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
- 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). - Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
- 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
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- 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.