AI Model Costs Plummet by 300x as OpenAI's GPT-5.1 Delivers Enhanced Efficiency

A recent social media post by "Chris" has highlighted a staggering 300-fold reduction in the per-task cost of artificial intelligence, citing OpenAI's newly released GPT-5.1 "Thinking High" model as significantly more economical than its predecessor, "o3-preview (Low)." This dramatic decrease in operational expense arrives just one year after previous models, signaling a rapid evolution in AI affordability and accessibility. The tweet emphasizes that despite the massive cost reduction, GPT-5.1 maintains comparable performance on the ARC-AGI-1 benchmark.

OpenAI officially launched its GPT-5.1 series around November 12-13, 2025, introducing two primary models: GPT-5.1 Instant and GPT-5.1 Thinking. The "Thinking High" reference in the tweet likely pertains to the advanced reasoning capabilities of the GPT-5.1 Thinking model, which dynamically adjusts its computational effort based on task complexity. This adaptive reasoning allows for faster processing of simpler queries while dedicating more resources to intricate problems, optimizing both speed and cost.

The predecessor, "o3," was an earlier OpenAI model that GPT-5, and subsequently GPT-5.1, replaced. While specific "300 times cheaper" figures comparing GPT-5.1 directly to "o3-preview" were not independently verified, OpenAI has consistently emphasized enhanced efficiency and lower token usage in its latest models. Reports from early testers like Balyasny Asset Management indicate GPT-5.1 uses approximately half the tokens of leading competitors for similar or better quality, and AI insurance BPO Pace noted agents running 50% faster.

The ARC-AGI-1 benchmark, mentioned in the tweet, is a critical tool for evaluating AI intelligence. Inspired by the Abstraction and Reasoning Corpus (ARC), it assesses a model's capacity for reasoning, learning, and generalization across diverse tasks that demand common sense, creativity, and problem-solving. GPT-5.1 has shown strong performance on various technical benchmarks, including AIME 2025 and Codeforces, underscoring its capability despite the significant cost improvements.

This substantial reduction in AI operational costs, as observed by Chris, suggests a future where advanced AI capabilities become more economically viable for widespread application. The rapid pace of innovation continues to drive down the barriers to entry for businesses and developers looking to integrate sophisticated AI into their operations, making powerful intelligence more accessible than ever before.