On April 5, Meta released Llama 4 Scout, claiming support for 10 million tokens of context — no open-source model comes close to this number. But stripping away the marketing, the actual situation is more complex.
Standardized evaluations like LongBench v2 and RULER show that current models' effective retrieval range mostly falls between 128K and 1M tokens. Once this range is exceeded, model performance in key information location and reasoning coherence starts to drop significantly. 10M is the technical upper limit, 1M is the effective sweet spot under current algorithm and hardware constraints.
Unlike the route of pushing theoretical limits, Qwen3.6-Plus and Claude Opus 4.7 chose to be optimal within 1M context. Alibaba's Qwen3.6-Plus, released April 29, focuses on Agent coding scenarios, with 1M tokens sufficient for full code repository analysis, and more stable coding quality — Llama 4 Scout bets on width, Qwen3.6-Plus bets on depth, the two are fundamentally different positioning.
The context-length race is experiencing a rational return. 10M tokens proves it's possible engineering-wise, laying the foundation for ultra-long document processing; but under current attention mechanism and hardware constraints, 1M tokens is the realistic choice for production environments. For engineers choosing models, the key is to ask yourself: do you want to process an entire library, or just one floor's worth of books? The former is worth betting on the 10M token future, the latter can just pick a 1M token model now.