One of the quietest but most striking stories of summer 2026 is actually about a model that hasn't shipped: Gemini 3.5 Pro. Google CEO Sundar Pichai confidently told developers in May that the model would arrive in June. That deadline came and went — and as of mid-July, it's still nowhere to be seen.
Leaked reports point to a clear cause: Gemini 3.5 Pro fell short of Google's own expectations on coding performance during internal testing. This isn't a routine "needs a bit more polish" delay — the problem appears to run all the way down to the model's foundations.
In late June, Google reset and updated the model's training data, hoping to close the coding gap. The results were still disappointing. Some reports suggest the issue may be far more structural — that the model might need a full retrain starting from the pre-training phase, something described internally as a "structural problem."
Why Is This So Hard to Fix?
Reports point to several internal dynamics behind the delay:
- A fragmented organizational structure. Google's massive, sprawling corporate setup makes a fast, coordinated fix harder to pull off.
- Internal consolidation efforts. Google is in the process of merging multiple internal coding initiatives into a single, unified system — which may itself be adding to the short-term delay.
- Senior researcher departures. Reports indicate several key researchers have left the company, contributing to frustration within internal engineering teams.
Among Google engineers, there's growing concern that the company is losing its competitive edge against faster-moving rivals.
The Competition Didn't Wait
Gemini 3.5 Pro's delay landed right as rivals were moving at their fastest. That same week, OpenAI shipped GPT-5.6 and Meta shipped Muse Spark 1.1 — and Meta explicitly claimed its model beat Google's current Gemini release on some coding and reasoning benchmarks. A week later, Anthropic would set a new price-to-performance bar with Claude Opus 5.
Google has tried to fill the gap with smaller updates like Gemini 3.7 Flash, but enterprise users looking for a coding-focused flagship-tier alternative are still left waiting.
What Is Wall Street Saying?
The delay put real pressure on Alphabet's stock — some investors reportedly dropped the stock in July. But market opinion isn't unanimous: analysts at firms like Bank of America stayed bullish heading into the July 22 earnings report, pointing to cloud-segment growth and the value of Google's stake in Anthropic.
The real question is whether the delay is actually costing Google Cloud customers. So far there's been no mass exodus — partly because switching platforms is expensive, and partly because many enterprises already run multi-model architectures. But the effect shows up elsewhere: CIOs evaluating new AI platform commitments are getting noticeably more cautious about betting on Google.
There's also a case for not overreacting to the delay: Gemini's user base doubled in a year, reaching 900 million monthly users by May — a scale of distribution that a single delayed model release doesn't come close to erasing.
Google's Long-Term Play: Its Own Chip
Rather than dwelling on the near-term delay, Google CEO Sundar Pichai steered attention toward the future — talking up Gemini 4 and a shift to a monthly model-release cadence. One of the company's longer-term bets is "Frozen v2," a custom AI chip built specifically for Gemini, targeting server deployment by 2028 and promising 6 to 10 times the throughput per watt of Google's current TPUs.
What Happens Next?
As of mid-August, there's still no official date for Gemini 3.5 Pro. How Google handles this delay — a quick patch versus a ground-up retrain — remains one of the industry's most closely watched open questions. In a market moving this fast, every additional month of delay is getting more costly for Google.
