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Context Windows By Model ⚠ verify

Context Management & Reliability

Definition

The maximum number of tokens (input + output + thinking) a model can process in a single request, which varies by model and is billed at standard rates across the whole window.

Key points

  • 1M-token window: Opus 4.8 / 4.7 / 4.6, Sonnet 5, Sonnet 4.6, Fable 5, Mythos 5, Mythos Preview. 1M is the default — no beta header needed, billed at standard per-token pricing (a 900k request costs the same per token as a 9k one). ⚠ verify current model IDs/context sizes (2026-07-08).
  • 200k-token window: Haiku 4.5, Sonnet 4.5, Opus 4.5, Opus 4.1 (and other legacy). Max output 64k (Haiku 4.5) or lower.
  • Max output on 1M models = 128k tokens (max_tokens); on the Batch API, Opus 4.8/4.7/4.6, Sonnet 5, Sonnet 4.6 reach 300k via beta header output-300k-2026-03-24.
  • What counts toward the window: system prompt, every message (tool results, images, documents), tool definitions, and generated output including extended thinking. All three caching buckets — input_tokens, cache_read_input_tokens, cache_creation_input_tokens — count.
  • Image/PDF limit: 600 images or PDF pages per request on 1M models (100 on 200k models).
  • Newer tokenizer (Opus 4.7+, Fable 5, Sonnet 5) produces ~30% more tokens for the same text vs Sonnet 4.6 and earlier — a bigger window does not always mean proportionally more content.

Why it matters for the exam

  • Sizing scenarios: choosing a model for a large-document or long-session task turns on window size (1M vs 200k) and max output.
  • The exam tests that thinking blocks and tool results count toward the window, not just the visible prompt.

Common gotchas

  • Bigger window ≠ better recall: accuracy still degrades as tokens grow — see Context Rot.
  • 200k models (Haiku 4.5, Sonnet 4.5) can't hold what a 1M model can; don't assume all current models are 1M.
  • Overflow is handled differently by model — see model_context_window_exceeded behaviour.

See also

Sources

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