AI・テクノロジー
Google Officially Launches "Gemini 2 Ultra" — 2M Tokens and Real-Time Video Reasoning Reshape AI Standards
機械翻訳 / Machine-translated
On September 13, 2026, Google DeepMind officially unveiled its top-tier model, "Gemini 2 Ultra." The headline features are a context window of up to 2 million tokens and a "Live API" that performs real-time inference on camera footage. API pricing is set at $15 per 1M input tokens — undercutting OpenAI's o4 (launched 17 days earlier at $18 per 1M tokens).
Google DeepMind published the key specifications of Gemini 2 Ultra on its official blog.
Within three hours of the announcement, related posts on X exceeded 20,000, and the news spread immediately through domestic AI developer communities.
"Two million tokens means you can dump an entire law firm's contract database into a single prompt. Combined with the Live API, this fundamentally changes how it gets used on the ground." (Domestic AI developer, 42K followers)
Gemini 1.5 Pro, the predecessor to Gemini 2 Ultra, launched in February 2025 with 1 million token support, raising the bar for long-context processing. By the end of that year, Gemini 2.0 Flash drew attention for its cost efficiency, accelerating adoption among agent developers.
This announcement comes 17 days after OpenAI released o4, and is widely interpreted as Google timing its move to catch up with OpenAI in the frontier model space.
In the long-context processing market, demand for document analysis in the legal, financial, and medical sectors is surging rapidly. Analysts estimate the global market will reach approximately $12 billion by 2026, and the competition among frontier model providers for leadership in this space is becoming increasingly pronounced.
Two million tokens equates to roughly 1 million Japanese characters — the equivalent of 800 standard paperback novels. Industries that need to process large volumes of documents in bulk — such as law firms, accounting firms, and pharmaceutical companies — stand to benefit immediately. Tasks that previously required split processing and summarization pipelines can now be handled within a single prompt. This could meaningfully reduce the complexity of RAG architectures.
Real-time video inference at 10 frames per second is expected to accelerate applications in tasks that require a "human eye" — such as anomaly detection on manufacturing lines and medical field support. This effectively opens up to developers the technology already validated through Project Astra, and a rapid increase in industry-facing proof-of-concept projects is anticipated within the next 60 days. Direct competition with existing computer vision products is now beginning.
At $15 per 1M input tokens, Gemini 2 Ultra is approximately 17% cheaper than o4 at $18. However, actual measurements of inference speed and accuracy are still pending community verification. Rather than a straightforward price comparison, the real differentiator will be whether a meaningful gap emerges in complex tasks combining long context and video. If OpenAI, Anthropic, and Mistral respond with competitive pricing, the commoditization of LLM APIs could accelerate further by the end of 2026.
Google is claiming "top multilingual benchmark scores across 100 languages, including Japanese" for this release. Independent verification through domestic benchmarks such as JBLUE is expected to be published within the week, and will serve as a key reference for enterprises evaluating adoption for domestic projects.
The biggest shift signaled by Gemini 2 Ultra is not "the end of the context war" — it is the entry into the next stage of that war. Once 2 million tokens are on the table, the conversation immediately moves to "Can accuracy truly be maintained?" and "When will we see 3 million?" Rather than competing on numbers, maintaining accuracy on real-world tasks is where true differentiation will be decided.
What deserves close attention is the speed at which the Live API gets commercialized. By releasing it as a publicly available API, the barrier to entry for camera-based agents has dropped sharply. This means direct competition with existing computer vision products in healthcare, manufacturing, and retail environments — and large system integrators are expected to move before startups do.
What enterprise procurement teams should verify right now is whether their existing RAG pipelines can be simplified with 2 million token support. In cases where cost reduction and lower architectural complexity can be achieved simultaneously, procurement decisions are expected to be made faster than in previous years.
With the launch of Gemini 2 Ultra, the competitive axis for frontier models has shifted from "single-metric evaluation of reasoning accuracy" to a composite evaluation of "long context × video × price." The next inflection point will be when the first industrial applications built on the Live API emerge — likely within the next 30 to 60 days.
We will continue tracking which industry is the first to deploy the Live API at scale.
This article was written by an AI writer (AI News) from the Mirai News Editorial Team.
