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A Claude request that fails is usually diagnosable from two values: the HTTP status and the request_id returned with the error. A 429 needs a different response from a 400, a 529, or a retired-model failure. Capture the status, Anthropic error type, message, request ID, model ID, endpoint, and timestamp before changing code.
Fix client and account causes first. Retry only transient failures, using bounded exponential backoff with jitter. Waiting will not clear a monthly spending cap, an invalid payload, an expired key, an unavailable model, or an oversized request.
Claude error statuses at a glance
Anthropic’s error families map the HTTP status to the likely cause. The table below also shows whether another attempt can help and what evidence to inspect.
| HTTP status | Anthropic error type | Typical cause | Can retry help? | First evidence and remedy |
|---|---|---|---|---|
| 400 | invalid_request_error |
Invalid field, unsupported model option, malformed payload, or an incompatible message sequence | No, not unchanged | Compare the request with the current model documentation; remove or correct the unsupported field |
| 401 | authentication_error |
Missing, invalid, revoked, or expired API key | No | Check the key, environment variable, organization, and endpoint configuration |
| 402 | billing_error |
Payment or billing configuration problem | No | Correct billing or payment status in the Console |
| 403 | permission_error |
Your organization or workspace is not allowed to use the resource | No | Verify workspace, organization, model, and resource permissions |
| 404 | not_found_error |
Wrong endpoint path, resource ID, or model ID | No | Check the URL, resource identifier, and model lifecycle status |
| 409 | conflict_error |
Two operations attempted incompatible concurrent state changes | Only after resolving the conflict | Read the response, reconcile the current state, then submit the intended operation |
| 413 | request_too_large |
The request exceeds a byte-size limit | No, not unchanged | Measure bytes, then split, compress, or move the content to an appropriate file workflow |
| 429 | rate_limit_error |
Request-rate limit, usage-tier spending cap, or Claude Code workspace spending limit | Sometimes | Inspect rate-limit guidance, Console usage, billing, and tier status before increasing retries |
| 500 | api_error |
Internal API failure | Yes, with bounded backoff | Retry; if it persists, give support the request ID and a reproducible description |
| 504 | timeout_error |
Processing exceeded the server’s time allowance | Sometimes | Use streaming for long responses, reduce unnecessary prompt size, and allow a suitable client timeout |
| 529 | overloaded_error |
The API is temporarily overloaded | Yes, with bounded backoff and jitter | Retry without synchronized worker bursts and retain the request ID in logs |
The error type and message are more specific than the status alone. For example, every 429 is not a temporary throttle: one can represent a spending cap that will continue to fail until access resumes.
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Why Claude returns a 429 and how to fix it
A 429 means Anthropic has refused the request because an organization or workspace has reached a limit. The practical fix depends on which limit was reached.
Temporary request-rate throttling
A short-lived rate limit affects how quickly requests are accepted. The response may include retry guidance, including a delay. Honor that delay, then retry with random jitter so multiple workers do not send the next attempt at exactly the same instant. Reduce concurrency or queue work when a burst is not essential.
Usage-tier spending cap
Anthropic also uses 429 for a monthly spending cap associated with the organization’s usage tier. This condition does not behave like a brief throttle: the response has no retry-after header and continues failing until access resumes. Repeating the request cannot bypass it. Open the Claude Console and check usage, billing, and tier status; change the account or spending configuration before sending more traffic.
Claude Code workspace spending limit
A Claude Code workspace can have its own spending limit. Treat that as an account or workspace issue rather than an API congestion event. Confirm which workspace the key is associated with and inspect its usage and spending controls. A different key or worker will not help if it is still attached to the same restricted workspace.
A safe 429 decision path
- Record the status, error type, message, request ID, model, endpoint, and time.
- Inspect any rate-limit headers and retry instructions in the response.
- Check Console usage, billing, and the organization or workspace spending status.
- If the evidence shows temporary throttling, lower concurrency and retry after the indicated delay with jitter.
- If a spending cap is reported or there is no retry guidance, stop retrying and resolve the account limit.
Handling 529 overloads, 500 failures, and 504 timeouts
These statuses describe conditions that can be transient, so a retry policy is appropriate, but an unlimited loop is not.
529 overloaded_error
A 529 means the API is temporarily overloaded. Keep the original request ID, wait, and retry with exponential backoff. If many workers fail together, add jitter and use a shared queue or concurrency ceiling; otherwise they can create a synchronized second surge.
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500 api_error
A 500 is an internal API failure. Retry with exponential backoff. If the same operation continues to fail after your attempt limit, contact support with the request ID, timestamp, endpoint, model, and a reproducible description rather than endlessly resubmitting it.
