Gemini 3.5 Pro: countdown — 10 days before Google's deadline, 2 million tokens and Deep Think mode, the most anticipated model of the year (amidst a talent chaos)
🔎 10 days, a self-imposed deadline, and a draining team
On May 19, 2026, on the Google I/O stage, Sundar Pichai made a clear promise: Gemini 3.5 Pro would be released "next month." We are now on June 20. There are exactly 10 days left before this deadline becomes a missed commitment — or a strong signal.
At the same time, DeepMind is losing its most visible talents. Noam Shazeer (co-author of the Transformer architecture) and John Jumper (2024 Nobel Prize in Chemistry for AlphaFold) have left or are about to jump ship. The internal context has never been as tense for a flagship model release.
Yet, the specs of Gemini 3.5 Pro are aggressive enough to justify the wait: a 2 million token context window, a Deep Think reasoning mode inherited from Gemini 3.1 Deep Think, and pricing that could disrupt the market. It remains to be seen whether Google will hold the date.
The essentials
- Gemini 3.5 Pro is announced for June 2026, with an implicit deadline of June 30 set by Sundar Pichai's statement at I/O on May 19.
- The model promises a 2 million token context window — double that of Gemini 3.5 Flash and the largest of all frontier models in production in 2026.
- Deep Think mode (System 2 reasoning) is confirmed, filling the void left by the discontinuation of Claude Fable 5 according to wowhow.cloud.
- Pricing oscillates between two ranges depending on the sources: ~$1.50/$9 or ~$3.50/$10.50 per million tokens (input/output).
- The launch occurs in a context of a major talent exodus at DeepMind, which adds significant execution risk.
Recommended tools
| Tool | Main usage | Price (June 2026, check on site) | Ideal for |
|---|---|---|---|
| Google AI Studio | Testing and development with Gemini 3.5 Pro (preview) | Free (limited quota) | Rapid prototyping with 2M tokens |
| Vertex AI | Enterprise access to Gemini 3.5 Pro | ~$3.50/$10.50 per M tokens | Scalable enterprise production |
| Gemini CLI | Terminal interface for Gemini | Included with Google account | Developers, CLI workflows |
| Gemini in Workspace | Docs/Gmail/Sheets integration | Included with Workspace subscription | Non-developer pro users |
What is confirmed vs. what remains speculative
Separating facts from rumors is crucial with a model still in limited preview. Here is the state of knowledge as of June 20, 2026, cross-referencing official sources and leaks.
What is confirmed
The official Gemini 3.5 family page on DeepMind confirms the existence of the 3.5 lineup with Flash already in production. Sundar Pichai has publicly stated that Pro is "in internal use at Google" and will be released to the public the month after I/O, according to AIMLAPI.
The 2-million-token context window is mentioned in four independent sources and is double that of Flash. Deep Think mode is inherited from the 3.1 branch, whose official page documents the ARC-AGI-2 comparisons against GPT-5.2 Thinking and Claude Opus 4.6 Thinking.
What remains uncertain
The exact pricing is debated. TechFastForward reports pricing of $1.50/$9 per million tokens (input/output), which is the same level as Flash. But ZoomBangla iNews cites $3.50/$10.50 on Vertex AI. This discrepancy could reflect two tiers of access (AI Studio vs Vertex enterprise) or genuine uncertainty about the final pricing.
The codename "Snow Bunny", revealed by CometAPI, has not been officially confirmed by Google. And the Polymarket prediction market gives about a 70% probability to a release before June 30 — which means the market itself has doubts.
2 million tokens: why it's a real technical leap
2 million tokens is not a marketing number. It's the difference between "analyzing a large file" and "understanding an entire ecosystem."
What 2M tokens concretely enable
With 2 million tokens, you can ingest approximately 1,500 pages of dense text, an entire medium-sized code repository (200,000 to 300,000 lines), or complete medical records with follow-up history spanning several years.
According to TechFastForward, this window is the widest of any frontier model in production in 2026. The previous record was held by Gemini 3.5 Flash with 1 million — which means Google is doubling its own bar in a single release cycle.
The comparison with the competition
Z.AI's GLM-5.2 offers 1 million tokens in open-weights under an MIT license. Claude Opus 4.7 (Adaptive) hovers around 200K native tokens with limited contextual extension. OpenAI's GPT-5.5 remains on a similar window, with no announcement of a massive extension.
