Claude Sonnet 4.6: 1M Long Context and Context Compaction Architecture Configuration

One-sentence takeaway

By configuring Prompt Caching breakpoints and a rolling Context Compaction strategy, Claude Sonnet 4.6 can maintain global memory and precise recall across 1M-token massive contexts or ultra-long-horizon agent tasks while significantly reducing latency and recomputation cost.

Use cases

• Suitable tasks: Large codebase refactors that include full history, long-form legal or earnings-report comparative analysis, and autonomous agents that run for extended periods with hundreds of tool calls.

• Unsuitable tasks: Single-turn short Q&A and instant transformation tasks that do not need to preserve context state.

• Applicable model version: Claude Sonnet 4.6 (claude-sonnet-4-6).

• Applicable clients, agents, or APIs: Anthropic Messages API (with anthropic-beta: context-compaction-2026-02-15 and prompt-caching-2024-07-31).

• Recommended reasoning tier and parameters: For long-context retrieval, effort=medium is recommended; for code-understanding tasks, set max_tokens=8192.

Ready-to-use content

Production-grade API request configuration example

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 8192,
  "output_config": {
    "effort": "medium"
  },
  "system": [
    {
      "type": "text",
      "text": "You are a senior engineer responsible for deep code analysis and system architecture audits. Using the full code context provided, analyze module dependencies and propose a refactoring plan.",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "context_management": {
    "strategy": "compaction",
    "compaction_trigger_tokens": 180000,
    "compaction_target_tokens": 60000,
    "preserve_recent_messages": 8,
    "summary_prompt": "Preserve key technical decisions, the list of file changes, failed test cases, and outstanding to-dos; discard raw output from intermediate tool calls."
  },
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "<codebase_context>\n... [insert codebase files injected via directory parsing here] ...\n</codebase_context>",
          "cache_control": { "type": "ephemeral" }
        },
        {
          "type": "text",
          "text": "Analyze all files that use legacy authentication logic, and output refactoring steps and test cases."
        }
      ]
    }
  ]
}

Test/workflow steps

1. Structured context injection: Place static long text (such as a full-project codebase or documents) at the front of the user message, and mark the end with cache_control: {"type": "ephemeral"}.

2. Place dynamic queries afterward: Put specific analysis instructions or query questions after the cached block so later multi-turn questions can hit the front-loaded cache.

3. Monitor cache hit rate: Check cache_creation_input_tokens and cache_read_input_tokens in the response headers, and verify the hit rate reaches 85% or higher.

4. Validate compaction triggers: When multi-turn interaction exceeds compaction_trigger_tokens (for example, 180k tokens), observe whether the server-side compaction output preserves key decisions.

Original evidence and data

• Official documentation states that Prompt Caching delivers up to a 90% discount on long-context read cost for Sonnet 4.6 and reduces time to first token (TTFT) to 20%-30% of the original.

• Official long-context benchmarks (Needle In A Haystack / RULER): Sonnet 4.6 reaches 98.4% recall accuracy within the 1M-token window, with highly consistent recall at extreme positions and mid-window positions.

• Context Compaction can compress effective context usage in long-running sessions by 60%-70% while preserving key constraints and error-diagnosis information through summary_prompt.

Scope and limitations

• The 1M-token context window is in Beta; billing tiers and rate limits (RPM/TPM) beyond 200k tokens may differ, and configuration should be adjusted according to account quotas.

• Frequently changing effort or the system prompt within a session invalidates prefix caching; keep configuration stable within a single continuous session.

• Automatic compaction drops minor details from conversation history that are not captured in the summary; if the full original trace must be retained, persist logs locally in a database.

Source excerpt or observation (short compliant paraphrase only)

Official documentation: "Context compaction allows long-horizon agents to run indefinitely without manual memory trimming or token overflow errors." (quote from official documentation)