Google DeepMind model releases

A specialized, highly efficient cybersecurity model built on Gemini 3.5 Flash and fine-tuned to find and fix software vulnerabilities at a lower price per token than larger models. Deployed inside Google's CodeMender agent, where multiple 3.5 Flash Cyber agents collaborate to produce a single combined report, reaching competitive frontier performance on the CyberGym benchmark. Given its dual-use nature, it is not generally available: access is limited to governments and trusted partners via CodeMender as part of a limited-access pilot program.

Google DeepMind's fastest and most cost-effective 3.5-class model, released July 21, 2026 for low-latency and high-throughput agentic workloads like agentic search and document processing. Runs at ~350 output tokens/s (Artificial Analysis) with configurable thinking levels and built-in computer use, priced at $0.30 / $2.50 per 1M input/output tokens. Multimodal over a 1M-token context and a large step up on 3.1 Flash-Lite: Terminal-Bench 2.1 54% (vs 31%), GDM-MRCR v2 72.2% (vs 60.1%), GDPval-AA v2 1140 (vs 642); on several agentic and coding evals it even surpasses 3 Flash (SWE-Bench Pro 54.2% vs 49.6%, OSWorld-Verified 74.0% vs 65.1%). Available in the Gemini API (AI Studio, Android Studio), Gemini Enterprise, the Gemini app, and rolling out in Google Search.

Google DeepMind's July 2026 workhorse Flash model, built for scaling agentic workflows. Multimodal over a 1M-token context, it improves on Gemini 3.5 Flash in coding, knowledge work, and computer use while cutting output-token usage ~17% (up to 65% on some benchmarks like DeepSWE) and taking fewer reasoning steps and tool calls. Ships at a lower price than 3.5 Flash ($1.50 / $7.50 per 1M input/output tokens). Google-reported gains: DeepSWE 49% (vs 37%), MLE-Bench 63.9% (vs 49.7%), OSWorld-Verified 83.0% (vs 78.4%), GDPval-AA v2 1421 (vs 1349); knowledge cutoff advances to March 2026. Computer use is a built-in client-side tool. Available in the Gemini API (AI Studio, Android Studio, Antigravity), Gemini Enterprise, and the Gemini app.

An open-weight text-diffusion model built on the Gemma 4 26B-A4B MoE backbone (25.2B total / 3.8B active). Denoises text in parallel 256-token blocks for up to ~4x faster generation (1,000+ tok/s on an H100), with a 256K context and text, image, and video input. Apache-2.0.

A dense 12B member of the Gemma 4 family with a unified, encoder-free multimodal architecture: vision and audio are projected straight into the LLM backbone. First medium-size Gemma to natively ingest audio; runs on a 16GB laptop. 256K context, Apache-2.0.

Announced at Google I/O 2026; emphasizes deep multimodal reasoning over a 2M-token context. Recent reporting says the broad launch slipped from June toward July while testers continue using it in Google Antigravity and LMArena.

Google's fast, cost-efficient Gemini 3.5 tier, unveiled at I/O 2026. Multimodal over a 1M-token context and tuned for agentic and coding workflows; Google says it beats Gemini 3.1 Pro on coding and tool-use while running ~4x faster.

Google DeepMind's Gemma 4 advanced-reasoning open model for personal computers, part of the April 2026 Gemma 4 family.

Generally available multimodal flagship with native tool use and a 2M-token context.

Google's open multimodal model: 128k context, 140+ languages, runs on a single GPU.

Google's enhanced Gemini 2.5 reasoning mode for harder math, science, coding, and multimodal analysis. Previewed at Google I/O 2025 and later made available to Gemini app subscribers, Deep Think uses more deliberative reasoning for complex prompts.

Google's lowest-latency, lowest-cost Gemini 2.5 tier, designed for summarization, classification, extraction, routing, and other high-volume production tasks. Proprietary API model with a 1M-token context and multimodal support.

Google's faster, lower-cost Gemini 2.5 model for high-throughput multimodal and agentic workloads. It brought Gemini 2.5's reasoning improvements to a production Flash tier with a 1M-token context and broad text, image, audio, video, and coding support.

Google's mobile-first Gemma 3n model variant, built with a MatFormer-style architecture for efficient on-device multimodal inference. The E4B variant has roughly 4B effective parameters, supports text, vision, audio, and video-oriented use cases, and is released under Gemma terms.

Reasoning-focused Gemini 2.5 model that made thinking a core part of Google's flagship model line.

First Gemini 2.0 release, built for native multimodal input/output, tool use, and agentic product integrations.

Second-generation Gemma model, improving open-weight quality and efficiency at 9B and 27B sizes.

Open code-specialized Gemma model for local code completion, generation, and instruction-following.

First Gemma open-weight text model family, derived from the same research lineage as Gemini.

Gemini generation that introduced production-scale long context, eventually expanding to a two-million-token window.

Google's first natively multimodal Gemini flagship, since superseded by the 1.5/2/3 lines.

Google's improved multilingual, reasoning, and coding foundation model family introduced at I/O 2023.

Google's 540B Pathways model; the API was later deprecated in favor of Gemini.

The bidirectional encoder that reshaped NLP and seeded the transformer era.