1M context is now generally available for Opus 4.6 and Sonnet 4.6

Original Article ↗ Hacker News Discussion ↗

Overall reaction to 1M context

  • Many are excited: fewer forced compactions, easier long-running coding/debugging and multi-hour agent workflows.
  • Others report that quality still degrades well before 1M tokens (often ~150–200k, sometimes ~600–700k) and that 1M is mainly useful for avoiding compaction, not for “using” the whole window intelligently.
  • Several say the “dumb zone” problem has improved in recent months but not disappeared.

Pricing, plans, and token usage

  • Key change: standard Opus/Sonnet pricing now applies across the full 1M context; previous “long-context premium” goes away.
  • Confusion and complaints around subscription tiers (Pro/Max/extra usage, 5x vs 20x plans, fast mode, 1M access on Pro).
  • Long sessions are very expensive: large contexts multiply per-call cost; people report burning through hundreds of dollars or hitting Max limits in minutes if they don’t manage context aggressively.
  • Prompt caching alleviates some costs but doesn’t remove them.

Context rot, compaction, and workflows

  • Broad agreement that “context rot” is real: models start forgetting design decisions, constraints, and earlier reasoning as sessions grow.
  • Compaction is widely seen as dangerous for Claude Code: can drop key steps, reintroduce solved bugs, or lose CLAUDE.md and other instructions.
  • Mitigations discussed:
    • Frequent intentional compaction via explicit summaries and fresh sessions (RESEARCH → PLAN → IMPLEMENT).
    • Using project-level memory files (CLAUDE.md, spec.md, goal.md, log.md, task.md) and reloading them into new chats.
    • Subagents/agent teams to keep the main orchestrator’s context small while workers run in fresh windows.
    • Editing/rewinding session history, or external tools to slice/summarize JSONL logs.

Comparisons with other models and harnesses

  • Some find Opus 4.6 clearly ahead; others report better long-context stability, compaction, and review quality from OpenAI’s Codex, or niche strengths from Gemini.
  • Several emphasize that harness quality (Claude Code vs Codex CLI vs third-party tools) matters as much as the base model.

Use cases and limits

  • Positive reports: large refactors, CI-driven agents, reverse engineering, emulator and game implementations, SEO/content generation, data analysis.
  • Negative reports: infrastructure work, tricky debugging, or highly constrained/low-level domains still often require strong human oversight.
  • Debate over claims that Opus 4.6 is “AGI”; many point to brittleness, looping, and architectural blind spots as counterevidence.