What Is Going On With Google Gemini? Why Gemini 3.5 Pro Missed Its Moment
At Google I/O 2026, Google said Gemini 3.5 Pro was already being used internally and that it expected to begin rolling it out the following month.
That meant June.
June passed. July passed. We entered August without a broadly available Gemini 3.5 Pro.
Google’s language has also changed. In May, the company said the model was coming the following month. By late July, Google said Gemini 3.5 Pro was still being tested with partners and would become broadly available “as soon as it’s ready.” In the same announcement, Google confirmed that it had already started what it called its most ambitious pretraining run yet for Gemini 4.
That creates an obvious question:
What is going on at Google?
This is not a small AI startup struggling to find enough GPUs. It is Google.
Google has DeepMind, custom TPUs, enormous data centers, billions of users, Search, Android, Chrome, YouTube, Workspace, Google Cloud and some of the most experienced artificial intelligence researchers in the world.
Yet in the most important race in technology, Google increasingly looks like the company reacting to the frontier rather than defining it.
My prediction
Here is my personal prediction:
I believe there is a 99% chance that Google will not launch Gemini 3.5 Pro in the form originally presented at Google I/O 2026.
Google will either move directly toward Gemini 4 Pro or release Gemini 3.5 Pro as a short-lived transitional model while positioning Gemini 4 as its real competitor to GPT-5.6 Sol and Claude Fable 5.
The reason is simple. Anthropic has already moved forward with Fable 5, Mythos 5 and Opus 5. OpenAI has moved forward with GPT-5.6 Sol. These models have raised the frontier beyond the target Gemini 3.5 Pro was probably designed to reach.
Google may now need to make Gemini 4 significantly more compute-intensive and capable just to become competitive again.
The bad news for Google is that its competitors will not wait. By the time Gemini 4 arrives, Anthropic could answer with Fable 5.1, Fable 6 or another frontier model. OpenAI could begin moving toward GPT-6.
I do not know what is happening inside Google. But from the outside, Google is no longer competing effectively with Anthropic and OpenAI at the flagship-model frontier.
Google is too slow. That is what we have seen so far in 2026.
This is only my personal gut feeling based on public information and my experience using leading AI models. I have no inside information, and this should not be considered investment advice.
Google did not merely tease Gemini 3.5 Pro
There is an important difference between a rumored product and a delayed product.
Gemini 3.5 Pro was not invented by technology journalists, leakers or social media users. Google discussed it during its own I/O event.
Google said the model was already being used internally and that it looked forward to rolling it out the following month. That was a clear public expectation, even though Google did not provide an exact launch day.
By early August, the official Gemini model page still described Gemini 3.5 Pro as “coming soon.” Google’s July update said it remained in partner testing and would launch broadly once it was ready.
The change in language matters.
In May:
It is coming next month.
In July:
It will become broadly available when it is ready.
That is not a normal continuation of the original schedule. It is a delay, even if Google does not explicitly use that word.
And when a frontier model gets delayed, time works differently than it does for an ordinary software feature.
A two-month delay in a calendar application might be inconvenient.
A two-month delay in frontier AI can mean that the competitive target has changed completely.
The frontier moved while Google waited
The biggest problem for Gemini 3.5 Pro is not simply that it missed June.
The problem is what happened during and after that delay.
Anthropic released Claude Fable 5 and Claude Mythos 5 in June. Fable 5 became broadly available, while Mythos 5 was offered through restricted access for specialized customers. Anthropic presented these models as major improvements in long-running autonomous work, coding, knowledge work, memory and scientific use cases.
Anthropic then released Claude Opus 5 on July 24. It positioned Opus 5 as approaching Fable-level intelligence at roughly half the price, with particularly strong results in coding and professional knowledge work.
OpenAI also moved.
GPT-5.6 reached general availability with Sol as the flagship model, supported by the less expensive Terra and Luna models. OpenAI presented GPT-5.6 Sol as its strongest option for coding, professional work, computer use, science and long-running agentic workflows.
We should always be cautious with benchmark claims published by the companies selling the models. Every laboratory chooses evaluations, configurations and comparisons that present its products favorably.
But the strategic picture is still clear.
While Google was preparing Gemini 3.5 Pro, the competitive field changed.
CompanyFrontier position in early August 2026GoogleGemini 3.1 Pro broadly available; Gemini 3.5 Pro still testing with partnersOpenAIGPT-5.6 Sol broadly availableAnthropicFable 5 broadly available; Opus 5 broadly available; Mythos 5 in restricted availabilityGoogle’s next moveGemini 4 pretraining underway, with no public release date
Google is not standing still. It released Gemini 3.5 Flash and later Gemini 3.6 Flash, alongside other specialized Gemini models.
