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litellm.ContextWindowExceededError — LiteLLM read the provider’s error as “the input is too long”

litellm.ContextWindowExceededError: litellm.BadRequestError: ContextWindowExceededError: <Provider>Exception - <provider message>

(The error as str(e) gives it when it comes from LiteLLM’s generic branch, assembled by us from three prefixes quoted as source below, with our placeholders in angle brackets. Other branches put a different text after litellm.BadRequestError: .)

In its exception mapper, LiteLLM raises this after the provider call failed: it maps the provider’s exception to its own classes, and when the error text contains one of a list of “context too long” phrases, the class it picks is ContextWindowExceededError, a 400 BadRequestError. Every claim about the library below is read off the Python source of BerriAI/litellm at tag v1.104.2, fetched on 10 October 2026, with the line of each quote given; where we draw a conclusion from those lines rather than quote them, we say so.

The generic raise

In litellm/litellm_core_utils/exception_mapping_utils.py, inside _map_openai_exception (defined at line 275), lines 317–319:

elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): raise ContextWindowExceededError( message=f"ContextWindowExceededError: {exception_provider} - {message}",

The raise passes llm_provider=custom_llm_provider, and model=model, (lines 320–321). In the same function, exception_provider is "OpenAI" + "Exception" for openai and otherwise the provider name with its first letter upper-cased plus Exception (lines 302–305). exception_type (line 2349) sends openai, text-completion-openai, custom_openai, mistral, runwayml and the providers in litellm.openai_compatible_providers to this function (the condition at lines 2458–2465). Only the rate-limit check at line 307 comes before it.

What makes the condition true

is_error_str_context_window_exceeded (line 82 of the same file) lowercases the error string and first rules out two parameter errors, lines 86–91:

_error_str_lowercase: Final = error_str.lower() # Exclude param validation errors (e.g. OpenAI "user" param max 64 chars) if "string_above_max_length" in _error_str_lowercase: return False if "invalid 'user'" in _error_str_lowercase and "string too long" in _error_str_lowercase: return False

Then it returns True if any of the nine entries of known_exception_substrings (lines 92–102) is in the lowercased string — among them "this model's maximum context length is",, "is longer than the model's context length", and "exceeds the available context size", # llama.cpp/Lemonade — or if it contains both "current length is" and "while limit is" (lines 108–109, commented as a Cerebras pattern), or both "maximum input length is" and "tokens" (lines 111–112). Otherwise it returns False (line 114). So the class says what the provider’s text matched, not that LiteLLM counted anything (our reading).

The other raise sites in that file

A grep of the file for raise ContextWindowExceededError finds 16 lines. Besides line 318, each sits in a provider’s mapping function: _map_anthropic_exception (542), _map_replicate_exception (658), _map_openai_like_exception (758), _map_bedrock_exception (885, 899), _map_sagemaker_exception (1064), _map_vertex_exception (1168, 1174), _map_cohere_exception (1417), _map_huggingface_exception (1497), _map_ai21_exception (1574), _map_nlp_cloud_exception (1637), _map_together_ai_exception (1731), _map_aleph_alpha_exception (1817) and _map_azure_exception (1974). The checker above is called at lines 317, 540, 757 and 1173, guarding the raises at 318, 542, 758 and 1174; the Anthropic branch also tests its own strings, such as "prompt is too long" (line 538), and the others test only their own, such as "Too many input tokens" (line 897, Bedrock); several build a message with different text, such as message=f"AnthropicError - {error_str}", (line 543).

When you call a LiteLLM proxy as provider litellm_proxy, exception_type first calls extract_and_raise_litellm_exception (lines 2448–2457), which looks for r"litellm\.\w+Error" in the error text (line 232) and raises the LiteLLM class of that name with the whole text as its message (lines 236–245), so a proxy-side ContextWindowExceededError comes back as one on the client too (our reading). Outside this file, among the files we fetched, it is also raised by Router._pre_call_checks (router.py line 12431) when its checks have left no deployment and a context-window check was failed (our reading), with the message litellm._pre_call_checks: Context Window exceeded for given call. and llm_provider="",, and by two mock-testing paths (main.py line 773, router.py line 7550).

