Qwen3.5-2B
Qwen3.5-2B is a 2B dense multimodal model with 262K context, thinking mode, and native vision including video understanding. OCRBench 84.5, VideoMME 75.6. Apache 2.0 licensed.
Articles Tagged "Small Language Models"
Qwen3.5-4B
Qwen3.5-4B is a 4B dense multimodal model that matches Qwen3-30B on MMLU-Pro and beats GPT-5-Nano on vision benchmarks. Runs on 8GB VRAM, Apache 2.0 licensed, 262K-1M context.
Qwen3.5-9B
Qwen3.5-9B is a 9B dense model that outperforms Qwen3-30B on most benchmarks and beats GPT-5-Nano on vision tasks. Natively multimodal with 262K-1M context, Apache 2.0 licensed.
Kimi K2.5 vs Phi-4: Mainframe Intelligence vs the Best Pocket Reasoning Model
A detailed comparison of Moonshot AI's 1T-parameter Kimi K2.5 against Microsoft's 14B Phi-4 - the most extreme size gap in frontier AI, with 71x the parameters but vastly different use cases.
Microsoft Phi-4
Microsoft's 14B dense transformer that consistently beats models 5x its size on MATH and GPQA, available under the MIT license for unrestricted commercial use.
Qwen3.5-27B vs Phi-4: When Twice the Parameters Is Not Twice as Obvious
A data-driven comparison of Alibaba's Qwen3.5-27B and Microsoft's Phi-4 - a 27B hybrid architecture versus a 14B STEM specialist, testing whether raw parameter count or training efficiency wins in practice.
Cohere's Tiny Aya Fits 70 Languages Into 3.35 Billion Parameters and Runs on a Phone
Cohere Labs releases Tiny Aya, a 3.35B open-weight multilingual model that beats Gemma 3 4B in 46 of 61 languages on translation and runs at 32 tokens/sec on an iPhone.
