How low will the OpenAI GPT 5.5 input token price get in 2026?

Currently, it’s difficult to directly compare prediction market odds to traditional analyst forecasts regarding the OpenAI GPT-5.5 token price. However, the collective wisdom of traders in this market offers a unique perspective, reflecting real-money conviction rather than stated opinions. If analysts widely predict a certain price range, we would expect to see that reflected in the odds offered here. Discrepancies between this market and analyst expectations could signal areas where the market anticipates factors not yet fully considered by conventional analysis.

On Kalshi, this market is priced using a continuous double auction. Traders submit bids (the price they’re willing to buy) and asks (the price they’re willing to sell) for contracts representing the outcome of the market. The current price reflects the probability that traders assign to the event occurring. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The market price adjusts dynamically based on supply and demand, meaning that increased buying pressure will raise the price, while increased selling pressure will lower it. This creates a real-time assessment of the likelihood of the OpenAI GPT-5.5 token price falling to the specified level.

This market resolves around Jan 1, 2027, with the outcome confirmed once the average input token price for OpenAI’s GPT-5.5 model in 2026 is verifiable from credible public reporting. The resolution will be based on publicly available data regarding the cost of using the model, as reported by OpenAI or other trusted sources. Traders will be able to see the final price and whether the market settled in their favor. This provides a clear and objective determination of the outcome for all participants.

Several factors could significantly impact this market. Announcements from OpenAI regarding the pricing structure of GPT-5.5, or any subsequent models, would be a major catalyst. Technological advancements that reduce the computational cost of running the model could lead to lower token prices. Broader economic conditions, such as increased competition in the AI space or changes in cloud computing costs, could also play a role. Finally, significant changes in the demand for AI services could shift the price, influencing trading activity in this market.