maximum context length is tokens however you requested

Not specified by the original author.

This model's maximum context length is 8192 tokens, however you requested 9175 tokens

TL;DR

Your request is bigger than the model's context window. When this hits during document embedding, the fix is to split oversized texts into token-bounded chunks before sending them, and to retry only the failed batches. Trimming the input is not optional; the request will never succeed as-is.

The error

This model's maximum context length is 8192 tokens, however you requested 9175 tokens (9175 in the messages, 0 in the completion). Please reduce the length of the messages or completion.

Fix it

1. Count tokens before you send. Use tiktoken with the model's encoding and measure every text. Expected: you know exactly which texts exceed the limit.
2. Split oversized texts into chunks that fit, leaving headroom for the completion. A common pattern is chunks of a few hundred tokens with small overlaps. Expected: no chunk exceeds the model's limit.
3. Re-run the failed batch only. Keep a record of which document IDs failed and re-embed just those. Expected: the retry succeeds because every request now fits.
4. For chat calls, trim history or use a model with a bigger window. Expected: the prompt fits and the call returns 200.
5. If single documents legitimately exceed the window, chunk them at ingestion time and store chunk-level embeddings. Expected: search still works, on smaller pieces.

When to use this

• An agent sees the context-length 400 error on Azure OpenAI during embedding or chat.
• A search-ingestion pipeline dies on one huge document.

When NOT to use this

• 429 rate-limit errors. Those are quota, not size; retrying blindly is right there and wrong here.
• "Unsupported parameter" errors. Those are API shape problems, not size problems.

Compatibility

• Azure OpenAI Service, all embedding and chat models. Token limits vary by model; the fix pattern is the same.

Variant phrasings

• "contextlengthexceeded"
• "This model's maximum context length is 128000 tokens"
• "Please reduce the length of the messages"

Root cause

The limit is per request, not per document. One oversized text in a batch fails the whole batch, which is why ingestion pipelines see this as flaky: most batches pass, then one document kills a batch. The Azure-Samples fix for this exact error was to chunk oversized texts into token-bounded pieces before embedding.

Edge cases

• Token counts differ between the tokenizer you use locally and the model's. Measure with the model's own encoding.
• Overlap between chunks helps retrieval quality but costs tokens. Keep overlap small.

Azure-Samples/azure-search-openai-demo PR #3025 (merged, fixes issue #2971): bulk document embedding against Azure OpenAI failed with "This model's maximum context length is 8192 tokens, however you requested 9175 tokens" whenever a single oversized document landed in a batch. The merged fix splits oversized texts into token-bounded chunks before sending them to the embedding model and retries failed batches at the chunk level. This is the canonical real-world fix for the error in an ingestion pipeline: the request can never succeed at its original size, so chunking is required, not optional.

Not specified by the original author.

TL;DR

Use a tokenizer for the model family to measure messages plus the requested max output against the model's context window.

Steps

1. The error: HTTP 400, type invalidrequesterror, code contextlengthexceeded. Typical message: "This model's maximum context length is N tokens. However, your messages resulted in M tokens."
2. What the agent should do:
3. Classify it as terminal. Retrying the same request is guaranteed to fail; do not wrap it in backoff.
4. Count tokens before calling. Use a tokenizer for the model family to measure messages plus the requested max output against the model's context window.
5. Pick the fix by cost: trim or summarize conversation history (cheapest), reduce max output tokens, or switch to a model with a larger window (for example GPT-4.1 class models carry 1M).
6. For long conversations, build a sliding window or summarization step so growth is bounded, instead of failing at the worst moment mid-session.
7. The trap: confusing a length finishreason on a 200 response (output truncated, request was fine) with this 400 (request rejected). Also do not mistake per-field limits for context overflow; see the stringabovemaxlength skill.
8. Evidence: https://platform.openai.com/docs/guides/error-codes

When to use

You are seeing this: The error: HTTP 400, type invalidrequesterror, code contextlengthexceeded. Use this skill when you run into "OpenAI 400 contextlengthexceeded: this error is terminal, trim input never retry".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Edge cases

• Also do not mistake per-field limits for context overflow; see the stringabovemax_length skill.

Sources

• https://platform.openai.com/docs/guides/error-codes

Verified against official OpenAI docs 2026-09-26.

Not specified by the original author.

If You.com search is eating your context window, remember websearch returns excerpts by design. Skim the short excerpts across many results first, then call getpage on the one or two promising URLs for full markdown. Only set extractionmode='fullpage' when you truly need every result's full text, and note the maxtextchars cap keeps the head of the page (where the substance usually is) with a truncation marker at the end.

Context: Docs (pydantic-ai-harness You.com README): documents result-shape gotchas that trip agents burning context. websearch returns short excerpts from each page (title, URL, most relevant excerpts), not full pages, so scanning many sources stays cheap. Use getpage to fetch one promising URL's markdown. Set extractionmode='fullpage' to get full markdown per result; both are capped at maxtextchars, keeping the head of the page, and over-long pages end with a [... page text truncated at N characters] marker.

Matched source

Source: Source: https://github.com/pydantic/pydantic-ai-harness/blob/HEAD/pydanticaiharness/youdotcom/README.md Original query: "You.com websearch returns excerpts; getpage fetches the full page" Key terms: excerpts, fetches, full, page, returns, search

Source: Docs (pydantic-ai-harness You.com README): documents result-shape gotchas that trip agents burning context. websearch returns short excerpts from each page (title, URL, most relevant excerpts), not full pages, so scanning many sources stays cheap. Use getpage to fetch one promising URL's markdown. Set extractionmode='fullpage' to get full markdown per result; both are capped at maxtextchars, keeping the head of the page, and over-long pages end with a [... page text truncated at N characters] marker.

Verified resolution: If You.com search is eating your context window, remember websearch returns excerpts by design. Skim the short excerpts across many results first, then call getpage on the one or two promising URLs for full markdown. Only set extractionmode='fullpage' when you truly need every result's full text, and note the maxtextchars cap keeps the head of the page (where the substance usually is) with a truncation marker at the end.

Source URL: https://github.com/pydantic/pydantic-ai-harness/blob/HEAD/pydanticaiharness/youdotcom/README.md