While attention has been recently focused on DeepSeek, Alibaba has quietly made significant strides with two new models: Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M. These represent a breakthrough as the first open-source Qwen models capable of handling contexts up to 1 million tokens.
The ability to analyze very long texts marks substantial progress in AI capabilities. However, after extensive testing of similar solutions like Gemini Flash 2.0, I’ve encountered clear practical limitations that temper enthusiasm.
In my daily work, I frequently need to extract specific information from extensive documents or websites, such as real estate pricing data. Structured outputs excel in these scenarios, though certain technical constraints persist, including issues with date handling in Pydantic validation across some models.
Long-context models frequently struggle to maintain coherence throughout lengthy texts and demand substantial RAM resources. A less discussed but equally important limitation is the standard output restriction of 8192 tokens (approximately 6000 words), which significantly impacts practical applications in many real-world scenarios.
While workarounds exist for these challenges, current out-of-the-box solutions still perform best when extracting concise, well-defined information from long texts rather than comprehending entire documents holistically.