AI Provider2026-05-17

Google DeepMind

Also known as: Google DeepMind / DeepMind / Google AI

Google's consolidated AI research division — formed by merging Google Brain and DeepMind — responsible for Gemini, Gemma, Veo, Imagen, AlphaFold, and other flagship AI research and products.

Overview

In 2026, Google DeepMind continued major releases including Deep Research Max in Gemini 3.1 Pro, Gemma 4 open-weights, and Veo 3 audio-synced video. AI integration into Google Search and Workspace is accelerating.

Research areas

Research spans LLMs, multimodal AI, scientific AI (protein structure prediction), and reinforcement learning. Also leading development of autonomous AI agents.

Summary of Google's Deep Research and Deep Research Max, announced April 21, 2026, built on Gemini 3.1 Pro: MCP support, native visualizations, long-horizon research workflows, DeepSearchQA 93.3% / Humanity's Last Exam 54.6%, and paid preview availability via the Gemini API — based on official sources.

Gemma 4 Complete Guide — Features, System Requirements & Ollama Setup [2026]

Complete guide to Google Gemma 4 (released April 2, 2026): 4 model variants (E2B/E4B/26B MoE/31B Dense), Apache 2.0 license, system requirements, multimodal capabilities, AIME 89% benchmark, 140+ languages, and step-by-step Ollama installation and setup instructions.

Gemma 4 vs Llama 4 vs Qwen 3.5 Comparison — 2026 Local LLM Selection Guide

Comprehensive comparison of Gemma 4, Llama 4, and Qwen 3.5 local LLMs. Detailed analysis of benchmark performance, licensing, Japanese support, hardware requirements, and use case selection criteria.

[Cerebras Inference Runs Multimodal Gemma 4 31B](https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest-inference-is-now-multimodal) Deep Dive (Announced 2026-06-29) — 1,851 Output Tokens per Second (35× a Typical GPU Endpoint), Cerebras's First Multimodal Model Accepting Images / Screenshots / Charts / UI States, the First Google DeepMind Model on the Platform, Apache 2.0 Open Weights, 18× Faster Than Claude Haiku 4.5 at a Comparable Intelligence Index of 29 Unlocking Practical "Computer Use / Image-Driven Agents / UI Debugging / Dashboard Analysis" at Wafer-Scale

**On 2026-06-29 Cerebras launched [Gemma 4 31B on Cerebras Inference](https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest-inference-is-now-multimodal)** — the platform's **first multimodal model** and the **first Google DeepMind model** available on it, in public preview. **Performance**: **1,851 output tokens/second** (**35× a typical GPU endpoint**), **≤1.5 s to first token including reasoning**, **[Artificial Analysis Intelligence Index of 29](https://artificialanalysis.ai/)** (comparable to Claude Haiku 4.5's 30), and **18× the speed of Haiku on Cerebras**. **Model specs**: **[Gemma 4 31B](../columns/gemma-4-technical-report-2026-07) dense architecture** (not MoE), **Apache 2.0 open weights**, long-context capable, and image understanding (screenshots, charts, documents, UI states, diagrams, scanned pages, forms). **Distinctive value**: unlocks multimodal workloads that used to be impractical on GPUs — **computer use, image-driven agentic loops, UI debugging with code-patch generation, real-time dashboard analysis, long-document summarization** — at wafer-scale speed. **Position**: the [Gemma 4 Technical Report](../columns/gemma-4-technical-report-2026-07) covered Google DeepMind's open-weights strategy; combined with **Cerebras's wafer-scale hardware, it reaches a new "multimodal × fast inference × open weights" infrastructure stack** — the open-weights + fast-inference camp's counter to the closed-model camp underlying [OpenAI GPT-5.6 + ChatGPT Work](../columns/openai-chatgpt-work-launch-2026-07) and [Claude Cowork](../columns/claude-cowork-web-mobile-launch-2026-07). Paired with [Nous Portal's 300+ neutral models](../columns/nous-portal-cloud-hermes-agent-2026-07) or [local LLM deployments](../columns/local-llm-landscape-2026-june-update), it becomes part of the **late-2026 open-weights-practical AI infrastructure stack**. **Cerebras Inference Cloud public preview** (limited time), with details in the [API docs](https://inference-docs.cerebras.ai/models/gemma-4-31b).

