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    Home»Tech»Alphabet shares fall on report its Gemini 3.5 Pro AI model is delayed
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    Alphabet shares fall on report its Gemini 3.5 Pro AI model is delayed

    franperez66q@protonmail.comBy franperez66q@protonmail.comJuly 16, 2026No Comments2 Mins Read
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    Alphabet shares sank 4% on Thursday following a report that the company has delayed releasing its flagship artificial intelligence model.

    The search giant’s Gemini 3.5 Pro AI model is months behind schedule due to the company’s efforts to improve its performance, according to Bloomberg, citing sources familiar with the matter. The model’s coding capabilities, in particular, were short of internal expectations and come at a time when rivals like OpenAI and Meta have recently debuted new AI models that outpace Google’s current offerings in generating software code, the report said.

    The company previously announced the Gemini 3.5 Pro AI model in May as part of the company’s annual Google I/O developer conference, saying at the time that it was being used internally, but wouldn’t be ready for a broader rollout until the following month.

    An Alphabet spokesperson told CNBC in an emailed statement that the company is “shipping quickly across a wide range of models while keeping them highly cost-effective for customers.”

    “We’re currently testing 3.5 Pro, an upgraded Flash model, and other models with partners, and we’re productively engaged with the U.S. government,” the spokesperson said.

    Code-generation has become one of the biggest use cases for AI model providers like Anthropic and OpenAI and Chinese AI labs like Z.ai that offer so-called open-weight variants that developers can access for free via the open-source ecosystem.

    Meta debuted last week its Muse Spark 1.1 AI model, which the company’s AI chief Alexandr Wang described as the social media giant’s “strongest model for agentic and coding work yet.”

    OpenAI last week released its GPT-5.6 Sol AI model, which CEO Sam Altman said is 54% more token efficient on agentic coding tasks, underscoring how AI labs are pitching their respective AI coding models as being cost-effective relative to their performance.

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