Gemma 4 26B A4B vs Grok 4.5

Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.

For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.

Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.

For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.