The jump from 1M to 2M is not linear. Attention becomes exponentially more computationally expensive as the window stretches. If Google has solved this problem at the scale of Pro while maintaining pricing close to Flash, it is a non-trivial engineering accomplishment.
Use cases that change category
Law firms can submit complete litigation files with associated case law. Bioinformatics teams can feed the model with entire genomic datasets. Refactoring teams can request an architecture analysis on a 250K-line monolith without having to artificially slice it up.
This is exactly the type of usage that Gemini 3.5 Flash was starting to make possible, but Pro promises to do it with superior depth of understanding thanks to Deep Think mode.
Deep Think mode: System 2 reasoning, but Google version
"Chain-of-thought" reasoning has become an industry standard. But not all reasoning is created equal.
The legacy of Gemini 3.1 Deep Think
Gemini 3.1 Deep Think, released in February 2026, introduced System 2 reasoning at Google — an approach where the model explicitly explores multiple solution paths before converging on an answer. The official DeepMind page documents comparisons on ARC-AGI-2 showing competitive performance with GPT-5.2 Thinking and Claude Opus 4.6 Thinking.
The Deep Think in 3.5 Pro inherits this architecture but benefits from the expanded context window and likely a more powerful base model. The challenge isn't just the ability to reason, but the ability to reason at length without losing the thread.
Deep Think vs. OpenAI's o-series vs. Claude's Thinking
The fundamental difference lies in control. OpenAI's o-series (o1-preview, agentic score of 90.2) operates with opaque reasoning: the model "thinks" but you don't see the intermediate steps in a structured way. Claude Opus 4.7 (Adaptive, agentic score of 94.3) partially exposes its reasoning, but the adaptive module remains proprietary.
Google's Deep Think, according to the 3.1 documentation, offers a mode where the reasoning is not only visible but potentially directed — you can guide the resolution strategy. If 3.5 Pro extends this capability with 2M tokens, it opens up reasoning workflows on massive documents that no one else offers today.
The gap that Pro needs to bridge
According to Wavespeed.ai, Gemini 3.5 Flash already beats Gemini 3.1 Pro on code and agentic tasks, but has regressed in complex reasoning. This is exactly the gap that Pro is designed to bridge: the speed of Flash with the reasoning depth of a thinking model.
In the comparison of the best LLMs for coding, this speed/depth distinction is central. A fast but superficial code model does not replace a slower model that understands why an architecture is good or bad.
Pricing: two scenarios, one strategic issue
The pricing of Gemini 3.5 Pro is perhaps the most politically sensitive aspect of this release. Google is playing a delicate game between profitability and market aggressiveness.
Scenario 1: Flash pricing ($1.50/$9 per M tokens)
TechFastForward reports that Google could align Pro's pricing with that of Flash. This is the explosive scenario. At $1.50 for input and $9 for output per million tokens, Pro would cost a fraction of GPT-5.5 or Claude Opus 4.7 for a 10x larger context window.
This is also the least likely scenario at GA. Preview pricing is often subsidized to drive adoption.
Scenario 2: Vertex pricing ($3.50/$10.50 per M tokens)
ZoomBangla iNews cites these figures for Vertex AI. This is more realistic but remains very aggressive. As a reminder, DeepSeek V4 Pro recently imposed a permanent price cut that accelerated the price war. Google cannot afford to be perceived as expensive.
The 10x problem, according to ByteIota
ByteIota identifies a "10x pricing problem": with a 2M token context, a single poorly calibrated prompt can cost 10x more than with a 200K model. Per-token pricing becomes dangerous when the window is so wide that users fill it "because they can" rather than because it's necessary.
Google will likely need to introduce guardrails — contextual quotas, cost alerts, or tiered pricing for partial contexts. Otherwise, the surprise bill will become a barrier to adoption.
The talent exodus: the human factor behind the code
A model doesn't ship on its own. And at DeepMind, the team supposed to deliver Gemini 3.5 Pro in 10 days is in the midst of a seismic shift.
Shazeer, Jumper: what Google is really losing
Noam Shazeer is one of the founding figures of modern AI — co-author of the 2017 paper "Attention Is All You Need" which defined the Transformer architecture. His departure (or imminent departure) from DeepMind is not an ordinary career adjustment. It is the loss of an architectural vision that has shaped every generation of Gemini.
