Comprehensive side-by-side LLM comparison
Gemini Diffusion leads with 2.8% higher average benchmark score. Qwen2.5-Omni-7B 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.
Alibaba / Qwen
Qwen2.5-Omni-7B is a 7-billion-parameter end-to-end multimodal model from Alibaba, released in March 2025 as part of the Omni series designed to unify perception and generation across text, images, audio, and video in a single model architecture. Unlike pipeline-based multimodal systems, it processes all modalities end-to-end and can generate both text and speech outputs, targeting use cases in voice assistants, multimodal agents, and real-time interactive applications. Its compact size made it notable for on-device and resource-constrained multimodal deployments.
1 month newer
Qwen2.5-Omni-7B
Alibaba / Qwen
2025-03-26

Gemini Diffusion
Google DeepMind
2025-05-20
Average performance across 1 common benchmarks
Gemini Diffusion
Qwen2.5-Omni-7B
Performance comparison across key benchmark categories
Gemini Diffusion
Qwen2.5-Omni-7B
Available providers and their performance metrics
Gemini Diffusion
Qwen2.5-Omni-7B
Gemini Diffusion
Qwen2.5-Omni-7B