SenseTimeSenseTime (Chinese: 商汤科技; pinyin: Shāngtāng Kējì) is a partly state-owned, publicly traded artificial intelligence company headquartered in Hong Kong that grew into China's largest AI software company by…Mentioned 47 times above
• MMultimodal ModelA multimodal model is a machine learning model, or a model-based system, that processes, relates, or produces information across more than one kind of data. Each kind is called a modality.Mentioned 6 times above
• SenseNova 6.8 Code2VideoSenseNova 6.8 Code2Video is an open-weight model from SenseTime for making short videos by writing and rendering code instead of generating pixels.Mentioned 5 times above
• SSpatial intelligenceSpatial intelligence is the ability of an AI system to perceive, understand, reason about, generate, and interact with three-dimensional space rather than just text or two-dimensional pixels.Mentioned twice above
• XXu LiXu Li (Chinese: 徐立; born in the 1980s) is a Chinese computer scientist and technology entrepreneur who co-founded SenseTime, one of China's largest artificial intelligence companies, and serves as its…Listed in See also
• AArtificial intelligence in ChinaArtificial intelligence in China is the research, development, deployment, and state governance of artificial intelligence within the People's Republic of China, the world's second largest national AI…Listed in See also

More to read

• Hugging Face
• GitHub
• Foundation Models
• Large Language Model
• GPT-6 Astra
• GPT-4

Topics:AI ModelsChinese AILarge Language ModelsMultimodal AI

References

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