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.
Multimodal
Sandbagging Models, Sparse Critics, Compact Reasoning
New research reveals models can fake poor performance under adversarial prompts, a smarter critic improves SWE-bench by 15 points, and Microsoft shows compact vision models can punch above their weight.
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.
