Google Confirms Upcoming Release of Gemini 3.5: Internal Performance “Extremely Exciting,” Competitive Landscape May Shift

On April 25, 2026, Google Cloud CEO Thomas Kurian officially confirmed in a recent in-depth interview that the next-generation large language model—Gemini 3.5—is about to be released, describing its internal benchmark results as “extremely exciting.” The news quickly ignited discussion across AI developer circles, with many in the industry viewing it as a strong counterstrike to competitors like OpenAI, potentially reshaping the current AI race.

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I. Technical Foundations and Innovations Behind Gemini 3.5

For years, Google’s Gemini series has relied on its self-developed TPU hardware and Google Cloud platform to build a unique “full-stack” advantage. According to Kurian, Google’s eighth-generation TPUs are now divided into two categories: 8T, dedicated to training, and 8i, focused on inference. The latter uses an air-cooling design that adapts to various data center environments, effectively controlling inference costs. The 8T training chips support up to 2PB of memory and a low-latency Optical Torus network, greatly enhancing model training efficiency. This massive foundational architecture powers Gemini 3.5’s efficient performance on multimodal tasks.

Compared to OpenAI and Anthropic, who rely mainly on NVIDIA GPUs, Google’s fully in-house infrastructure not only grants more bargaining power but also greater flexibility in R&D funding—providing strong support for the “Gettysburg-scale” campaign toward AGI development.

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II. A Stunningly Powerful New Model

According to multiple insiders, Gemini 3.5 has exceeded expectations across several key test scenarios. Specifically:

• Character Recognition and Historical Document Restoration:
  Professor Mark Humphries from Wilfrid Laurier University (Canada) tested an unreleased Gemini 3.5 version using Google AI Studio. Recognition errors for 18th-century handwritten ledgers dropped to 0.56% per character and 1.22% per word, showing a 50–70% improvement, reaching expert-level human accuracy. This means it not only “understands” the handwriting but can also reason based on the economic and cultural context of the time—for example, interpreting “145” as “14 pounds and 5 ounces.”

• Breakthroughs in Coding and Multimedia Generation:
  Multiple internal developers report notable improvements in code completion and error diagnosis, along with upgraded contextual understanding. The image generation tool Nano Banana has also received a major update, now supporting finer and more diverse rendering styles.

• The Dawn of Intelligent Agents:
  Kurian emphasized that the next phase of AI competition will revolve around intelligent agents. Gemini 3.5 integrates an improved Virgo architecture, helping AI become more human-like—capable of naturally operating computers and handling complex enterprise data multitasking.

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III. Strategic Significance and Industry Response

Since ChatGPT’s explosive debut in late 2022, Google was at one point caught in a “red alert” due to its slow response. Now, the launch of Gemini 3.5 is seen as a crucial turning point to reclaim leadership. As Kurian put it, “Having self-managed compute that’s in high demand is far better than depending on external sources.”

With complex relationships between Google and competitors such as Anthropic—who are both clients and rivals—the company is showing signs of pursuing cooperation and symbiosis. Whether to prioritize internal use during compute shortages is to be decided by its executive committee, but the overall stance remains open and balanced.

Industry analysts generally believe Gemini 3.5 will not only catch up to but may even surpass OpenAI’s GPT-5, leveraging its multimodal and reasoning advantages to seize more ground in professional and enterprise applications.

For developers, there’s growing anticipation for richer API offerings from Google and its cloud services—particularly in code generation and multimodal interaction support—opening new opportunities for innovation.

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IV. Current Status of OpenAI Hub Compatibility

As China’s leading AI API aggregation platform, OpenAI Hub has already integrated Gemini 3.5, allowing developers to call Google and other mainstream models directly through a unified key—compatible with OpenAI’s format and accessible domestically. Example:

from openai_hub import OpenAIHub

client = OpenAIHub(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    model="gemini-3.5",
    messages=[{"role": "user", "content": "Please help me write Python code to scrape news headlines."}]
)
print(response.choices[0].message.content)

This seamless integration offers great convenience, enabling enterprises and individual developers to quickly experience the power of the new generation of large models.

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V. Outlook for the Future

The debut of Gemini 3.5 shows that Google’s progress isn’t confined to hardware—it has also achieved a substantial leap in software intelligence. Later versions are rumored to integrate a trillion-parameter-level “Mythos” model, using a decoupled-service inference architecture that maintains efficiency even at massive scale. This could have profound implications for AI democratization and complex task enablement.

Moreover, as Agent technology matures, future AI systems will increasingly serve as cross-platform intelligent assistants, capable of replacing humans in more cognitive and operational work—further accelerating industry transformation.

For developers in China, platforms like OpenAI Hub that provide access to new models from the Gemini family promise broader technological and commercial potential—well worth long-term attention.

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Reference Links

• Google Cloud CEO Deep Interview: TPU Supremacy and Gemini’s New Model
• ITHome: Gemini 3.0 to Be Released Before Year-End
• Zhihu: Gemini 3.0’s Performance Was Stunning

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Original article by the OpenAI Hub tech editorial team, dedicated to bringing developers the most cutting-edge and practical insights into the AI industry.