# Why Google is chasing Jev into decision models > Source: > Published: 2026-11-11 23:53:54+00:00 As AI model cost and efficiency comes sharply into focus for enterprises, Google is taking the hint. On Wednesday, the tech giant released the [Gemini Decisions API](https://blog.google/innovation-and-ai/technology/developers-tools/gemini-decisions-api/), an endpoint for users to access Gemini-decision-1, a model built for rapid decision making, which creates another competitor to [TypeSafe's popular Jev](https://www.thedeepview.com/articles/jev-puts-frontier-ai-price-premium-under-pressure). The new Google model costs 10 cents per million input tokens, and output tokens are free, [matching OpenAI's price](https://www.thedeepview.com/articles/openai-s-jev-rival-bets-on-simplicity) for its own recently-released decision model. Gemini Decisions API is built to handle text, images, audio, video, and PDFs, as well as up to 1,000 independent questions with "bounded choices, such as things like tool selection, policy checks, model routing, and context grading. Then, instead of coming up with answers using tokens to generate text, the model grades different options and returns probabilities and confidence scores. - Like other decision models, Gemini Decisions API isn't built for open-ended questions. The model offers three decision choices: Yes or no, multiple choice with up to 255 options, and rubric-based scoring. - Google says that its system can return outputs in as little as 150 milliseconds, compared to Jev's response time of between 70 and 500 milliseconds and OpenAI's of between 100 milliseconds and three seconds. These decision models beat the response time of a traditional LLM by a mile, which can take up to 329 seconds to respond. - Google also claims its model beats out the TypeSafe and OpenAI offerings in decision-specific benchmarks, including for tasks like agent routing, reasoning, ranking, and multimodal understanding. Inside Google, the company said it's using Decisions API for things like dynamic model routing in Gemini Notebook, iterating on designs through rapid generation and editing, and adding a "safety layer" for MCP in search that detects adversarial behavior. Google's latest release adds to a growing movement towards more efficient and affordable AI as enterprises grow weary of the expensive token bills brought on by frontier models. Though many are looking into open-source, local models, and small models to fix this, decision models provide a way to drastically drive down the cost curve, especially as agents, which don't need outputs to be in lengthy text, make up an ever-increasing share of token spend. ## Our Deeper *View* While the decision model trend isn't necessarily favorable to the financials of frontier labs like Google and OpenAI, these companies' validation of decision models could signal that they are eager to keep pace with the changing tide. The fact that two of the biggest labs in the industry have jumped on this trend following Jev's explosion onto the market could indicate that decision models may be one of AI's next big trends. Though it's unlikely that these models will replace language models entirely, they certainly have the potential to upend the economics of AI, making it far more affordable to implement agents at larger scales for tasks that don't require language outputs. However, one thing to be wary of is the fact that these models aren't built for humans to understand their outputs. While that's exactly the beauty in them, as cutting out the friction of human understanding makes them far more efficient, that may also limit our ability to observe and control them.