Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Both models have their strengths depending on your specific coding needs.
Google DeepMind
Gemma 3 12B is a 12-billion-parameter open-weight model from Google DeepMind, released in March 2025 as part of the Gemma 3 series designed to bring multimodal reasoning to accessible hardware. The model supports both text and image inputs across a 128K token context window, extending the vision capabilities that defined the Gemma 3 generation compared to earlier text-only Gemma releases. It became widely adopted for domain-specific fine-tuning in research and enterprise settings where full multimodal capability was needed without the infrastructure demands of larger frontier models.
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 12B
Google DeepMind
2025-03-12
Qwen2.5-Omni-7B
Alibaba / Qwen
2025-03-26
Average performance across 1 common benchmarks
Gemma 3 12B
Qwen2.5-Omni-7B
Performance comparison across key benchmark categories
Gemma 3 12B
Qwen2.5-Omni-7B
Available providers and their performance metrics
Gemma 3 12B
Qwen2.5-Omni-7B
Gemma 3 12B
Qwen2.5-Omni-7B