Open Source AI: SOTA Models 2025

Open Source AI Models: A New Competitive Landscape

This collection showcases the remarkable advancement of open source AI models across multiple domains, demonstrating how they now compete directly with proprietary systems from major tech companies. The landscape includes general-purpose LLMs like GLM-4.5 (355B parameters, 3rd globally), DeepSeek-V3-0324 (685B parameters with significant reasoning improvements), and Kimi K2 (1T total parameters with 32B active); specialized coding models such as Qwen3-Coder-480B-A35B-Instruct and DeepSeek-Coder-V2 that rival GPT-4 Turbo in coding tasks; and multimodal embedding models including jina-embeddings-v4 and Nomic Embed Multimodal that achieve state-of-the-art performance in visual document retrieval.

Key themes emerging from this collection include the widespread adoption of Mixture-of-Experts (MoE) architectures for computational efficiency, agentic capabilities becoming a primary focus with models designed for tool use and autonomous problem-solving, multimodal integration enabling unified text-image processing, and long context support extending to millions of tokens. Performance benchmarks consistently show these open models matching or exceeding proprietary alternatives on specialized tasks, while leaderboard data confirms their competitive positioning across reasoning, coding, and retrieval benchmarks.

This represents a fundamental shift in AI development, where open source models are no longer playing catch-up but are setting new standards and driving innovation in the field.