Long Context ≠ "Can Read"

Many models claim million-token context support, but real-world performance varies dramatically. "Supports 1M context" and "accurately finds a specific sentence in 1M tokens" are very different things.

Performance by Context Length

128K: All mainstream models handle this well. GPT-5.5, Claude Opus, DeepSeek V4 Pro all achieve 95%+ retrieval accuracy.

256K: Differentiation begins. GPT-5.5 and Claude Opus maintain 90%+. DeepSeek ~88%.

500K: GPT-5.5 (~88%) > Claude Opus (~86%) > DeepSeek (~82%) > Gemini (~80%).

1M: Only a few models truly work. GPT-5.5 (~82%), Claude Opus (~80%), DeepSeek (~75%).

10M (Llama 4 Scout): Accuracy drops below 50%, suitable only for rough scanning.

Best For

Precise information retrieval → GPT-5.5 or Claude Opus Book/report summarization → DeepSeek V4 Pro (1/10 the price) Legal contract review → Claude Opus (best detail recall) Codebase understanding → DeepSeek or MiMo (million context + low price)

Tips

  1. Segment processing often outperforms single massive input
  2. Place critical info at the beginning or end of documents
  3. Use structured formatting (headings, numbers, Markdown)
  4. Test with your own documents — performance varies by content type