Comprehensive side-by-side LLM comparison
Kimi K2.5 leads with 59.4% higher average benchmark score. Qwen2.5-VL 32B Instruct supports multimodal inputs. Overall, Kimi K2.5 is the stronger choice for coding tasks.
Moonshot AI
Kimi K2.5, released by Moonshot AI in January 2026, is an updated Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters. It builds on Kimi K2 with improved coding performance across multiple languages and an expanded context window. Kimi K2.5 targets agentic development workflows, polyglot code generation, and open-source deployments requiring large-scale MoE reasoning.
Alibaba / Qwen
Qwen2.5-VL-32B-Instruct is a 32-billion-parameter vision-language model from Alibaba, extending the Qwen2.5 architecture with multimodal capabilities for understanding images, documents, charts, and video frames alongside text. The model was designed for tasks requiring deep visual reasoning — such as document parsing, table extraction, and spatial understanding — with performance that made it a practical choice for document intelligence and visual data analysis workflows. As an open-weight model, it became a widely adopted foundation for fine-tuning domain-specific multimodal applications.
10 months newer
Qwen2.5-VL 32B Instruct
Alibaba / Qwen
2025-03-01
Kimi K2.5
Moonshot AI
2026-01
Context window and performance specifications
Average performance across 1 common benchmarks
Kimi K2.5
Qwen2.5-VL 32B Instruct
Performance comparison across key benchmark categories
Kimi K2.5
Qwen2.5-VL 32B Instruct
Available providers and their performance metrics
Kimi K2.5
Moonshot AI
Qwen2.5-VL 32B Instruct
Kimi K2.5
Qwen2.5-VL 32B Instruct
Kimi K2.5
Qwen2.5-VL 32B Instruct