Gemma 4 Technical Report Deep Dive — Google DeepMind's Open-Weight, Natively Multimodal 2.3B–31B LLMs with an Encoder-Free 12B Unified Design and Built-In Reasoning Mode [arXiv:2607.02770](https://arxiv.org/abs/2607.02770), Published 2026-07-02, 300+ Authors, Both Dense and MoE Variants

**Google DeepMind's Gemma Team released the Gemma 4 Technical Report as [arXiv:2607.02770](https://arxiv.org/abs/2607.02770) on 2026-07-02**. The paper introduces **2.3B / 12B / 31B parameter models**, **both Dense and MoE variants**, **natively multimodal (text / image / audio)**, a **12B encoder-free unified design** (raw audio and image patches processed directly without separate encoders), a **built-in reasoning (thinking) mode**, **improved vision / audio encoders**, **architectural refinements for inference speed, memory efficiency, and long context**, and **competitive performance against larger open models on STEM, multimodal, and long-context benchmarks**. Over 300 authors contributed. Open weights allow commercial use, distributed via Hugging Face and Ollama. Sits alongside [Qwen 3.6-35B-A3B](../columns/qwen36-35b-a3b-uncensored-abliterated-2026-07) and the [Local LLM June 2026 update](../columns/local-llm-landscape-2026-june-update) as a new chapter at the open-weights frontier. **A milestone in Google's open-weights strategy**; the encoder-free unified design departs from Qwen / Llama / DeepSeek multimodality (separate vision encoder + projection). The **reasoning mode** mirrors the extended-thinking modes of Anthropic and OpenAI closed models — the open ecosystem catching up. Caveats: commercial-license fine print, potential systemic-risk classification (EU AI Act's 10^25 FLOPs threshold), and heavy Google Cloud Vertex AI integration bias.

DiffusionGemma Deep Dive — Google DeepMind's June 10, 2026 Open-Weight Text-Diffusion LLM, Same Backbone as Gemma 4 26B (A4B MoE), Up to 4× Faster Than AR Counterparts, Apache 2.0, With an Honest "Quality Trails AR" Disclosure

A primary-source deep dive on **DiffusionGemma** (`google/diffusiongemma-26B-A4B-it`, 25.2B total / 3.8B active MoE), released June 10, 2026 by Google DeepMind in coordination with NVIDIA. Grounded in the [official Google blog](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), [ai.google.dev model card](https://ai.google.dev/gemma/docs/diffusiongemma/model_card), [Hugging Face card](https://huggingface.co/google/diffusiongemma-26B-A4B-it), and [NVIDIA's blog](https://blogs.nvidia.com/blog/rtx-ai-garage-local-gemma-diffusion/). Where autoregressive (AR) models generate one token at a time left-to-right, diffusion language models (DLMs) **denoise a 256-token canvas in parallel into final text**. 15-20 tokens commit per forward pass, up to 48 denoising steps, 1,000+ tok/sec on H100, 700+ on RTX 5090, ~3.5–4× the throughput of the AR Gemma 4 counterpart. Crucially, Google **openly states that quality lags AR**: MMLU Pro 77.6 vs 82.6, GPQA 73.2 vs 82.3, MMMU Pro 54.3 vs 73.8. Apache 2.0, distributed via Hugging Face / Vertex AI / NVIDIA NIM — the first large-scale open-weight diffusion LLM in the industry. The column covers practical implications for Japanese enterprises (on-prem internal agents, code editing, low-latency workflows) and positioning against Mercury (Inception Labs), LLaDA, and Gemini Diffusion.

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