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Developers
What are open-weight models, and how far behind the frontier are they?
Downloadable parameters under a licence, not open source, currently landing at 58 percent of the leading closed model's score for 44 percent of the cost per point.
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· The Open Source AI Definition requires data information, complete training code and parameters, and says open source weights must include the data information and code used to derive them· DeepSeek V4.1-Flash is a 552B-backbone mixture of experts activating 8B parameters per token during prefill and 16B during decode, trained on a 45T-token multimodal corpus, released under MIT· OpenBMB released MiniCPM5-2B under Apache 2.0 with its training datasets and GGUF builds for llama.cpp, Ollama and LM Studio· MiniCPM5-2B is a dense 2.5-billion-parameter model built for on-device deploymentGo deeper · 6 min
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