AI industry faces pricing volatility amid intense lab competition

Artificial intelligence companies are facing complex challenges regarding pricing models and token consumption. Because the amount of tokens required to process information varies based on prompt complexity and model selection, businesses struggle to forecast costs. Goldman Sachs projects that external token consumption could reach 120 quadrillion tokens monthly by 2030 as companies adopt agentic AI systems.

Simultaneously, major AI labs are engaging in intense price competition. According to Stanford’s AI Index 2025 Report, the cost of querying a model at the GPT-3.5 level dropped from $20 per million tokens in late 2022 to $0.07 by October 2024. This trend, referred to by Andreessen Horowitz as ‘LLMflation,’ involves a roughly 10-fold annual price decline for models of fixed capability.

Recent shifts show OpenAI, Google, and Anthropic all reducing or freezing prices within recent weeks. For instance, OpenAI significantly reduced prices for certain GPT-5.6 models, while Google has implemented price cuts for its Gemini Flash models. While input token costs are falling, output tokens remain significantly more expensive, typically running five to six times higher.