But Flash is not the issue.
The missing product is Google’s new flagship Pro model.
Gemini 3.5 Pro may have been overtaken before launch
This is the central problem.
Gemini 3.5 Pro may still be a strong model. It may perform well in coding, reasoning, tool use and agentic workflows. It may even outperform some currently available competitors on individual benchmarks.
But that does not mean it will have the impact Google originally expected.
Frontier models are designed against a moving target.
When Google began training and evaluating Gemini 3.5 Pro, its engineers were likely comparing it with an earlier competitive landscape. The relevant targets may have included GPT-5.3, Claude Opus 4.8, Gemini 3.1 Pro and other models available or under development at the time.
By August, that landscape had changed.
Google would now be releasing Gemini 3.5 Pro into a market containing GPT-5.6 Sol, Fable 5, Opus 5 and Mythos 5.
A model that would have looked impressive in June can look transitional in August.
That does not require Gemini 3.5 Pro to be bad. It only requires its competitors to have improved faster than Google expected.
Why would Google delay Gemini 3.5 Pro?
Google has not provided a detailed public explanation.
The company has only said that the model is being tested with partners and will become broadly available when it is ready. Therefore, the following possibilities are analysis rather than confirmed information.
1. The model may not be strong enough
The simplest explanation is often the most credible.
Gemini 3.5 Pro may not have reached the performance level Google expected.
It might be better than Gemini 3.1 Pro while still failing to create enough distance from it. Or it might be competitive with older models but insufficient against the latest releases from OpenAI and Anthropic.
Google cannot easily launch a flagship model that immediately appears one generation behind.
A mediocre release would damage the Gemini brand more than a delay.
2. Its performance may require too much computation
A model can achieve impressive benchmark results while still being commercially difficult to deploy.
It may consume too many reasoning tokens, respond too slowly or require too much expensive inference capacity.
The strongest configuration might work well in controlled testing but be impractical for hundreds of millions of users.
Google does not only have to demonstrate Gemini 3.5 Pro. It has to operate it at Google scale.
That creates a more difficult optimization problem:
Intelligence
Response speed
Reliability
Token efficiency
Inference cost
Safety
Tool-use consistency
Capacity across consumer and enterprise products
A model that wins benchmarks but loses money on every intensive request is not necessarily ready for broad deployment.
3. Long-running agentic reliability may remain inconsistent
Google positioned Gemini 3.5 around complex, agentic workflows.
These are more difficult than producing a strong answer to a single prompt.
An agent must maintain a goal, use tools correctly, recover from mistakes, interpret changing information and continue working across many steps. Small errors compound.
A model that succeeds 90% of the time at each individual step can still fail frequently across a workflow containing dozens of decisions.
Gemini 3.5 Pro may be intelligent enough in isolated evaluations but insufficiently reliable for the long-running tasks Google wants it to perform.
4. Safety testing may be taking longer
More capable models create more demanding security and safety requirements.
Coding agents can inspect repositories, execute commands and interact with external systems. Models with stronger cybersecurity capabilities require additional controls. Models operating across Google products may also encounter sensitive personal, business and organizational information.
Google may have decided that the model needed more testing before broad deployment.
That would be responsible, but it would not change the competitive consequence of the delay.
5. Google may already see Gemini 4 as the real product
Google’s July announcement included a revealing detail.
While Gemini 3.5 Pro was still being tested, the company had already started pretraining Gemini 4. Google described it as its most ambitious pretraining run yet.
Model generations naturally overlap. Companies begin training future systems long before current models reach users.
However, publicly mentioning Gemini 4 while Gemini 3.5 Pro remains unavailable changes how people interpret the roadmap.
It makes Gemini 3.5 Pro look less like Google’s next frontier and more like a bridge toward the model that really matters.
Why Gemini 4 is now Google’s real competitive model
Regardless of whether Gemini 3.5 Pro launches, Gemini 4 is becoming the more important story.
There are two possible outcomes.
Outcome one: Google releases Gemini 3.5 Pro
In this scenario, Google finishes testing and makes the model broadly available.
It may be competitive. It may be excellent. It could outperform expectations and restore some confidence in Google’s model development.
But unless it clearly exceeds GPT-5.6 Sol and Fable 5, it will still feel like a delayed transition model.
Users will immediately ask about Gemini 4.
Outcome two: Google effectively skips the 3.5 Pro moment
Google might still release a model carrying the Gemini 3.5 Pro name, but give it a limited period as the flagship.
