DeepSeek V4 is here: How it compares to ChatGPT, Claude, Gemini

DeepSeek’s real significance is not that it narrows the frontier-model gap; it is that it compresses pricing power across the entire model stack. If a credible, low-cost open model can handle a large share of coding and agentic workflows, the margin pool shifts away from model vendors toward the layers that own distribution, workflow integration, and proprietary data. That is structurally negative for closed-model monetization assumptions and positive for any platform that can swap model providers without customer-visible degradation.

The second-order effect is a faster commoditization cycle for inference, which should pressure cloud attach rates per task even if total token volumes keep rising. In the near term, this is bearish for premium AI API pricing and likely forces competitors into either price cuts or bundling, but it is bullish for compute demand at the application layer because cheaper tokens expand use cases and raise throughput. The winner set broadens to orchestration, evaluation, and enterprise software vendors that sit above the model layer and can arbitrage price/performance across providers.

The biggest market miss is that lower model prices are not automatically bearish for all AI infrastructure; they can be bearish for model gross margins while still bullish for aggregate GPU consumption if usage explodes. The true risk to that bull case is a sudden acceptance that open-source models are “good enough,” which would shift spending from frontier model subscriptions to self-hosted deployments over the next 6-18 months. That would matter most for vendors with the least differentiated distribution and the most exposed pricing power, while accelerating geopolitical bifurcation in enterprise AI procurement.