Google releases three new Gemini models — but no 3.5 Pro
Google DeepMind has released three new Gemini models — 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber — but has again held back the long-awaited update to its flagship Gemini Pro model. The launch is notable largely for what was missing, since rivals OpenAI and Anthropic have been shipping new flagship models at a rapid pace in recent months, putting pressure on Google to keep up in the increasingly competitive AI race.
Gemini 3.6 Flash is billed as Google's "workhorse model", offering improved coding, knowledge work and multimodal performance while cutting token usage by up to 17%, making it cheaper than its predecessor. Gemini 3.5 Flash-Lite is the cheapest model in the line-up, while 3.5 Flash Cyber is tuned specifically for finding and fixing cybersecurity vulnerabilities and will only be available to governments and trusted partners under a limited pilot. Gemini Pro, last updated in February, has reportedly been delayed by internal performance issues, according to Bloomberg; Google product lead Logan Kilpatrick said 3.5 Pro is being tested with partners and should "land soon", adding that the company has also begun its most ambitious pre-training run yet for Gemini 4.
- Google launched Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber models
- Flagship Gemini Pro update still missing, delayed by performance issues
- Google says Gemini 4 pre-training has already begun
New here? Start with this
Google DeepMind, the artificial intelligence arm of Google, has been steadily updating its Gemini family of AI models, which power products such as chatbots, coding tools and search features. These models come in different sizes and specialisms, from lightweight versions built for speed and low cost to a top-tier "Pro" version intended to be the most capable and reliable option, typically used for the most demanding tasks.
The wider context is a fierce competition between the major AI companies, chiefly Google, OpenAI (maker of ChatGPT) and Anthropic (maker of Claude), each racing to release more capable models faster than the others. Being seen to fall behind on flagship model updates can affect a company's reputation with developers and businesses, who choose which AI systems to build on based on which appear most advanced.
Gemini Pro matters because it is Google's flagship model, the one meant to compete directly with the best offerings from rivals, and it has not been refreshed since February. Its continued absence, even as smaller companion models are released, has drawn attention because it raises questions about whether Google is keeping pace with competitors in an industry where perceived leadership shifts quickly.
Both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
A thoughtful defender of Google's approach would argue that shipping polished, cost-efficient updates like 3.6 Flash and a specialised security-focused model shows discipline rather than weakness, since rushing out a flagship Pro model with known performance issues risks reputational damage far worse than a delay. They would point to Kilpatrick's confirmation that 3.5 Pro is already in partner testing and that an ambitious Gemini 4 pre-training run is underway as evidence Google is playing a longer, more deliberate game focused on getting the flagship right rather than chasing headlines. On this view, incremental, well-tested releases serve users and enterprise customers better than a pressured launch driven by competitor timelines.
The case against
A reasonable critic would counter that repeatedly delaying the flagship Pro model, now overdue since February, suggests Google is genuinely struggling to keep pace with OpenAI and Anthropic, who have continued shipping frontier models on a much faster cadence. They would argue that releasing three secondary or niche models, including one restricted to governments and select partners, cannot substitute for the flagship product that developers, enterprises and everyday users most want to evaluate Google's competitive standing by. From this perspective, the gap between announcement and delivery matters because it affects real decisions being made now about which AI provider to build on, and Google's caution risks being read as a symptom of deeper technical or organisational difficulty rather than prudent restraint.