Alternatively, it could narrow its distribution, reposition it or move attention quickly toward Gemini 4.
In practical terms, that would mean Google missed an entire flagship window.
Either way, Gemini 4 is now the model that must prove Google can still lead at the frontier.
Google has almost every advantage imaginable
Google’s slow execution is especially difficult to understand because the company has advantages that OpenAI and Anthropic spent years trying to build.
Google has its own AI chips
Google has been developing Tensor Processing Units for approximately a decade.
At I/O 2026, Google said its eighth-generation TPU architecture included separate chips optimized for training and inference. It also said its training infrastructure could scale across more than one million TPUs globally, with TPU 8t offering nearly three times the raw computing power of the previous generation.
This is an enormous strategic advantage.
Google does not depend entirely on another company’s accelerator roadmap. It can coordinate chip design, networking, software, model architecture, training and inference.
Few companies in the world possess that full stack.
Google has enormous financial resources
Alphabet reported almost $119.8 billion in revenue for the second quarter of 2026.
It spent approximately $18.2 billion on research and development during the quarter and almost $44.9 billion on property and equipment. Not all of that spending went directly to Gemini, but Alphabet explicitly said it was scaling AI infrastructure and global computing capacity.
This is not a company being prevented from competing because it cannot raise enough capital.
Google already has distribution
The Gemini application had approximately 950 million monthly active users by the second quarter of 2026, according to Alphabet. Google also said Gemini models were processing 22 billion API tokens per minute.
Google can distribute a model through:
Search
Android
Chrome
Gmail
Google Docs
Google Drive
YouTube
Google Cloud
Google Workspace
The Gemini application
OpenAI and Anthropic have to negotiate distribution agreements and convince users to adopt new applications.
Google already owns the surfaces.
Google has DeepMind
Google DeepMind is not merely another product team.
It has produced foundational work across reinforcement learning, protein structure prediction, robotics, world models, scientific reasoning and general artificial intelligence research. Its history includes AlphaGo, AlphaZero, AlphaFold, Genie and the Gemini family.
Google has research depth, infrastructure, capital and distribution.
That is why the failure to deliver Gemini 3.5 Pro on schedule is so difficult to excuse.
So why does Google keep failing to convert its advantages into speed?
This may be the most important question in the article.
Google does not appear to have an intelligence problem.
It appears to have an execution problem.
Google is optimizing for too many objectives
Anthropic can focus heavily on developing Claude and turning it into the strongest possible model for coding and professional work.
OpenAI has expanded into many products, but ChatGPT and its model family still sit at the center of the company.
Google has a more complicated structure.
A new Gemini model may affect Search, advertising, Workspace, Cloud, Android, consumer subscriptions, enterprise contracts, safety policies and infrastructure demand.
The model is not simply a product. It is a component that can reshape several major businesses.
That creates more stakeholders, more testing, more coordination and more opportunities for delay.
Google has more existing revenue to protect
A startup can disrupt an established market without worrying about damaging a mature business.
Google cannot.
A more capable Gemini could change how users interact with Search. That could affect how results are presented, how publishers receive traffic and how advertisements are displayed.
Google has to build the future while protecting one of the most profitable business models ever created.
That tension may encourage caution precisely when the market rewards speed.
Size can become friction
Google’s scale is an advantage in infrastructure and distribution.
It can also be a disadvantage in decision-making.
Large organizations often have:
More management layers
More internal dependencies
More approval processes
More product teams
More legal review
More launch requirements
More reputational risk
The larger the launch surface, the harder it becomes to move quickly.
OpenAI and Anthropic are no longer small companies, but they remain much more concentrated around a single strategic priority.
For Google, Gemini is one critical initiative inside a global technology empire.
For Anthropic, Claude is the company.
Research excellence is not the same as product execution
Google has repeatedly demonstrated exceptional research.
But frontier leadership is not awarded for publishing influential papers or inventing the underlying architecture.
It is awarded to the company that turns research into the best usable model and gets it into customers’ hands at the right time.
Being early in research and late in product delivery is still being late.
Google is successful in AI, but that is not the same as leading the frontier
It would be inaccurate to claim that Gemini is a commercial failure.
The Gemini application has hundreds of millions of users. Google Cloud is growing rapidly. Gemini is integrated throughout Google’s ecosystem, and the company has enormous enterprise adoption. Alphabet said nearly 90% of Fortune 100 companies were using Gemini Enterprise by the second quarter of 2026.
Google can build an extremely successful AI business without having the single strongest flagship model at every moment.
