Google Shipped Three New Gemini Models This Week, and Still No Sign of the One Everyone Actually Wants

On July 21, Google DeepMind released Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber, and Google VP Josh Woodward personally confirmed the launch on X. One detail set the tone for everything that followed: the drop landed one day before Alphabet’s earnings call, and the model the community has been asking for since June, Gemini 3.5 Pro, still wasn’t in it.

What actually shipped

Gemini 3.6 Flash is Google’s new “workhorse” model, cutting token usage on complex coding tasks and reducing average time-per-task by roughly 50% versus its predecessor, according to independent testing from Artificial Analysis, which also clocked its output speed at 304 tokens/second. Gemini 3.5 Flash-Lite is the volume play, built for high-throughput agent workflows at 350 output tokens per second. And Gemini 3.5 Flash Cyber is the interesting outlier: a model tuned specifically to find and patch software vulnerabilities, running inside Google’s CodeMender agent and positioned directly against Anthropic’s Mythos, a cybersecurity-focused model. Flash Cyber isn’t public, it’s limited to governments and trusted partners, which tells you Google sees this less as a product launch and more as a controlled pilot.

Both Reddit and X converged fast on the actual headline, though: Google shipped three models and the one people want still didn’t show up. Per Ars Technica, Google says 3.5 Pro will arrive “as soon as it’s ready,” with earlier reporting suggesting the delay traces back to the model falling short on coding benchmarks against competitors.

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The elephant in the room

The reaction on r/singularity (289 upvotes, 122 comments) read less like excitement about new models and more like a running commentary on the Pro delay. u/bleachjt noted flatly it’s “interesting that it’s cheaper than Gemini 3.5 Flash,” but u/Hereitisguys9888 cut closer to the real anxiety: “At this point, gemini 3.5 pro has to be a breakthrough. Even if it’s on fable level, its way too late considering open source is reaching fable level.” u/duluoz1 connected it directly to the earnings timing: “This is why they’re delaying. If they release their new model and it’s behind the open weight latest models, they’ll be impact to their share price.”

One X post put a number on just how far behind Google’s frontier positioning has slipped: as of this launch, Google doesn’t have a single model in the top 10 of the Artificial Analysis rankings, prompting the poster to argue Google is “conceding the frontier race to OpenAI and Anthropic” on raw capability while competing on cost instead. Chinese social media reaction was less diplomatic about it: one widely shared post described netizens mocking Gemini’s trajectory as “Alzheimer’s-like,” with commenters quipping the launch backfired into “least promising, least likely to deliver.”

r/Bard, the community that actually lives inside these releases daily, split between practical reviews and open frustration. One post was blunt: “AI community: Give us Gemini 3.5 Pro. Google DeepMind:”, title trailing off into implied silence, with one comment riffing on Mistral’s Le Chat as the punchline: “too bad even v4 cant beat the OG Le Chaton Fat xD.” Another was more direct: “Gemini 3.6 flash is out on web!! BUT where is the pro version 🥲🥲”

The genuinely strange part: Flash is now the flagship

A detailed YouTube breakdown from Universe of AI (18,988 views) pulled Google’s own benchmark numbers and found something Google didn’t exactly advertise: Gemini 3.6 Flash now outperforms Gemini 3.1 Pro across the board, software engineering (49% vs. 12%), machine learning engineering (63.9% vs. 42.6%), knowledge work (1421 vs. 965). “This flash model is now their strongest model, not even their pro model anymore,” the reviewer noted, calling it “concerning” that Google’s mid-tier model has quietly become its best one. Reddit caught the same tension from a different angle: one r/singularity thread pointed out that on Artificial Analysis’s own index, 3.6 Flash scores the same as 3.5 Flash despite the marketing, meaning the efficiency gains are real but the intelligence gains are thinner than the release implied. And even on cost, the win isn’t clean: one Reddit post flagged that GPT-5.6 Luna reportedly outperforms Gemini 3.6 Flash while costing 2.5x less.

Not everyone read it as decline. @iRakeshPurohit framed it plainly as a deliberate strategy shift: “Google’s latest Gemini launch isn’t about building the ‘smartest’ model. It’s about making AI economical at scale… This is an efficiency release rather than Google’s flagship moment.” One quieter but sharper observation came from @emadgnia: “Gemini 3.6 Flash and 3.5 Flash-Lite shipped with zero teaser post. That’s the real signal, model drops are becoming routine infra updates, not launch events. The hype cycle is normalizing faster than the models are improving.”

The other Gemini story this week: a chip that is the model

Running in parallel to the Flash launch, Google confirmed it’s building a chip with Gemini’s architecture etched directly into the silicon, internally called Frozen v2, targeting 6-10x more tokens per watt than current TPUs. u/Gaiden206 explained why that’s a genuinely different approach: “Most AI chips are general-purpose. You load a model onto them, and they run it. Google is reportedly trying something stranger: a chip that is the model, with Gemini’s blueprint etched into the hardware.” The tradeoff is obvious and the community caught it immediately, u/UnkarsThug noted it’s “less of a speedup… because it isn’t the model itself, just the architecture,” meaning Google would have to bake in a new chip generation every time it wants to upgrade the model. Reactions ranged from practical (“this will be a win for some customers, in two years”) to genuinely excited about downstream effects: “Imagine the implications this would have for robotics. Goodbye latency. Scarecrow gets a real brain.”

What’s next

Logan Kilpatrick, Google AI’s product lead, confirmed on X that Google has started pre-training Gemini 4, described as more ambitious than any of the company’s previous training runs. Polymarket traders are actively pricing the Pro question too: one market on the next Gemini Pro release timing moved up 6.4% this week, while a companion market on whether it debuts directly in the Arena leaderboard slipped 3%, traders clearly expect something soon, just not with full confidence about how it arrives.

On the developer side, the strongest signal isn’t a benchmark at all, it’s adoption. Gemini CLI, Google’s open-source terminal agent, sits at over 106,000 GitHub stars with 1,388 open issues, evidence that whatever the frontier-ranking debate looks like this week, a large number of developers are already building daily inside the Gemini ecosystem regardless.

Where this leaves things

Google didn’t lose this week so much as it declined to play the game everyone expected. Instead of a flagship swing at Claude and GPT, it shipped an efficiency release, teased a chip that rethinks the hardware layer entirely, and confirmed Gemini 4 is already in motion. Whether that reads as quiet confidence or as stalling depends entirely on what Gemini 3.5 Pro looks like when it finally lands, and right now, that’s the only question anyone in these threads actually cares about.

By Anthony Batt — 20+ years building software and digital media products at scale. Podcasting host at Future-Proof Podcast by CO/AI.