In May 2026, a new study targeting million-token context capability attracted attention. Using classical Chinese as test material, the study designed two experiments: single-needle retrieval (locating hidden information within 1M tokens) and multi-hop reasoning (3-hop relational chain traversal, covering 256K, 512K, and 1M tiers). The study covered five flagship models claiming million-token context support: Gemini 3.1 Pro, Claude Opus 4.7, GPT-5.5, Qwen3.6-plus, and DeepSeek V4 Pro. The results show a counterintuitive conclusion: single-needle retrieval at the 1M scale is essentially solved, with Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.5 all reaching 100% accuracy. But multi-hop reasoning is the real dividing line, with three models showing sharply different decay curves: stable-type (Gemini 3.1 Pro, Claude Opus 4.7) maintain 80%+ accuracy within 512K, with only slight decay at 1M; cliff-type (GPT-5.5, Qwen3.6-plus) have acceptable accuracy at 512K (4/5) but plummet to 2/5 and 0/5 upon entering 1M; gradual-decline-type (DeepSeek V4 Pro) shows continuous decay from 256K to 1M.
This finding reveals a core issue: vendor-claimed context window length does not equal actual usable multi-hop reasoning length. Multi-hop reasoning, not single-needle retrieval, is the key metric distinguishing current million-context flagship models' true capability. This also explains why RAG and knowledge graphs remain irreplaceable in production — if a model shows cliff-like decay between 512K and 1M, the so-called million-context may be theoretical only in real applications. The study's choice of classical Chinese isn't accidental: classical Chinese has very high information density per character and is abundant in LLM pretraining data, naturally forming a test scenario for tokenization asymmetry and training data leakage, making evaluation conclusions more rigorous. Brief take: For real engineering selection, this study's takeaway is clear — when choosing long-context models, don't just look at the maximum context window number; do real multi-hop retrieval tests tailored to your business scenario.