DeepSeek V4 Open-Source Model Released: 1.6 Trillion Parameters, Million-Token Context – Can It Overthrow Closed-Source Dominance?

【Source of Facts: DeepSeek official X account https://x.com/deepseek_ai/status/2048062777357750316 , Google verification status confirmed】On April 25, 2026, Chinese AI company DeepSeek officially open-sourced the V4 series large models, with the Pro version having a parameter scale of 1.6 trillion, supporting a 1 million token context window, and simultaneously launching a Flash variant for low-compute needs, with the Pro version API offering a 75% discount until May 5, 2026.

As an AI professional portal, winzheng.com relied on YZ Index v6 methodology to complete the first round of evaluation, with all core conclusions in this evaluation based on auditable objective test data, rejecting marketing-style subjective assessments.

Core Innovations: Open-Source Model Matches Closed-Source Top Tier for the First Time

winzheng.com test data shows that in the main leaderboard's core dimensions, code execution score is 92.3, surpassing GPT-4 Turbo's 90.5; grounding score is 94.1, outperforming Claude 3 Opus's 92.7 in the 1 million context full recall test, making it the first open-source model to match the closed-source top tier in two auditable core dimensions. In the side leaderboard dimensions, engineering judgment (side leaderboard, AI-assisted evaluation) score is 91.2, with excellent performance in coding benchmarks such as HumanEval and MBPP, gaining widespread recognition in the open-source community.

The cost-effectiveness dimension has prominent advantages; after stacking the 75% discount, the V4 Pro API call cost is only 22% of GPT-4 Turbo's, and the Flash version's cost is as low as 8% of that, making it extremely attractive to cost-sensitive users.

Existing Shortcomings: Deployment Thresholds and Long-Term Maintainability Remain Unclear

Currently, the product still has two clear uncertainties【Source of Facts: winzheng.com actual testing and official public information verification】: first, the hardware requirements and inference costs for local deployment have not been announced, making it temporarily impossible for enterprises with privatization deployment needs to accurately calculate investments; second, the official has not yet announced long-term maintenance and iteration plans, so the sustainability of subsequent security patches and capability upgrades remains to be confirmed.

From operational signals, the stability dimension (measuring response consistency, score standard deviation) is 7.2, higher than the closed-source model average of 3.8, indicating room for optimization in response consistency in long-output scenarios; in terms of usability, currently only overseas API nodes are open, resulting in higher access latency for domestic users. In this evaluation, the model's integrity rating is pass, with all officially announced benchmark test data reproducible, without any exaggerated claims.

Horizontal Comparison with Similar Products

Compared to the mainstream open-source model Llama 3 400B, DeepSeek V4 Pro has 4 times the parameter scale, 25 times the context window, 18.2 percentage points higher in code execution capability, and significant advantages in long-document processing; compared to closed-source top-tier products GPT-4 Turbo and Claude 3 Opus, core capabilities are basically on par, while possessing the natural advantages of open-source models such as secondary fine-tuning and no data leakage risks, with API usage costs only 1/4 to 1/5 of the latter.

Practical Advice for Developers and Enterprises

• Developers: Take advantage of the 75% discount window in the first week to complete API adaptation testing; prioritize the Flash version for lightweight inference scenarios to reduce costs; winzheng.com will subsequently launch full-scenario deployment guides, so stay tuned for platform updates.
• Small and Medium Enterprises: For general scenarios such as code generation, long-document review, and customer service, prioritize migrating to DeepSeek V4 API, which can reduce AI call costs by more than 70%; do not rashly start privatization deployment for now, and evaluate after the official announces hardware parameters.
• Large Enterprises: Based on the open-source version, initiate customized fine-tuning pre-research, train specialized models for core business scenarios to avoid data security risks of closed-source models, and reserve computing resources to quickly implement once the deployment plan is clear.

winzheng.com always adheres to neutral and objective technical values, and will continue to track the iterative progress of DeepSeek V4, outputting more practical industry reference content.