DeepSeek V4 Reportedly Outperforms ChatGPT and Claude in Long-Context Coding—AI Race Heats Up

Rumors swirl that DeepSeek's latest model has pulled ahead in the marathon of long-context programming tasks.

The AI coding landscape just got a major shakeup. Industry whispers suggest DeepSeek V4 now handles extended codebases and complex programming contexts more effectively than its established rivals—potentially changing how developers approach large-scale projects.

What This Means for Development

Long-context capability isn't just about processing more text—it's about maintaining coherence across thousands of lines of code, understanding intricate dependencies, and generating consistent solutions where other models might lose the thread. If verified, this advancement could streamline everything from legacy system migrations to enterprise-scale software architecture.

The Benchmark Question

Every AI breakthrough comes with the same caveat: show us the numbers. The tech community now waits for independent verification through standardized coding benchmarks—the only currency that matters in this increasingly crowded market. Until then, it's all speculation wrapped in impressive demos.

A Shifting Competitive Field

This rumored leap forward demonstrates how quickly the AI landscape can change. Established leaders face constant pressure from agile competitors willing to push technical boundaries—and occasionally deliver on those promises. The real test comes when developers put these tools through their daily workflows.

One thing's certain: the AI coding assistant space just got more interesting. Whether this translates to actual developer productivity gains—or just another round of venture capital fundraising—remains to be seen. After all, in tech, sometimes the most impressive breakthroughs are measured in valuation jumps rather than actual utility.

Developers express deep anticipation for the DeepSeek V4 release

The Chinese company has not publicly disclosed any information about the imminent release or confirmed the rumors as of the time of writing. Developers across different social networks have expressed deep anticipation for the release. Yuchen Jin, an AI developer and co-founder of Hyperbolic Labs, wrote on X that “DeepSeek V4 is rumored to drop soon, with stronger coding than Claude and GPT.”

Subreddit r/DeepSeek also heated up, with one user explaining that their obsession with DeepSeek’s imminent V4 model was not normal. The user said that they frequently “check news, possible rumors, and I even go to read the Docs on the DS website to look for any changes or signs that indicate an update.”

DeepSeek’s previous releases have had a significant impact on global markets. The Chinese AI start-up released its R1 reasoning model in January 2025, leading to a trillion-dollar sell-off. The release matched OpenAI’s 01 model on math and reasoning benchmarks, despite costing significantly less than the US AI startup spent on its 01 model. 

The Chinese company reportedly spent only $6 million on the model release. Meanwhile, global competitors spend nearly 70 times more for the same output. Its V3 model also logged a 90.2% score on the MATH-500 benchmark, compared to Claude’s 78.3%. DeepSeek’s more recent V3 upgrade (V3.2 Speciale) further improved its productivity.

Its V4 model’s selling point has evolved from the V3’s emphasis on pure reasoning, formal proofs, and logical math. The new release is expected to be a hybrid model that combines both reasoning and non-reasoning tasks. The model aims to capture the developer market by filling an existing gap that demands high accuracy and long-context code generation.

Claude Opus 4.5 currently claims dominance in the SWE benchmark, achieving an accuracy of 80.9%. The V4 needs to beat this to overturn Claude Opus 4.5. Based on previous successes, the incoming model may surpass this threshold and claim dominance in the benchmark.

DeepSeek pioneers mHC for training LLMs

DeepSeek’s success has left many in profound professional disbelief. How could such a small company achieve such milestones? The secret could be deeply entrenched in its research paper published on January 1. The company identified a new training method that allows developers to easily scale large language models. Liang Wenfeng, founder and CEO of DeepSeek, wrote in the research that the company is using Manifold-Constrained Hyper-Connections (mHC) to train its AI models. 

The executive proposed using mHC to address the issues encountered when developers train large language models. According to Wenfeng, mHC is an upgrade of Hyper-Connections (HC), a framework that other AI developers use to train their large language models. He explained that HC and other traditional AI architectures force all data through a single, narrow channel. At the same time, mHC widens that pathway into multiple channels, facilitating the transfer of data and information without causing training collapse. 

Lian Jye Su, chief analyst at Omdia, commended CEO Wenfeng for publishing their research. Su emphasized that DeepSeek’s decision to publish its training methods dictates renewed confidence in the Chinese AI sector. DeepSeek has dominated the developing world. Microsoft published a report on Thursday, showing that DeepSeek commands 89% of China’s AI market and has been gaining momentum in developing countries.

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