The Release: A New Benchmark for AI Reasoning
On April 30, 2026, DeepMind publicly released Gemini 2.5 Ultra, its flagship model, and with it, a new standard for evaluating artificial intelligence. The headline feature isn't a simple parameter count increase or a marginal speed boost. It's the first native implementation and published benchmark for full multimodal chain-of-reasoning across text, code, image, and video. This means the model doesn't just process different data types side-by-side; it weaves them into a single, continuous reasoning process.
The numbers tell a compelling story. Benchmarks published alongside the release show a 41% improvement on the "M3Exam" multimodal reasoning suite compared to its predecessor, Gemini 2.0 Ultra. The M3Exam is a brutal test, requiring models to solve complex problems that demand sequential steps of logic drawing from diagrams, text descriptions, and sometimes even short video clips. A 41% leap isn't an iteration; it's an architectural breakthrough made public.
What This Actually Means: From Cross-Modal to Coherent Thought
Technically, the advance here is moving from cross-modal understanding to multimodal problem-solving. Previous models could describe an image or answer a question about a video transcript. Gemini 2.5 Ultra's chain-of-thought represents something fundamentally different. Imagine giving it a research paper with complex graphs, a block of supporting code, and a textual hypothesis. The model can now, in a documented internal monologue:
1. Parse the graph, identifying trends and anomalies.
2. Read the hypothesis, understanding the claim being made.
3. Examine the code, checking its logic and whether it could generate the graph.
4. Sequence these steps to conclude if the evidence supports the hypothesis, or identify where the logical chain breaks.
This is the "chain"—a traceable, stepwise reasoning process that hops between modalities as naturally as a human researcher might. The strategic implication is massive. It shifts the competitive axis from "who has the most data" or "the biggest model" to "who can build the most coherent and reliable reasoning engine." DeepMind is betting that true intelligence is less about vast recall and more about robust, audit-able thought processes. By open-sourcing the API, they're inviting the world to pressure-test this hypothesis, turning every developer into a beta-tester for a new form of AI cognition.
The Near-Term Future: Six to Twelve Months Out
Based on this release, the trajectory for the next 6-12 months becomes sharply clearer. We are not heading toward marginally better chatbots. We are heading toward AI that can be entrusted with complex, multi-format workflows.
The Honest Assessment: Gaps and Guardrails
This is not artificial general intelligence. The chain-of-thought is a scaffold, not a guarantee of correct reasoning. It can make its errors more transparent, which is a win for debugging, but it does not eliminate them. The model's reasoning is still a learned statistical process, potentially brittle when faced with true novelty or adversarial inputs designed to break its logic chain.
Furthermore, this technology will force a reckoning with interpretability vs. opacity. A long, detailed chain-of-thought output may give a false sense of understanding its "thinking." In reality, we are seeing a highly sophisticated pattern, not accessing a model's true causal motivations. The field must develop new tools to audit these reasoning traces for hidden biases or logical sleights of hand.
The Provocation
If an AI's reasoning process across text, code, and video becomes more coherent, traceable, and useful than that of a hurried human expert, have we succeeded in creating a tool for thought—or have we quietly redefined the value of human expertise in the loop?
P.S. For those inspired to build the agentic systems that will harness this new reasoning power, the principles of orchestrating reliable, multi-step AI workflows are covered in depth in AI4ALL's practical course on Hermes Agent Automation.