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OpenAI recently released its first mainstream open-weight models, which mark an entry into a field currently dominated by Chinese AI models. These models were developed in line with President Trump’s ‘AI action’ plan, which emphasized the importance of open-source AI models. OpenAI followed this guidance by releasing GPT-OSS, specifically designed as its first set of open-weight models since GPT-2. Available configurations include GPT-OSS-20B and GPT-OSS-120B.
GPT-OSS-20B features 21 billion parameters with a MoE (Mixture of Experts) transformer. It offers a context window of up to 131,072 tokens and can run effectively on 16GB VRAM platforms, making it accessible for most modern consumer GPUs. In contrast, GPT-OSS-120B has 117 billion parameters and requires at least an NVIDIA H100 platform for optimal performance.
These models are released under the Apache 2.0 license, allowing commercial use, modification, and redistribution. This gives them a fully open-source nature similar to many Chinese counterparts. OpenAI’s move into this domain is unique and likely targeted towards countering Chinese developments in AI.
When comparing parameter counts, Chinese alternatives like DeepSeek V2, Qwen3, and Alibaba models significantly outpace GPT-OSS. For instance, the DeepSeek-V2 has 236 billion parameters, while Qwen3 has 235 billion parameters. However, for real-world performance across benchmarks such as MMLU (Massive Multitask Language Understanding), AIME Math, and GPQA (PhD Science), GPT-OSS-120B performs well in reasoning tasks and mathematical operations.
In areas like agentic workloads and multilingual capabilities, GPT-OSS-120B lags behind Chinese alternatives. Nevertheless, it remains a top-tier choice for the open-source ecosystem. Open-weight models are advantageous because they bring multiple benefits to the general AI community, strengthening the U.S.’s position in this domain, previously dominated by Chinese companies.
Sam Altman and his team at OpenAI would be pleased with these results.
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