What this error means

Error code: 413 - Request exceeds the maximum allowed number of bytes. The maximum request size is 32 MB (but payloads of ~2MB are rejected) is a Anthropic API failure pattern reported for developers trying to developer sends ~2mb document payload to claude sonnet 4 via vertex ai and gets 413 error despite being well under the documented 32mb limit — debugging difficult because error misreports the cause. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #1028 on anthropics/anthropic-sdk-python opened Sep 7, 2025 by abhiwebshar. Detailed root-cause analysis reveals Vertex AI incorrectly maps Citations API rate limit violations (actual 429 rate_limit_error) to misleading 413 'Prompt is too long' errors. Payloads as small as 147KB are rejected with 413. Root cause: error misreporting, not actual payload size limit. Affects production use of large-context capabilities on Vertex AI. Also reports 403 errors around 200k tokens blocking full context window usage.

Common causes

  • GitHub Issue #1028 on anthropics/anthropic-sdk-python opened Sep 7, 2025 by abhiwebshar. Detailed root-cause analysis reveals Vertex AI incorrectly maps Citations API rate limit violations (actual 429 rate_limit_error) to misleading 413 'Prompt is too long' errors. Payloads as small as 147KB are rejected with 413. Root cause: error misreporting, not actual payload size limit. Affects production use of large-context capabilities on Vertex AI. Also reports 403 errors around 200k tokens blocking full context window usage.

Quick fixes

  1. Confirm the exact error signature matches Error code: 413 - Request exceeds the maximum allowed number of bytes. The maximum request size is 32 MB (but payloads of ~2MB are rejected).
  2. Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.