But commercial scale and frontier leadership are different questions.
Google may lead in distribution.
It may lead in AI infrastructure.
It may lead in the number of people exposed to generative AI through existing products.
It may lead in multimodal research, scientific AI, video generation or other specialized categories.
But in the specific competition for the strongest broadly available flagship language model, Google does not currently appear to be setting the pace.
OpenAI and Anthropic announce new performance targets.
Google responds.
They ship another generation.
Google’s expected model remains in testing.
That is what it means to lose control of the frontier narrative.
The competition will not wait for Gemini 4
Even a successful Gemini 4 launch would not end the problem.
Artificial intelligence development does not pause while one company completes a training run.
Anthropic could release a Fable 5 update, a sixth-generation model or a broader version of Mythos.
OpenAI could improve GPT-5.6, introduce new reasoning configurations or begin the transition toward GPT-6.
Chinese laboratories and open-weight model developers will continue improving performance and reducing costs.
Gemini 4 does not only need to beat the models available when its training began.
It needs to compete with the models available when it launches.
This is why speed matters so much.
A frontier model can be technically successful and strategically late.
What Google needs to do next
Google does not need another broad announcement about how much AI is being integrated into its products.
It needs to deliver a flagship model that clearly belongs at the front of the industry.
That means Gemini 4 must do more than produce incremental benchmark improvements.
It needs to demonstrate leadership in the areas where frontier models are increasingly judged:
Long-running software development
Reliable tool use
Agentic execution
Complex professional work
Computer interaction
Scientific reasoning
Efficiency at high reasoning levels
Consistency across extended tasks
It also needs to arrive before competitors move the target again.
Google has enough users.
It has enough distribution.
It has enough researchers.
It has enough capital.
It has enough computing infrastructure.
The missing element is execution at the speed of the frontier.
Final verdict
Gemini 3.5 Pro may still launch.
Google continues to describe it as a model being tested with partners, and there has been no official cancellation. It would therefore be wrong to report its cancellation as fact.
But I believe the original Gemini 3.5 Pro moment is already gone.
The model was supposed to follow Gemini 3.5 Flash in June. Instead, OpenAI and Anthropic moved the frontier while Google continued testing.
If Gemini 3.5 Pro launches now, it risks looking like a transitional release.
If Google skips it or quickly moves past it, that confirms the company was unable to turn its announced roadmap into a competitive product on time.
Either way, Gemini 4 has become Google’s real test.
The question is no longer whether Google has enough research talent, computing power, money or distribution.
It clearly does.
The question is why a company with nearly every possible advantage continues to move more slowly than competitors with fewer resources.
From the outside, the conclusion is increasingly difficult to avoid:
Google is not currently competing effectively with OpenAI and Anthropic at the flagship-model frontier.
Google is too slow.
And unless Gemini 4 changes that pattern, the company may continue owning much of the AI infrastructure and distribution while allowing someone else to define what the frontier actually is.
Frequently Asked Questions
Did Google promise to release Gemini 3.5 Pro in June 2026?
At Google I/O in May 2026, Google said Gemini 3.5 Pro was already being used internally and that it expected to begin rolling it out the following month. That created an expected June release window, although Google did not provide a specific date.
Has Gemini 3.5 Pro been cancelled?
No cancellation has been announced. Google said in July that Gemini 3.5 Pro was being tested with partners and would become broadly available when ready.
Why has Gemini 3.5 Pro been delayed?
Google has not published a detailed explanation. Possible reasons include performance targets, inference costs, agentic reliability, safety testing and the rapidly changing competitive landscape. These are analytical possibilities, not confirmed internal reasons.
Is Google already developing Gemini 4?
Yes. Google confirmed in July 2026 that it had begun what it described as its most ambitious pretraining run yet for Gemini 4. Google has not announced a public launch date.
Is Google falling behind OpenAI and Anthropic?
Google remains extremely successful in AI infrastructure, distribution and enterprise adoption. However, in the narrower competition for the strongest broadly available flagship language model, OpenAI and Anthropic are currently shipping major frontier releases faster.
Could Gemini 3.5 Pro still be competitive?
Yes. A delayed model can still be powerful. However, it must now compete against newer releases such as GPT-5.6 Sol, Claude Fable 5 and Claude Opus 5 rather than the models available when its development began.
Will Google skip directly to Gemini 4?
Google has not said that it will. My personal prediction is that Gemini 3.5 Pro will either not launch in its originally intended form or will serve as a relatively short transitional release before Gemini 4 becomes Google’s main flagship.