DeepSeek slashes V4-Pro prices 75 percent, pushing the China AI price war into Western API turf

At the promotional rate, V4-Pro inference costs roughly one-thirtieth of comparable Western frontier models — and cache hits drop another 90 percent.

DeepSeek dropped a 75 percent promotional discount on its newly released V4-Pro flagship model on April 27, running through May 5. List price was already aggressive — $0.145 per million input tokens and $3.48 per million output tokens — and the discount takes input down to roughly $0.036 per million. The Hangzhou-based lab also cut input cache-hit pricing across its model family by 90 percent, making repeated-prompt workloads dramatically cheaper. Western frontier output prices remain in the $12 to $25 per million token range.

The mechanics behind the cut matter as much as the headline number. V4-Pro was released the previous week with a 1-million-token context window, top-tier coding benchmark scores, and a hybrid attention architecture that DeepSeek claims runs significantly more efficiently than comparable Western dense models. Combine that efficiency with a willingness to operate at near-zero margin, and DeepSeek can sustain pricing that OpenAI and Anthropic structurally can't match without subsidizing every query.

This is the second leg of the Chinese AI price war that started in mid-2024. The first wave (Qwen, GLM, Yi, MiniMax) drove domestic Chinese model prices down 80 to 95 percent and forced Baidu, Alibaba, and Tencent to follow. The new wave is aimed outward — at developers in the US, EU, and India who route their API traffic on price. With DeepSeek's open weights also self-hostable, the company is squeezing margin from both ends: cloud customers who'd otherwise pay OpenAI rates, and enterprise customers who'd otherwise license Anthropic.

Takeaway for learners: model pricing is now a strategic weapon, not a cost-recovery exercise. If you're choosing an API for a side project or a startup MVP, run the same prompts through DeepSeek and a Western frontier model and compare the actual quality delta against your specific task. For most non-frontier workloads — summarization, classification, structured extraction, code review — the gap is now smaller than the price difference, which means picking the expensive option needs an actual reason.