Comprehensive side-by-side LLM comparison
Gemma 3 27B leads with 1.2% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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.
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.
14 days newer

Gemma 3 27B
Google DeepMind
2025-03-12
Qwen2.5-Omni-7B
Alibaba / Qwen
2025-03-26
Average performance across 1 common benchmarks
Gemma 3 27B
Qwen2.5-Omni-7B
Performance comparison across key benchmark categories
Gemma 3 27B
Qwen2.5-Omni-7B
Available providers and their performance metrics
Gemma 3 27B
Qwen2.5-Omni-7B
Gemma 3 27B
Qwen2.5-Omni-7B