Gemma 4 268B-A4B is Google's largest open model ever — 268 billion parameters in a Mixture-of-Experts architecture activating 4B per token, pushing the frontier of open-source multimodal AI.
Gemma 4 268B-A4B is an AI model released June 2025 by Google, a leading organization in large language model research. Large Gemma-4 MoE — 268B total, 4B active. Google's open model with multimodal capabilities and strong reasoning. With 268B total (4B active via MoE) and a 128K tokens context window, Gemma 4 268B-A4B is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
Gemma 4 268B-A4B employs a Mixture-of-Experts (MoE) architecture with 268B total (4B active via MoE). In a MoE design, only a subset of "expert" sub-networks activates for each token during inference — Gemma 4 268B-A4B has 268B total (4B active via MoE) total parameters but uses only a fraction per token. This allows the model to store significantly more learned knowledge than a comparably-priced dense model. MoE architectures have proven effective at reaching frontier-level capability while keeping serving costs manageable — a key reason Gemma 4 268B-A4B can be offered at the scale that platforms like LITHOVEX AI make possible.
As of 2026-10-16, Gemma 4 268B-A4B by Google demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 83.4%. For software engineering tasks, Gemma 4 268B-A4B scores 79.2% on HumanEval, which tests ability to write correct functions from docstrings. These scores place Gemma 4 268B-A4B — at 268B total (4B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of Gemma 4 268B-A4B are: MoE efficiency: 268B knowledge at 4B inference cost — extremely fast; Native multimodal from Google DeepMind; Much lower serving cost than 318B dense variant. These make it particularly well-suited for High-throughput multimodal applications where latency matters, Cost-efficient enterprise AI at 268B knowledge scale. Users working on complex, multi-step problems benefit most from Gemma 4 268B-A4B's ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, Gemma 4 268B-A4B has limitations: MoE requires specialized serving infrastructure. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. Google continues to improve Gemma 4 268B-A4B through ongoing research.
Gemma 4 268B-A4B is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making Gemma 4 268B-A4B accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, Gemma 4 268B-A4B by Google demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 83.4%. For software engineering tasks, Gemma 4 268B-A4B scores 79.2% on HumanEval, which tests ability to write correct functions from docstrings. These scores place Gemma 4 268B-A4B — at 268B total (4B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
Mixture-of-Experts: 268B total / 4B active per token, multimodal
The training pipeline for Gemma 4 268B-A4B involves multiple stages: large-scale pretraining on diverse multilingual text and code corpora to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. Google's research team — based in United States — has optimized Gemma 4 268B-A4B for the specific capability profile described above.
Inference efficiency: Gemma 4 268B-A4B at 268B total (4B active via MoE) is designed to deliver quality outputs at practical serving speeds. Its 128K tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with Gemma 4 268B-A4B on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with Gemma 4 268B-A4B: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.
Frontier-quality open-source multimodal AI applications
Research requiring Google DeepMind's training quality at scale
Enterprise AI with complete model ownership and no API dependency
Multimodal document intelligence at frontier capability
Competitive prototyping against GPT-4o using open weights
Google DeepMind is Alphabet's AI research organization, formed through the merger of Google Brain and DeepMind in 2023. Google published the original "Attention Is All You Need" paper in 2017, which introduced the transformer architecture underlying virtually every modern language model. The Gemma model family represents Google's open-weight contributions — models that bring Google research quality to open-access deployment. Gemma 3 and Gemma 4 models support multimodal inputs including images and long-document processing. Google's computational infrastructure and proprietary TPUs give it advantages in training efficiency that few organizations can match.
Funding: Part of Alphabet Inc. ($1.9T+ market cap)
Founded: 1998, headquartered in United States.
Gemma 4 268B-A4B by Google competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both Gemma 4 268B-A4B and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
Gemma 4 268B-A4B is released under Gemma Terms of Use, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs Gemma 4 268B-A4B on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
Gemma 4 268B-A4B demonstrates Google DeepMind's commitment to open AI at frontier scale. With MoE efficiency and multimodal vision, it serves global organizations seeking frontier AI capability with full model ownership and Apache 2.0 commercial freedom.
Gemma 4 268B-A4B by Google has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official Google model page.