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Topic: "hybrid-attention"
OpenAI GPT Image-1.5 claims to beat Nano Banana Pro, #1 across all Arenas, but completely fails Vibe Checks
gpt-image-1.5 nano-banana-pro mimo-v2-flash deepseek-v3.2 openai gemini xiaomi lmsys deepseek openrouter image-generation instruction-following benchmarking model-efficiency long-context multi-token-prediction hybrid-attention model-optimization inference-speed agentic-workflows model-architecture model-quantization fuli_luo eliebakouch
OpenAI released its new image model GPT Image 1.5, featuring precise image editing, better instruction following, improved text and markdown rendering, and faster generation up to 4×. Despite topping multiple leaderboards like LMArena (1277), Design Arena (1344), and AA Arena (1272), user feedback from Twitter, Reddit, and Discord communities is largely negative compared to Nano Banana Pro by Gemini. Xiaomi introduced the MiMo-V2-Flash, a 309B MoE model optimized for inference efficiency with 256K context window, achieving state-of-the-art scores on SWE-Bench. The model uses Hybrid Sliding Window Attention and multi-token prediction, offering significant speedups and efficiency improvements. The timing of OpenAI's launch amid competition from Gemini and Nano Banana Pro affects user sentiment, highlighting challenges in benchmarking relevance.
not much happened today
mobilellm-r1 qwen3-next-80b-a3b gpt-5 meta-ai-fair huggingface alibaba openai reasoning model-efficiency hybrid-attention long-context benchmarking agent-evaluation hallucination-detection model-calibration inference-complexity model-pricing _akhaliq tacocohen pkirgis sayashk
Meta released MobileLLM-R1, a sub-1B parameter reasoning model family on Hugging Face with strong small-model math accuracy, trained on 4.2T tokens. Alibaba introduced Qwen3-Next-80B-A3B with hybrid attention, 256k context window, and improved long-horizon memory, priced competitively on Alibaba Cloud. Meta AI FAIR fixed a benchmark bug in SWE-Bench affecting agent evaluation. LiveMCP-101 benchmark shows frontier models like GPT-5 underperform on complex tasks with common failure modes cataloged. OpenAI highlights hallucination issues due to benchmark incentives, proposing calibration improvements. Community demos and tooling updates continue to evolve.