Where completion() sends it

In litellm/main.py, completion (line 5113) ends with, lines 6036–6038:

except Exception as e: ## Map to OpenAI Exception raise exception_type(

passing model, custom_llm_provider and the original exception (lines 6038–6044). acompletion (line 400) does the same at lines 709–717. exception_type returns an exception that is already one of LiteLLM’s types unchanged (lines 2357–2358), so an error mapped once is not mapped again (our reading).

The class

In litellm/exceptions.py, the class is at line 535, and the parent, class BadRequestError(openai.BadRequestError):, at line 219. The parent’s constructor sets, lines 231–232:

self.status_code = 400 self.message = f"litellm.BadRequestError: {message}"

and the subclass, after calling it, lines 556–557:

# set after, to make it clear the raised error is a context window exceeded error self.message = f"litellm.ContextWindowExceededError: {self.message}"

That is why the string carries both prefixes. Its __str__ (lines 559–565) returns self.message, plus a retry note when num_retries or max_retries is set.

What it looks like in the field

In rossoctl/cortex#1309, an issue about a different matter (a BYTES column), the reporter quotes the error that prompted it: API Error: 400 litellm.ContextWindowExceededError: litellm.BadRequestError: ContextWindowExceededError: Hosted_vllmException - followed by a vLLM This model's maximum context length is message. That is the shape of line 319 for provider hosted_vllm, and the text matches the first entry quoted above (our reading).

In ititti-es/litellm#1, an issue on a fork of LiteLLM (version 1.103.0 per the report), titled [Bug]: context-limit errors become retriable server errors on /v1/messages, the reporter constructs the class directly, prints Original status: 400, and reports that a streaming adapter on the Anthropic /v1/messages path turned it into a 500. We did not check that adapter.

What the caller sees and can read

• str(e) and e.message — litellm.ContextWindowExceededError: litellm.BadRequestError: followed by the message the raise passed (lines 232 and 557); str(e) adds the retry note when set.
• e.status_code — set to 400 by the class (line 544) and its parent (line 231). We did not fetch openai.BadRequestError, whose constructor the parent calls (lines 249–251).
• e.model, e.llm_provider — what the raise passed (lines 545–546); at line 318 the model you asked for and the resolved provider name; at the Router’s pre-call check, llm_provider is empty.
• e.litellm_debug_info, e.response — the extra information the mapper passed, if any, and the provider’s httpx.Response when it had a request attached, otherwise a stub 400 response (lines 240–248, 121–129).
• Catching it — except litellm.ContextWindowExceededError, before any except litellm.BadRequestError or openai.BadRequestError, which would also catch it (our reading, from lines 219 and 535).

What to do

• Send less. The provider refused the input as too long for the model, so fewer or shorter messages, trimmed conversation history, or fewer retrieved chunks is the direct fix (our reading). Retrying the same request unchanged repeats the same input.
• Or use a model with a larger context window. On a Router, the constructor takes context_window_fallbacks: list = [], (line 785, documented at line 840 as a List of context window fallback options.), and the fallback handler checks if isinstance(e, litellm.ContextWindowExceededError): then if context_window_fallbacks is not None: (lines 7314–7315) before retrying on the fallback group, in litellm/router.py. We say nothing here about any model’s window size.
• Read the tail of the message. After the prefixes is the provider’s own text, which can say which limit was hit, as the vLLM text in #1309 does (our reading).
• If a framework wraps the call, the messages it builds are what grew; trim them there (our reading).

import litellm def call(model: str, messages: list[dict], keep_last: int = 6): # Our sketch: on a context-window error, retry once with older turns dropped. try: return litellm.completion(model=model, messages=messages) except litellm.ContextWindowExceededError as e: print(e.status_code, e.llm_provider, e.model) system = [m for m in messages if m["role"] == "system"] rest = [m for m in messages if m["role"] != "system"] return litellm.completion(model=model, messages=system + rest[-keep_last:])

(Our sketch, not library code, and not run against your version. If the shorter call raises again, the input is still too long; trim further or change model.)

What is behind this site

The short version: litellm.ContextWindowExceededError is a BadRequestError (status 400) that LiteLLM’s exception_type raises when the provider’s error text matches a context-length phrase — via the shared checker (lines 317, 540, 757 and 1173), or a provider branch’s own strings — and that a Router raises when its pre-call check leaves no deployment. str(e) starts litellm.ContextWindowExceededError: litellm.BadRequestError: . Send less, or route to a model with a larger context window, for example with context_window_fallbacks.

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