John Jumper, 2024 Nobel Prize winner in chemistry for AlphaFold, represents another type of loss: that of top-tier scientific credibility. When a Nobel laureate leaves your AI lab, the signal sent to the community is brutally negative.
The impact on the technical roadmap
The exodus creates a concrete execution risk. The models of this generation are not incremental improvements — they involve deep architectural choices (MoE scaling, long attention management, reasoning orchestration). The people who understand these choices in depth are leaving.
According to OFox.ai, Google has already delayed Pro once — from May to June. A second postponement would be interpreted not as caution, but as difficulty.
The aggravated market dynamic
This exodus is happening at the worst possible time. OpenAI is releasing GPT-5.5 (agentic score of 98.2, the highest on the market). Anthropic is maintaining Claude Opus 4.7 Adaptive at 94.3. The Claude, GPT, Gemini, Llama comparison for 2026 shows a market where Google is in a challenger position, not a leader. Losing its chief architects in this position is a major strategic risk.
Gemini 3.5 Pro vs. the competition: where does it really stand?
Rather than an abstract ranking, let's look at the dimensions that matter to a developer or a business in June 2026.
The comparison table
| Model | Context | Reasoning | Agentic Score | Input/Output Price (June 2026) |
|---|---|---|---|---|
| Gemini 3.5 Pro | 2M tokens | Deep Think | Unreleased (preview) | ~$1.50-3.50 / ~$9-10.50 per M |
| GPT-5.5 (OpenAI) | ~256K tokens | o-series | 98.2 | ~$15 / ~$60 per M (estimated) |
| Claude Opus 4.7 (Anthropic) | ~200K tokens | Adaptive | 94.3 | ~$15 / ~$75 per M (estimated) |
| Gemini 3.1 Pro Deep Think | ~1M tokens | Deep Think v1 | 87.3 | ~$1.25 / ~$5 per M |
| GLM-5.2 (Z.AI) | 1M tokens | Reasoning | 82 (GLM-5) | Open-weights, free locally |
On context, Pro stands alone at the top
No proprietary frontier model offers 2M tokens in production. GLM-5.2 offers 1M in open-weights, which is remarkable, but the base model quality (agentic benchmark score of 82 for GLM-5) remains below frontier models. The guide to the best LLMs to run locally positions GLM as the best local option — not as a direct competitor to Pro in raw quality.
On reasoning, the unknown persists
The real test will be benchmarking Deep Think v2 (in 3.5 Pro) against GPT-5.5 and Claude Opus 4.7 Adaptive. Gemini 3.1 Deep Think was competitive but not dominant. If 3.5 Pro doesn't make a significant leap, the 2M tokens will become a niche argument (long context) rather than an argument for general superiority.
On price, the advantage is structural
Even in the most expensive scenario ($3.50/$10.50), Gemini 3.5 Pro would cost between 4x and 7x less than GPT-5.5 or Claude Opus 4.7 per token. For businesses that bill per token or have high-volume workflows, this gap is decisive. Free and low-cost AI APIs like Groq or OpenRouter remain relevant for simple use cases, but Pro targets a different segment: heavy reasoning on massive contexts.
What Flash's release tells us about Pro
Gemini 3.5 Flash has been live since May 19, 2026. Its existence isn't just a product — it's a signal.
Flash as an indicator of the 3.5 architecture
The official DeepMind page states that "Gemini 3.5 Flash delivers code and reasoning quality close to Gemini Pro while maintaining Flash's speed and cost." This statement is revealing: it means that the 3.5 architecture is intrinsically more efficient than 3.1. The same "engine" running Flash at 289 tokens/second is supposed to run Pro with more capacity.
Flash's agent benchmarks are promising
Gemini 3.5 Flash already beats Opus 4.7 and GPT-5.5 on certain agent benchmarks. This is a surprising result for a "lightweight" model and suggests that the architectural gains of the 3.5 generation are real. Pro should amplify these gains with more parameters and Deep Think mode permanently enabled.
But the regression in complex reasoning is concerning
Wavespeed.ai notes that Flash has regressed in complex reasoning compared to 3.1 Pro. If this regression is structural to the 3.5 architecture (a speed/depth trade-off), then Pro might struggle to bridge the gap simply by scaling. This is the main risk of the "Flash first, Pro later" strategy.
