--- title: "DeepSeek does not have to be the savior" type: "News" locale: "en" url: "https://longbridge.com/en/news/275982474.md" description: "DeepSeek released the R1 model before the Spring Festival, causing shock in Silicon Valley and unease on Wall Street, marking a new research path for Chinese large model manufacturers. As companies like Kimi and KNOWLEDGE ATLAS also release flagship models, market expectations for DeepSeek have increased. DeepSeek is testing a new long-text model, which could be the highly anticipated DeepSeek-V4. Meanwhile, market attention is shifting towards Agentic AI, pursuing a new paradigm of autonomous decision-making and task planning" datetime: "2026-02-14T14:39:42.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/275982474.md) - [en](https://longbridge.com/en/news/275982474.md) - [zh-HK](https://longbridge.com/zh-HK/news/275982474.md) --- > Supported Languages: [简体中文](https://longbridge.com/zh-CN/news/275982474.md) | [繁體中文](https://longbridge.com/zh-HK/news/275982474.md) # DeepSeek does not have to be the savior Last year, DeepSeek released the R1 model on the eve of the Spring Festival, shocking Silicon Valley and unsettling Wall Street. It reaffirmed the path for Chinese large model manufacturers to focus on research and training, and it kicked off a year of rapid advancement for China's open-source model camp. The Spring Festival has become a prelude to the new year. Recently, Kimi, Knowledge Atlas, MiniMax, and Doubao have all rushed to release their flagship models before the Spring Festival. The outside world can't help but speculate that they are all worried that if they are late, they will fall behind in brand image and market competition. Ranking of the "intelligence" level of cutting-edge models (According to ArtificialAnalysis, the strongest model in the U.S. comes from Anthropic, while in China it comes from Knowledge Atlas) Now, the pressure is on DeepSeek. The market expects it to continue being the hero of the Spring Festival and to take on the role of the "savior" of the Chinese AI ecosystem. How should it respond to the market's expectations of it, or does it even have to respond? DeepSeek is indeed brewing something. A brand new long-text model structure test is underway, supporting a maximum of 1 million tokens in context. Could this be the DeepSeek-V4 that the market has been waiting for? In fact, the market has had such expectations in May, August, October, and December of last year. Ultimately, DeepSeek delivered DeepSeek-R1-0528, DeepSeek-V3.1, DeepSeek-V3.2-Exp, and DeepSeek-V3.2. During this period, DeepSeek also explored directions such as UE8M0 FP8, DSA, contextual optical compression, mHC, and Engram. One of their core ideas is "sparsity," making "expertise," "precision," "attention," and "memory" more sparse. People believe that in the upcoming V4, the shadows of these improved technologies will continue to be found. However, the market spotlight has shifted to AI Agents, more specifically, Agentic AI. The latter is beginning to pursue a new paradigm of autonomous decision-making, long-term task planning, inter-agent interaction, and end-to-end execution. Anthropic claims that AI can already write 90% of the code, and the next step is to complete 90% of end-to-end software engineering (SWE). The booming OpenClaw makes people believe how powerful and dangerous Agentic applications will become once they gain sufficient permissions. The flagship large model of 2026 will primarily be a native Agentic large model. In the U.S., Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.3-Codex have been launched one after another, especially with OpenAI introducing the Codex-Spark at 1000 tokens per second, pushing coding competition to a fever pitch In China, Kimi-K2.5 from the Dark Side of the Moon, GLM-5 from Zhipu, MiniMax-M2.5 from Xiyu Technology, and today’s Doubao-Seed-2.0 from ByteDance are all promoting their intelligent agent capabilities. Among them, Kimi-K2.5 introduces Agent Swarm technology and proposes Parallel Agent Reinforcement Learning (PARL), achieving higher accuracy and shorter time; GLM-5 aligns its programming capabilities with Claude Opus 4.5 and introduces an asynchronous agent reinforcement learning algorithm, allowing the model to continuously learn from long-term interactions and autonomously complete Agentic long-term planning and execution with minimal human intervention. MiniMax-M2.5 claims to be the first cutting-edge model that can be used indefinitely without considering usage costs, stating that “USD 10,000 can keep 4 Agents working continuously for a year.” DeepSeek-V3.1 has long announced its move towards the Agent era, but how will it define the impending Agentic era? Can it establish a new benchmark in practical experience through reasoning efficiency, tool integration, memory mechanisms, and extreme economy? Perhaps, DeepSeek may not need a separate “R series.” R symbolizes reasoning and