Comprehensive side-by-side LLM comparison
Gemini Diffusion leads with 1.6% higher average benchmark score. Gemma 3 27B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Google DeepMind
Gemini Diffusion is an experimental text and code generation model from Google DeepMind, announced at Google I/O in May 2025 as the first diffusion-based language model to achieve quality comparable to autoregressive models on standard benchmarks. Unlike transformer-based models that predict tokens sequentially left-to-right, it generates entire blocks of text by iteratively refining noise — the paradigm used in image and video generation models — enabling faster sampling speeds and stronger mid-generation error correction for code and mathematical editing tasks. At announcement it was available only as an experimental demo via waitlist, with no public API, marking it as a research milestone rather than a production deployment.
Google DeepMind
Gemma 3 27B is a 27-billion-parameter open-weight model from Google DeepMind, released in March 2025 alongside the Gemma 3 12B as the higher-capability variant in the series, built with native vision-language support for text and image inputs across a 128K token context window. Among the Gemma 3 releases, the 27B delivered the strongest results on instruction-following and knowledge-intensive reasoning tasks, making it the preferred option for developers needing greater accuracy from a self-hostable model. Its open-weight availability under a permissive license made it a common starting point for vision-language fine-tuning projects.
2 months newer

Gemma 3 27B
Google DeepMind
2025-03-12

Gemini Diffusion
Google DeepMind
2025-05-20
Average performance across 1 common benchmarks
Gemini Diffusion
Gemma 3 27B
Performance comparison across key benchmark categories
Gemini Diffusion
Gemma 3 27B
Available providers and their performance metrics
Gemini Diffusion
Gemma 3 27B
Gemini Diffusion
Gemma 3 27B