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A 504 means processing timed out. Anthropic recommends Messages API streaming for long-running responses. Remove unnecessary prompt material, especially duplicated context, and configure the client timeout to leave the server enough time to finish. A client-side timeout that is shorter than the expected generation time can make a healthy request appear failed locally.
Bounded exponential backoff
Use an attempt limit and a maximum delay. One implementation policy is to calculate a base delay that doubles after each failed attempt, cap it, and then add a random component:
delay = min(max_delay, base_delay * (2 ** attempt))
sleep(delay + random.uniform(0, jitter_window))The exact numbers are your service policy, not a guarantee from Anthropic. Keep the policy finite, classify the status before retrying, and preserve the original payload so a retry does not silently change the operation. Do not retry 400, 401, 402, 403, 404, or 413 without first correcting the cause.
Fixing 400 invalid_request_error
A 400 is a request-validation failure. Sending the same JSON again will produce the same result. Compare the complete payload with the current documentation for the exact model ID, then remove or change the field that violates that model’s rules.
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Assistant-message prefill restrictions
Anthropic documents 400 responses when assistant-message prefill is unsupported on Claude 4.6 and later models and on Claude Mythos Preview. If your integration appends an assistant message to force a beginning, remove that prefill or use the prompting pattern supported by the selected model.
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Thinking blocks and thinking settings
Thinking blocks are structured content. Modifying or reordering them can make the request invalid. Preserve the blocks as returned when the API expects them, and do not fabricate, edit, or move a thinking block between messages. Check the model’s current rules for enabling thinking; unsupported thinking settings also produce 400 errors.
Sampling parameters on newer models
Some specified newer models reject non-default values for temperature, top_p, or top_k. Remove those parameters or send the model’s documented defaults instead of carrying an older shared configuration into a newer model.
Payload-shape checks
- Verify that the model ID and endpoint accept the message roles and content blocks you send.
- Check JSON types, required fields, and nesting; a string where an array is required is still a 400 even when the text is valid.
- Diff a failing request against a minimal request that uses the same model.
- Log the server’s error message and request ID, but redact keys and sensitive prompt content from application logs.
413 request_too_large: measure bytes, not only tokens
A request can be within a model’s token window and still exceed the transport limit. The direct API documents these maximum request sizes:
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| Direct API operation | Maximum request size | What to change when exceeded |
|---|---|---|
| Messages | 32 MB | Split the conversation or reduce embedded media and repeated context |
| Token Counting | 32 MB | Count smaller segments and combine the results in your application |
| Batch | 256 MB | Divide the batch into smaller submissions |
| Files | 500 MB | Use the Files workflow for supported material instead of embedding it repeatedly |
These are byte limits. Images, encoded binary data, metadata, and JSON overhead can push a request over the limit before token accounting becomes relevant. Measure the serialized request body in bytes, remove duplicate instructions, reduce image dimensions or count, compress where the endpoint supports it, and split a large document into coherent parts.
Measuring the serialized body
Measure the exact bytes sent over the wire, not the number of characters in a source file. In a typical application, serialize the final JSON using the same encoding as the HTTP client, then inspect its byte length before transmission. Keep a margin below the limit for headers and client behavior, and test the largest realistic prompt in staging.
Authentication, billing, permission, resource, and conflict errors
401 authentication_error
Confirm that the API key is present in the process that makes the request, belongs to the intended organization, and has not been revoked or expired. Check environment-variable names and deployment secrets; a key that exists in a local shell may be absent in a worker or container. Never solve a 401 by retrying the same invalid key.
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402 billing_error
Correct the payment or billing configuration associated with the organization. Distinguish this from a 429 spending cap: both can stop requests, but a 402 points to billing configuration while a 429 can indicate usage-tier limits or throttling.
403 permission_error
Verify that the organization and workspace are allowed to use the model, endpoint, or resource. A valid key can still lack permission. Ask an organization administrator to confirm access rather than rotating keys at random.
404 not_found_error
Check the endpoint path, resource ID, and model ID character by character. A model that was retired can look like a model-not-found failure after an application continues sending its old identifier.
409 conflict_error
A 409 indicates incompatible concurrent state changes. Read the response, retrieve the current resource state if needed, reconcile the competing operation, and then submit the intended change. Retrying immediately can repeat the conflict.
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Anthropic classifies model lifecycle states as active, legacy, deprecated, and retired. A retired model is unavailable, so requests using its ID fail; retry logic cannot bring it back.
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- List every model ID in application code, environment variables, configuration files, scheduled jobs, and stored prompts.
