All tags
Topic: "performance-evaluation"
MiniMax M2 230BA10B — 8% of Claude Sonnet's price, ~2x faster, new SOTA open model
minimax-m2 hailuo-ai huggingface baseten vllm modelscope openrouter cline sparse-moe model-benchmarking model-architecture instruction-following tool-use api-pricing model-deployment performance-evaluation full-attention qk-norm gqa rope reach_vb artificialanlys akhaliq eliebakouch grad62304977 yifan_zhang_ zpysky1125
MiniMax M2, an open-weight sparse MoE model by Hailuo AI, launches with ≈200–230B parameters and 10B active parameters, offering strong performance near frontier closed models and ranking #5 overall on the Artificial Analysis Intelligence Index v3.0. It supports coding and agent tasks, is licensed under MIT, and is available via API at competitive pricing. The architecture uses full attention, QK-Norm, GQA, partial RoPE, and sigmoid routing, with day-0 support in vLLM and deployment on platforms like Hugging Face and Baseten. Despite verbosity and no tech report, it marks a significant win for open models.
ChatGPT responds to GlazeGate + LMArena responds to Cohere
qwen3-235b-a22b qwen3 qwen3-moe llama-4 openai cohere lm-arena deepmind x-ai meta-ai-fair alibaba vllm llamaindex model-releases model-benchmarking performance-evaluation open-source multilinguality model-integration fine-tuning model-optimization joannejang arankomatsuzaki karpathy sarahookr reach_vb
OpenAI faced backlash after a controversial ChatGPT update, leading to an official retraction admitting they "focused too much on short-term feedback." Researchers from Cohere published a paper criticizing LMArena for unfair practices favoring incumbents like OpenAI, DeepMind, X.ai, and Meta AI Fair. The Qwen3 family by Alibaba was released, featuring models up to 235B MoE, supporting 119 languages and trained on 36 trillion tokens, with integration into vLLM and support in tools like llama.cpp. Meta announced the second round of Llama Impact Grants to promote open-source AI innovation. Discussions on AI Twitter highlighted concerns about leaderboard overfitting and fairness in model benchmarking, with notable commentary from karpathy and others.