DeepSeek cuts V4 Pro pricing by 75% to $0.003625 per million input tokens

DeepSeek has permanently reduced pricing for its flagship V4 Pro model by 75%, according to an update on the company's website. Input tokens now cost $0.003625 per million, down from $0.0145, while output tokens dropped to $0.87 per million from $3.48.

The price reduction makes permanent a promotional discount that was originally scheduled to end on May 31, 2026. DeepSeek released the V4 Pro and V4 Flash models in April 2026, claiming they would usher in an "era of cost-effective 1M context length."

Pricing comparison

The new pricing structure positions DeepSeek V4 Pro significantly below competing models:

• DeepSeek V4 Pro: $0.003625 input / $0.87 output per 1M tokens
• Previous DeepSeek V4 Pro pricing: $0.0145 input / $3.48 output per 1M tokens

The company describes its positioning as the "cost-effective" choice for AI agents, a strategy that could deliver substantial savings for enterprise accounts and power users processing millions of tokens daily.

Market context

DeepSeek's aggressive pricing comes amid increasing competition in the large language model market. The Chinese startup is now positioned as a lower-cost alternative to OpenAI's GPT-5 and Google's recently released Gemini 3.5 Flash, though specific pricing comparisons for those models were not provided.

The pricing strategy follows previous tensions with competitors. Anthropic has accused DeepSeek of "distillation attacks" — a practice where one company's model improperly learns from another's more capable system. The permanent price cuts may intensify these competitive dynamics.

What this means

DeepSeek's permanent 75% price reduction represents a significant escalation in AI model pricing competition. For enterprise users running high-volume workloads, the cost difference could be substantial — a workload requiring 1 billion tokens per day would now cost approximately $3,625 for input tokens instead of $14,500. However, buyers should evaluate whether DeepSeek's performance matches their requirements, as raw pricing doesn't account for differences in model capabilities, accuracy, or output quality. The move also raises questions about the sustainability of such pricing and whether competitors will respond with their own cuts.