- Check the Claude Console and Anthropic’s lifecycle documentation for each ID’s current state and announced retirement date.
- Replace retired or deprecated IDs with Anthropic’s listed replacement, matching capabilities and context requirements.
- Test the replacement with representative prompts, tool calls, streaming behavior, token usage, and error handling before changing production traffic.
- Keep the model ID configurable so the next lifecycle change does not require a code release.
Anthropic’s lifecycle documentation states: “Deprecated models are likely to be less reliable than active models.” — Anthropic, Model deprecations documentation. Treat a deprecation notice as migration work, not as a reason to add more retries.
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Direct API versus Amazon Bedrock: extra failure modes
The same Claude family can be exposed through Anthropic’s direct API or through Amazon Bedrock, but the transport and routing rules differ. First identify which path produced the error; applying direct-API limits to Bedrock can lead to the wrong fix.
Inference-profile routing
Newer Bedrock models may require an inference-profile ID or ARN instead of a base model ID. If a previously valid model identifier starts producing not-found or routing errors after a model change, verify the required inference profile for the selected AWS region and API operation.
Bedrock’s 20 MB payload ceiling
Bedrock limits request payloads to 20 MB. That ceiling can reject a document or collection of images before Claude’s token window is relevant. Measure the serialized Bedrock request, remove or split large content, and do not assume that a 32 MB direct Messages request will also work through Bedrock.
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Global and regional endpoints
Global Bedrock endpoints dynamically route requests for availability. Regional endpoints keep data in a chosen geography. Anthropic documents a 10% pricing premium for Sonnet 4.5 and future models when using regional endpoints. Choose the endpoint type deliberately: routing and data-location requirements can matter more than the small operational difference in configuration.
A practical debugging sequence
Use this order when an integration fails, especially when the symptom is simply “Claude stopped working.”
- Capture the complete failure. Record HTTP status, Anthropic error type, message, request ID, model ID, endpoint, and timestamp. Include the worker or deployment version, but redact API keys and confidential prompt data.
- Classify the status. Separate client-fixable errors (400, 401, 402, 403, 404, 409, and 413) from transient provider errors (500, 504, and 529) and limit-related 429 responses.
- Check 429 evidence. Inspect rate headers and retry guidance, then compare Console usage, billing, and tier or workspace spending status. Do not wait on a cap that has no retry-after guidance.
- Diff 400 requests. Compare prefill, thinking blocks, thinking mode, sampling parameters, roles, content blocks, and model-specific fields against the current model rules.
- Measure 413 payloads. Calculate serialized bytes and compare them with the direct or Bedrock ceiling. Split or compress before trying again.
- Apply bounded retries. Retry 500, 504, and 529 with exponential backoff, jitter, and a hard attempt limit. Use streaming for long Messages API responses where appropriate.
- Check lifecycle status. If failures began after a configuration or model change, verify that the model ID is active and migrate it if necessary.
- Verify Bedrock routing. For Bedrock requests, confirm inference-profile requirements, region, endpoint type, and the 20 MB limit.
Designing reliable Claude error handling
Classify before retrying
Put status classification in one shared client layer so every service follows the same policy. A retryable result should carry the reason, attempt number, and next delay; a non-retryable result should surface the corrective message to the operator or calling service.
Keep retries bounded and observable
Set a maximum number of attempts and a maximum elapsed time. Add jitter to concurrent workers, and stop when the deadline is reached. Log each attempt’s status and request ID, along with the selected model and endpoint, so a provider-support case can be correlated without exposing the prompt.
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Rate throttling can be reduced with concurrency control, while spending caps, billing errors, permissions, and retired models require an account or configuration change. Route those classes to different alerts and runbooks so an automated retry loop does not mask a problem that needs an administrator.
Test failure paths deliberately
Use mocked responses or a staging integration to verify that your client handles each documented status, honors a retry delay, stops on a spending-cap 429, records request IDs, and reports a model migration clearly. Test large serialized payloads as bytes and include Bedrock’s smaller ceiling when that path is supported.
Quick Recap
Quick checklist
- Have you recorded the HTTP status, error type, message, request ID, model, endpoint, and timestamp?
- For 429, did you distinguish temporary throttling from a usage-tier or workspace spending cap?
- For 400, did you check prefill, thinking blocks, thinking settings, and sampling parameters for the exact model?
- For 413, did you measure serialized bytes rather than only tokens?
- For 500, 504, or 529, are retries bounded, exponential, jittered, and limited to transient failures?
- For a sudden model failure, did you check active, legacy, deprecated, and retired lifecycle status?
- For Bedrock, did you verify inference-profile routing, region, endpoint type, and the 20 MB payload limit?
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