cognition, corresponding to OpenAI's o series models; while the Agentic era emphasizes execution and engineering, requiring a benchmark against OpenAI's Codex. DeepSeek already possesses Coder and Math series models, where coding and mathematical proof are both “meta-capabilities” leading to AGI (Artificial General Intelligence). Together, they form a self-improvement system for the model, accelerating recursive evolution. The market also expects DeepSeek to continue validating the potential of the domestic computing power ecosystem's synergy. For a long time, its exploration has focused on how to maximize training and reasoning efficiency through architectural innovation under limited resources. At the end of last year, DeepSeek-V3.2 achieved significant end-to-end acceleration in long-context scenarios with its new architecture DSA; earlier this year, Engram's conditional memory is expected to “become an indispensable foundational modeling paradigm in the next generation of sparse large models.” OpenAI's Codex-Spark proves that response speed is crucial and key to creating value. It runs on the Cerebras wafer-scale engine, which is precisely what the domestic reasoning ecosystem lacks. Can DeepSeek use “algorithms” to exchange for “computing power” and bridge this hardware-level gap? Moreover, starting from the algorithmic aspect and streamlining steps can not only enhance response speed, especially in scenarios that require high-speed and precise responses, but also alleviate the pressure on context. Previously, Chinese open-source models were often criticized for their “lengthy thinking” being unconstrained, consuming too many tokens, which would gradually erode cost advantages. DeepSeek has previously mentioned that it will focus on enhancing the intelligent density of the model's reasoning chain in future work to improve efficiency More important than reasoning is training, and pre-training remains the starting point for post-training. NVIDIA's Blackwell architecture is becoming the mainstay of training in the U.S. AI infrastructure, while Google's TPUv7 will also play a key role in the training of Gemini 4. Even if the H200 can be deployed domestically as soon as possible, the source of computing power for large model training in China is still in the Hopper era. Currently, the performance narrative of domestic AI chip manufacturers mainly revolves around the Hopper architecture, and there is still insufficient practical data support for stability and overall efficiency performance in large-scale cluster scenarios. DeepSeek acknowledges in its paper that due to insufficient training computing power, DeepSeek-V3.2 still lags behind leading proprietary closed-source models in terms of the breadth of world knowledge coverage. The team plans to bridge this knowledge gap in subsequent iterations by expanding the scale of pre-training computing power. There is no doubt that when DeepSeek-V4 is released, domestic AI chips will be fully adapted from Day 0; however, the market is more eager for its pre-training to be based on domestic AI chips, once again rewriting the market's pricing narrative around NVIDIA. People appreciate DeepSeek's refined research, and the market also looks forward to the native multimodal DeepSeek-V4. Gemini 3 already natively supports text, image, audio, and video inputs, while Kimi-2.5 emphasizes joint optimization of text and vision. To continue benchmarking against Google and OpenAI, it seems that DeepSeek must take action. However, technological innovation must stand on verifiable physical boundaries, not emotional boundaries. The entire ecosystem of chips, energy, networks, and algorithms determines the upper limit of computing power. And computing power is limiting the further catch-up of China's open-source models, as has already been demonstrated in pre-training and post-training. Any "algorithmic optimism" can only optimize within this boundary for a specific period. Moreover, AGI is a systemic engineering challenge that far exceeds the parameter scale or version updates of a single large model. DeepSeek's mission is to explore AGI. Simply focusing on large models cannot achieve AGI, especially if only language large models are pursued, as their limitations are becoming increasingly apparent. The AGI form that is closer to reality now is one that integrates knowledge and action, possessing cognitive abilities, execution capabilities, long-term constraints, and real-world feedback loops, among others. The Claude large model is often collectively outperformed by China's open-source models, but its revenue is growing at ten times a year, and breakthroughs in programming as a general function area have opened a new path toward AGI. Perhaps the true long-term expectation is to allow DeepSeek to continue its deep exploration, rather than concentrating all market anxieties and desires on a single name at a particular moment. 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