Multimodal stakes: beyond text
Gemini 3.5 Pro is not just a text model. The 3.5 range includes multimodal capabilities that position it as the direct successor to Gemini Ultra, according to Codersera.
The Omni legacy
Gemini Omni established Google's any-to-any capability: text, image, audio, video as input, video as output. 3.5 Pro is expected to inherit this frontier multimodality, which sets it apart from GPT-5.5 (primarily text/code) and Claude Opus 4.7 (text/image).
The video use case is the real differentiator
Analyzing a one-hour video with 2M context tokens, understanding visual transitions, audio, and displayed text — this is a use case that no competitor can handle today at this scale. Production, monitoring, and training teams could benefit from this directly.
But once again, the proof will be in the pudding. Frontier multimodality is easy to announce, difficult to deliver with consistent quality.
Reputational risk: what happens if Google misses the June 30 deadline?
This is the question the entire market is asking. And the answer depends on how the deadline is missed.
Scenario 1: silence until July 1, followed by a delay announcement
This is the most damaging scenario. The Polymarket market would immediately trade against Google. The tech press would headline the second delay. And in the context of the talent exodus, the narrative would be impossible to control: "Google is losing its best minds and missing its deadlines."
Scenario 2: partial release on June 30, progressive GA
Google could announce limited availability (AI Studio only, restricted quota) on June 30, with a full GA on Vertex in July. This is politically viable and technically realistic — in fact, this is what ByteIota describes as the most likely scenario: a limited Vertex enterprise preview evolving into GA.
Scenario 3: full release on June 30
The best-case scenario for Google. But it implies that the remaining DeepMind team has finalized the model despite the departures, which would be a strong signal of resilience. Codersera maintains that the GA is expected "late June 2026," which remains consistent with this scenario.
❌ Common mistakes
Mistake 1: confusing preview and GA
Several articles confuse the limited preview access on Vertex AI with general availability. ByteIota is clear: the enterprise preview is limited, GA is expected in June. These are not the same things in terms of stability, SLA, and guaranteed pricing.
Mistake 2: comparing Pro's agentic scores when they don't exist
Gemini 3.5 Pro has no published agentic score. Comparing its 2M tokens with GPT-5.5's score of 98.2 as if they were equivalent is a reasoning error. Context size and reasoning quality are different axes.
Mistake 3: ignoring the real cost of long context
Even at $1.50 per million input tokens, filling 2M tokens costs $3 per prompt. For output, at $9 per million, a 50K token response costs $0.45. An agentic workflow that performs 20 iterations on a 2M token context can quickly exceed $100 per session. Per-token pricing is misleading when the window is massive.
❓ Frequently Asked Questions
Will Gemini 3.5 Pro be available in the free version of Gemini?
Nothing indicates that Pro will arrive in the free tier at GA. Flash is already the free model, and Pro is positioned as a premium model. It will likely be accessible via AI Studio (with a quota) and Vertex AI (paid).
2M tokens, how many pages is that?
About 1,500 pages of dense text, or 250,000 to 300,000 lines of code, or 4 to 6 hours of audio transcription. The exact conversion depends on the tokenizer, but this is the right order of magnitude.
Is Deep Think different from Claude's "thinking mode"?
Yes. Claude's thinking mode exposes partial reasoning with a proprietary adaptive module. Deep Think, according to the Gemini 3.1 documentation, offers more explicit System 2 reasoning that is potentially user-steerable. Version 3.5 Pro could amplify this difference.
Do I have to choose between Pro and Flash?
Flash is optimized for speed and cost (289 tokens/second). Pro aims for reasoning depth with Deep Think and a massive 2M context. If your tasks are simple and repetitive, Flash is enough. If you need deep analysis on long documents, Pro is the right choice — once available.
What real impact does the talent exodus actually have on the product?
The impact is indirect but real. The departing architects don't directly code the final version — but they define the choices that make certain compromises possible or impossible. The risk lies more with future iterations than with 3.5 Pro itself, which is likely already locked in terms of architecture.
✅ Conclusion
Gemini 3.5 Pro is Google's litmus test in 2026: a model with 2M context tokens, Deep Think reasoning, and aggressive pricing, delivered under pressure by a team bleeding talent. The specs are impressive enough to justify the wait. But a missed deadline on June 30 would turn that wait into doubt. To follow the evolution of this lineup and compare it with alternatives, check out our monthly comparison of the best LLMs.