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Topic: "office-productivity"
Z.ai GLM-5: New SOTA Open Weights LLM
glm-5 glm-4.5 kimi-k2.5 zhipu-ai openrouter modal deepinfra ollama qoder vercel deepseek-sparse-attention long-context model-scaling pretraining benchmarking office-productivity context-window model-deployment cost-efficiency
Zhipu AI launched GLM-5, an Opus-class model scaling from 355B to 744B parameters with DeepSeek Sparse Attention integration for cost-efficient long-context serving. GLM-5 achieves SOTA on BrowseComp and leads on Vending Bench 2, focusing on office productivity tasks and surpassing Kimi K2.5 on the GDPVal-AA benchmark. Despite broad availability on platforms like OpenRouter, Modal, DeepInfra, and Ollama Cloud, GLM-5 faces compute constraints impacting rollout and pricing. The model supports up to 200K context length and 128K max output tokens.
Moonshot Kimi K2.5 - Beats Sonnet 4.5 at half the cost, SOTA Open Model, first Native Image+Video, 100 parallel Agent Swarm manager
kimi-k2.5 moonshotai multimodality model-training mixture-of-experts agentic-ai vision video-understanding model-optimization parallel-processing office-productivity
MoonshotAI's Kimi K2.5 is a 32B active-1T parameter open-weights model featuring native multimodality with image and video understanding, built through continual pretraining on 15 trillion mixed visual and text tokens. It introduces a new MoonViT vision encoder and supports advanced capabilities like Agent Swarm, which coordinates up to 100 sub-agents for parallel workflows, and an Office Productivity K2.5 Agent for large-scale office tasks. This release marks a significant leap in open models from China, claiming state-of-the-art results on benchmarks like HLE and BrowseComp, and offering aggressive API pricing and throughput.