AI News & Model Reviews
Technical deep-dives into trending AI models. Benchmark analysis, architecture breakdowns, and practical recommendations.
Featured This Week
MiniMax M2.1: The New SWE-bench Leader at 90% Lower Cost
229B parameter MoE model achieves 74.0% on SWE-bench Verified, beating Claude Sonnet 4.5 at $0.30/1M tokens. Technical analysis of the cost-efficiency champion.
GLM-4.7: 95.7% on AIME 2025 - Math Reasoning Breakthrough
Zhipu AI's 358B MoE model sets new records in mathematical reasoning, surpassing GPT-5.1 High on AIME. Interleaved thinking and MIT license make it accessible.
TRELLIS.2: Production-Ready 3D Assets in 3 Seconds
Microsoft Research's 4B parameter model generates game-ready PBR assets from single images. O-Voxel architecture enables 1536x resolution with full transparency support.
Recent Analysis
Tencent HY-MT1.5: Translation Model Beats Google by 15-65%
1.8B parameter model from WMT2025 winner achieves near Gemini-3.0-Pro performance while running on smartphones. Supports 33 languages including underserved ones.
LiquidAI LFM2-2.6B: Edge Model Beats 680B DeepSeek R1
2.6B dense model using pure RL surpasses models 263x larger on instruction-following. Hybrid convolution-attention enables phone and laptop deployment.
Wan2.2 Animate: First Open-Source MoE Video Model
Alibaba's 14B MoE model combines motion transfer and character animation. 720p at 24fps for ~$0.40 per 5s clip vs $2 for Veo 3.
Z-Image-Turbo: FLUX-Quality Images on 16GB GPUs
Alibaba's 6B distilled model achieves near-FLUX quality in 8 steps on consumer hardware. Apache 2.0 license enables commercial use.
About Our Coverage
CodeSOTA tracks trending AI models from Hugging Face, arXiv, and major conferences. Our analysis focuses on verified benchmark results, not marketing claims. We cover large language models (LLMs), vision models, 3D generation, video AI, translation, and specialized domains.
Each article includes: technical specifications, benchmark comparisons with competitors, deployment requirements, and practical recommendations for different use cases.