Google is the only one of the five labs in this series that has never had a single generation where its open and closed models weren’t built from the same underlying research — which makes the gap between what it locks and what it releases the most revealing test case of all.
This is the second entry in LIWARSE’s five-part review of the leading closed and open models from the world’s most consequential AI providers. Google DeepMind is unusual among the five for treating open release not as a side project but as a parallel product line, drawn from the same Gemini research that powers its closed flagship.
The Closed Flagship: Gemini 3.1 Pro
As of this writing, Gemini 3.1 Pro remains Google’s shipping flagship. Its intended successor, Gemini 3.5 Pro, was unveiled on stage at Google I/O on May 19, 2026 but has slipped past three separate release windows amid a reported rebuild of its reasoning and tool-calling pipeline — a reminder that even the best-resourced labs do not always ship on schedule. Gemini 3.1 Pro remains the model behind Google’s frontier multimodal offering: native video understanding, top-tier vision, document comprehension, and deep integration across Search, Workspace, and the Gemini app, at aggressive pricing that undercuts most closed rivals for its class.
Its primary usage lies in multimodal work — anywhere a task mixes text with images, video, or long documents — and in Google’s own agentic tooling, where it now underpins background assistants that act proactively across Workspace rather than waiting to be asked.
The Open Counterpart: Gemma 4
Released March 31, 2026 under the fully permissive Apache 2.0 license, Gemma 4 is built from Gemini research but distributed with no monthly-active-user restrictions and no commercial gate — a cleaner license than several rival open families carry. It spans five sizes, from models that run entirely offline on a phone or a Raspberry Pi to server-class variants for coding and reasoning, and supports multimodal input across the range.
What sets Gemma apart for LIWARSE’s readership is a specific member of its extended family: MedGemma, a collection of Gemma variants trained specifically for medical text and image comprehension, alongside MedSigLIP, a matching medical-image encoder. This is precisely the kind of domain-specialized, openly auditable model LIWARSE has argued Future Medicine needs — a clinician or a resource-limited hospital can download it, inspect exactly what it was trained on, and run it entirely within their own infrastructure rather than sending patient data to a third-party API. ShieldGemma, a companion safety-classification model, plays a role similar to OpenAI’s gpt-oss-safeguard: a policy-driven filter any developer can attach to any deployment.
Future Outlook
Gemini 3.5 Pro’s repeated delay, against a field where OpenAI, xAI, and Anthropic all shipped new flagships in the same window, is the story to watch. Google has said it has already begun pretraining for Gemini 4, suggesting the 3.5 generation may end up compressed rather than abandoned. On the open side, Gemma has shipped a new major version roughly every year with steadily expanding size tiers and modality support; a Gemma 5 aligned with Gemini 4’s research would be the natural next step, though Google has made no public commitment.
Risks and Benefits Through the LIWARSE Lens
Benefits
- MedGemma is a rare case of a major lab shipping a purpose-built, openly inspectable medical model rather than leaving clinicians to adapt a general-purpose system on their own — a direct contribution to Future Medicine.
- Gemma’s genuinely unrestricted Apache 2.0 license and edge-device reach put capable, auditable AI within a rural clinic’s or a field researcher’s budget, not just a data center’s.
- Gemini 3.1 Pro’s multimodal strength gives medical imaging, satellite and space-mission telemetry, and long-document research a single capable, low-cost tool.
Risks
- A medical-domain model that is easy to fine-tune is also easy to mis-tune; MedGemma’s safety depends entirely on the judgment of whoever deploys it, with no clinical-oversight requirement built into the license itself.
- Gemini 3.5 Pro’s extended, partially opaque delay illustrates how little outside visibility the public has into a closed flagship’s true readiness — the accountability of a corporate operator cuts both ways.
- Edge deployment of Gemma at scale — phones, Jetson boards, Raspberry Pi devices — multiplies the number of independently-run copies with no central kill switch, echoing the containment concerns LIWARSE has raised about any capable model leaving a controlled environment.
The LIWARSE Assessment
Google’s pairing is the closest thing in this series to LIWARSE’s own Future Medicine vision put into practice: a domain-specific, openly auditable medical model sitting alongside safety-classification tooling, released under a license clean enough that a hospital’s legal department does not need to fear it. That does not exempt Gemma from the standard LIWARSE holds every open release to — safety-by-construction, not safety-by-policy — and Google has not published the kind of tamper-resistance attestation this movement has called for. Gemini 3.1 Pro, meanwhile, remains a capable, accountable, closed system whose greatest current risk is simply uncertainty about what replaces it and when.
Under the 3 Absolute Laws, a model that reaches a bedside in a clinic with no internet connection is not a lesser achievement than one that reaches a billion phones through an app — it may be the more important one. Google’s willingness to build for both cases, without pretending either is risk-free, is worth watching closely as this series continues.
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