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3434## Context window sizes by model
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36Claude Opus 5, Claude Opus 4.8, Claude Opus 4.7, Claude Opus 4.6, Claude Sonnet 5, and Claude Sonnet 4.6 have a 1M-token context window on the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry. [Claude Mythos Preview](https://anthropic.com/glasswing) also has a 1M-token context window.
36Claude Fable 5.1, Claude Mythos 5.1, Claude Fable 5, Claude Mythos 5, Claude Opus 5, Claude Opus 4.8, Claude Opus 4.7, Claude Opus 4.6, Claude Sonnet 5, Claude Sonnet 4.6, and [Claude Mythos Preview](https://anthropic.com/glasswing) have a 1M-token context window. A single request to any of them can generate up to 128k output tokens (`max_tokens`). Other Claude models, including Claude Sonnet 4.5, have a 200k-token context window.
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38Claude Fable 5 and Claude Mythos 5 (claude-fable-5 and claude-mythos-5) also have a 1M-token context window. A single request to any model with a 1M-token context window can generate up to 128k output tokens (`max_tokens`). Other Claude models, including Claude Sonnet 4.5, have a 200k-token context window.
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4038For every model with a 1M-token context window, 1M is the default: you don't need a beta header, and long-context requests are billed at [standard pricing](https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing).
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4240A single request can include up to 600 images or PDF pages (100 for models with a 200k-token context window). If you send many images or large documents, you might reach [request size limits](https://platform.claude.com/docs/en/api/overview#request-size-limits) before the token limit.
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5048Thinking tokens are a subset of your `max_tokens` parameter, are billed as output tokens, and count toward rate limits. With [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking), Claude determines its thinking allocation dynamically, so thinking token usage varies from request to request.
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52Whether thinking blocks from previous assistant turns stay in the context window depends on the model. On Claude Opus 4.5 and later Opus models, Claude Sonnet 4.6 and later Sonnet models, Claude Fable 5, Claude Mythos 5, and Claude Mythos Preview, the API keeps previous thinking blocks by default, and they count toward the context window like any other input tokens. On earlier Opus and Sonnet models and all Haiku models, the API automatically strips previous thinking blocks from the conversation history when you pass them back, which preserves token capacity for conversation content. For the per-model defaults, see [thinking block preservation by model](https://platform.claude.com/docs/en/build-with-claude/thinking#thinking-block-preservation-by-model). To override the default in either direction, use [thinking block clearing](https://platform.claude.com/docs/en/build-with-claude/context-editing#thinking-block-clearing).
50Whether thinking blocks from previous assistant turns stay in the context window depends on the model. On Claude Opus 4.5 and later Opus models, Claude Sonnet 4.6 and later Sonnet models, Claude Fable 5.1, Claude Mythos 5.1, Claude Fable 5, Claude Mythos 5, and Claude Mythos Preview, the API keeps previous thinking blocks by default, and they count toward the context window like any other input tokens. On earlier Opus and Sonnet models and all Haiku models, the API automatically strips previous thinking blocks from the conversation history when you pass them back, which preserves token capacity for conversation content. For the per-model defaults, see [thinking block preservation by model](https://platform.claude.com/docs/en/build-with-claude/thinking#thinking-block-preservation-by-model). To override the default in either direction, use [thinking block clearing](https://platform.claude.com/docs/en/build-with-claude/context-editing#thinking-block-clearing).
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5452The following diagram shows how tokens are managed when thinking is enabled on a model that strips previous thinking blocks:
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8482 <Step title="New user turn (turn 3)">
85 * **Input components:** All inputs and the output from the previous turn are carried forward. The thinking block from the completed tool use cycle no longer has to stay in context: on models that strip previous thinking blocks, the API drops it automatically when you pass it back, and on models that keep previous thinking blocks, you can strip it yourself at this stage. This is also where you add the next `user` turn.
83 * **Input components:** All inputs and the output from the previous turn are carried forward. The thinking block from the completed tool use cycle no longer has to stay in context: on models that strip previous thinking blocks, the API drops it automatically when you pass it back, and on models that keep previous thinking blocks, it stays unless you clear it with [thinking block clearing](https://platform.claude.com/docs/en/build-with-claude/context-editing#thinking-block-clearing). This is also where you add the next `user` turn.
8684 * **Output components:** Because there is a new `user` turn outside the tool use cycle, Claude generates a new thinking block and continues from there.
8785 * **Token calculation:** On models that strip previous thinking blocks, the previous thinking tokens no longer count toward the context window. All other previous blocks still count toward the context window, as does the thinking block in the current `assistant` turn.
8886 </Step>
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124122Image tokens are included in these budgets.
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126Claude Opus 4.7 and later Opus models, Claude Fable 5, and Claude Mythos 5 don't receive these injected tags. On Claude Opus 4.7 and later Opus models, Claude Fable 5, and Claude Mythos 5, you can give the model an explicit budget with [task budgets](https://platform.claude.com/docs/en/build-with-claude/task-budgets), which are in beta.
124Claude Opus 4.7 and later Opus models, Claude Fable 5.1, Claude Mythos 5.1, Claude Fable 5, and Claude Mythos 5 don't receive these injected tags. On these models, you can give the model an explicit budget with [task budgets](https://platform.claude.com/docs/en/build-with-claude/task-budgets), which are in beta.
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128126<Tip>
129127 For agents that span multiple sessions, design your state artifacts so that context recovery is fast when a new session starts. The [memory tool's multisession pattern](https://platform.claude.com/docs/en/agents-and-tools/tool-use/memory-tool#multisession-software-development-pattern) walks through a concrete approach. See also [Effective harnesses for long-running agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents).