DeepSeek V4 Pro: Cheap 1M-Token Context in Python

On June 11, 2026 the AI price war stopped being a marketing skirmish and became structural. DeepSeek made its 75% discount on V4 Pro permanent, dropping the model to $0.435 per million cache-miss input tokens, $0.87 per million output tokens, and a startling $0.003625 per million tokens on cached input. That cached rate is roughly 120x cheaper than a cache miss, and the model ships with a 1M-token context window and 384K max output.

Here is the part most write-ups bury: the cache is automatic. You do not call a special endpoint or set a flag. If the first chunk of your request matches a recent call, DeepSeek serves the overlapping prefix from its disk cache and bills it at the cheap rate. For long-context workloads where you ask many questions against the same big document, that single behavior is what turns a dollar of inference into a fraction of a cent.