Microsoft Phi-4 Reasoning: Small Model, Big Math
Microsoft's Phi-4 reasoning family delivers near-70B-class math performance in a 14B open-weight package, but the overthinking problem is real and the use case is narrower than the benchmarks suggest.
Small language models
Nemotron 3 Nano 4B: NVIDIA Edge Model Runs on 8GB
NVIDIA's Nemotron 3 Nano 4B packs a Mamba-dominant hybrid architecture, 262K token context, and 95.4% on MATH500 into a model that fits an 8GB Jetson Orin Nano.
Mistral Small 4 Review: One Model, Three Jobs
Mistral Small 4 packs reasoning, vision, and agentic coding into a 119B MoE under Apache 2.0 - a serious small-model contender at a price that's hard to ignore.
Small Language Model Leaderboard: Best Under 10B
Rankings of the best small language models under 10 billion parameters, comparing Phi-4, Gemma 3, Qwen 3.5, and more across key benchmarks.
Microsoft's Phi-4 Vision Matches Models 10x Its Size
Microsoft releases Phi-4-reasoning-vision-15B - a 15B open-weight multimodal model trained on 240 GPUs in 4 days that competes with 100B+ parameter models on math, science, and GUI understanding.
Qwen 3.5 Small Series Ships Four Models From 0.8B to 9B
Alibaba completes the Qwen 3.5 lineup with four small models - 0.8B, 2B, 4B, and 9B - all natively multimodal, 262K context, Apache 2.0. The 9B outperforms last-gen Qwen3-30B and beats GPT-5-Nano on vision benchmarks.
Qwen3.5-0.8B
Qwen3.5-0.8B is the smallest natively multimodal model in the Qwen 3.5 family - 0.8B parameters handling text, images, and video with 262K context. MathVista 62.2, OCRBench 74.5. Apache 2.0.
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.
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.
