DeepSeek V4 targets mid-February launch with coding capabilities that could challenge OpenAI and Anthropic

Reviewed byNidhi Govil

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Chinese AI startup DeepSeek is reportedly preparing to release its V4 model around mid-February, targeting the Lunar New Year. Internal tests suggest the next-generation AI model could outperform both Anthropic's Claude and OpenAI's GPT series in coding tasks, particularly when handling extremely long code prompts. The move could disrupt Silicon Valley's dominance in the enterprise developer market.

DeepSeek V4 Targets Mid-February Release With Advanced Coding Capabilities

Chinese AI firm DeepSeek is preparing to launch its next-generation AI model V4 around mid-February, potentially coinciding with the Lunar New Year on February 17, according to a report from The Information

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. People with direct knowledge of the project claim the AI model focused on coding could outperform competitors including Anthropic's Claude and OpenAI's GPT series in internal benchmarks

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. While no benchmark data has been publicly shared and DeepSeek hasn't confirmed the rumors, the developer community is already preparing for what could be another disruption from the Hangzhou-based startup.

Source: ET

Source: ET

Internal Tests Suggest DeepSeek V4 Could Challenge Silicon Valley Giants

Internal tests by DeepSeek employees suggest V4 could deliver coding capabilities that surpass current market leaders, particularly when handling extremely long code prompts

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. This represents a strategic shift for the company, which previously emphasized pure reasoning with its R1 model released in January 2025. DeepSeek V4 is designed as a hybrid model capable of both reasoning and non-reasoning tasks, directly targeting the enterprise developer market where high-accuracy code generation translates to revenue

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. The ability to process very long coding instructions could prove especially valuable for developers working on complex software projects

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Breakthrough Training Method Could Explain Performance Gains

The company's potential to achieve such results despite operating under U.S. export restrictions may lie in its Manifold-Constrained Hyper-Connections (mHC) training method, detailed in a January 1 research paper co-authored by founder Liang Wenfeng

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. This technique addresses how to expand a model's capacity without causing training instability by widening information pathways into multiple streams. Wei Sun, principal analyst for AI at Counterpoint Research, called mHC a "striking breakthrough" that shows DeepSeek can "bypass compute bottlenecks and unlock leaps in intelligence" even with limited access to advanced chips

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. This low-cost, high-performance alternative approach mirrors the strategy that made DeepSeek's R1 model a phenomenon.

Market Impact and the AI Arms Race Intensifies

DeepSeek's previous R1 release triggered a $1 trillion sell-off in global markets when it matched OpenAI's o1 model on benchmarks despite reportedly costing just $6 million to developβ€”roughly 68 times cheaper than competitors

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. The startup, backed by quantitative hedge fund High-Flyer Quant, has emerged as a key player in China's push to build its own AI ecosystem

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. To claim dominance in coding, V4 would need to beat Claude Opus 4.5, which currently holds the SWE-bench Verified record at 80.9%

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. For investors, the impending V4 release adds volatility to the AI arms race, as markets reassess hardware dependency and market valuations across Western AI companies

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Developer Enthusiasm Meets Privacy Concerns and Skepticism

The developer community isn't waiting for official confirmation, with Reddit's r/DeepSeek and r/LocalLLaMA heating up as users stockpile API credits

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. Lian Jye Su, chief analyst at Omdia, noted that DeepSeek's willingness to publish its methods signals "newfound confidence in the Chinese AI industry," with the company's open-source approach making it popular among developers who see it as embodying what OpenAI used to be

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. However, privacy concerns have plagued the company, with some governments banning DeepSeek's native app

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. Critics also argue the company's benchmarks don't reflect real-world complexity, with some developers complaining about wasted compute on simple tasks

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. Still, if V4 delivers on its coding promises, enterprise adoption in Western markets could follow the model's widespread use across Asia.

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