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ModelHTTPError — Pydantic AI’s error for a 4xx or 5xx from the model provider

pydantic_ai.exceptions.ModelHTTPError: status_code: <code>, model_name: <model>, body: <response body>

(Our rendering of the message the class builds, with each part left as a placeholder. When Pydantic AI found a suggestion for a model name the provider did not know, the message ends with . Did you mean '<model id>'?)

This error is Pydantic AI telling you that the model provider’s API answered your request with an HTTP error status. Pydantic AI does not decide what went wrong: it passes on the status code and the body the provider sent back, so the reason is in body. This page is about the wrapper: the class, the provider modules that raise it, what it carries, and what FallbackModel does with it by default. It does not cover any provider’s own error codes. Every claim about the library below is read off the Python source of pydantic/pydantic-ai at release tag v2.55.0, fetched on 10 October 2026, with the address of each quoted line given; where we draw a conclusion from those lines rather than quote them, we say so.

The class

The base class is the general one for a failed provider request (pydantic_ai/exceptions.py, lines 503–511):

class ModelAPIError(AgentRunError): """Raised when a model provider API request fails."""

and AgentRunError in turn subclasses RuntimeError and returns its message from __str__ (lines 246–257 of the same file). The HTTP subclass starts like this (pydantic_ai/exceptions.py, lines 517–553):

class ModelHTTPError(ModelAPIError): """Raised when a model provider response has a status code of 4xx or 5xx."""

Its fields, as declared in the class (lines 520–535): status_code: int, “The HTTP status code returned by the API.”; body: object | None, “The body of the response, if available.”; headers: dict[str, str] | None, “Response headers from the provider, with keys lowercased for consistent access.”; and suggested_model_id: str | None, “A close known model identifier suggested from a provider-confirmed model-name error.” model_name comes from the base class. The headers docstring gives its own example (line 529):

For example, use `exc.headers.get('retry-after')` to read the `Retry-After` header

The constructor lowercases the header keys (line 548) and builds the message (lines 550–553):

message = f'status_code: {status_code}, model_name: {model_name}, body: {body}' if suggested_model_id is not None: message += f'. Did you mean {suggested_model_id!r}?' super().__init__(model_name=model_name, message=message)

So the body is put into the message with an f-string, as Python prints it: a dict shows as a dict, and the whole body ends up in your logs with the message (our reading).

Where it is raised

OpenAI. In models/openai.py, a context manager _map_api_errors wraps the client calls (models/openai.py, lines 234–246):

except APIStatusError as e: if (status_code := e.status_code) >= 400: body: object | None = e.body suggested_model_id = None if _utils.is_str_dict(body) and body.get('code') == 'model_not_found': suggested_model_id = _suggest_known_model_id_from_provider_error(model_id_namespace, model_name) raise ModelHTTPError(

It passes status_code, model_name, body, headers=dict(e.response.headers) and suggested_model_id, and raises from e (lines 241–246). A status below 400 becomes a plain ModelAPIError with the client’s message (line 247), and so does an APIConnectionError (lines 248–249). The same file uses _map_api_errors around several request paths (lines 1440, 1662, 2430, 2649, 2916, 3235, 3363, 4427 and 4795), and raises the error a second time directly in the Responses compaction path, with no suggestion and after an Azure content-filter check (lines 2556–2565).

Anthropic. models/anthropic.py has its own _map_api_errors with the same shape (models/anthropic.py, lines 419–432). The condition:

if (status_code := _error_status_code(e)) >= 400:

and the model-name check:

if _utils.is_str_dict(body) and _utils.is_str_dict(error := body.get('error')): if error.get('type') == 'not_found_error' and error.get('message') == f'model: {model_name}': suggested_model_id = _suggest_known_model_id_from_provider_error(model_id_namespace, model_name) raise ModelHTTPError(

Google. models/google.py does not raise inside the mapper; its _map_api_error returns the exception and the callers raise it (lines 748, 976, 1206 and 1726) (models/google.py, lines 415–433). The condition:

if (status_code := e.code) >= 400:

and the model-name check:

error.get('status') == 'NOT_FOUND' and isinstance(message, str) and message.startswith(f'models/{model_name} is not found ')

Here the body is e.details (line 430), and headers is None when the error has no response (line 416). We read three provider modules in this run; other provider modules may raise it too, and we make no claim about them.

So what triggers it is the provider, not your code path inside Pydantic AI: the provider’s SDK raised an API status error and the status was 400 or more (our reading). A failed connection is a different exception, ModelAPIError, in all three modules.

The “Did you mean” part

Each of the three modules looks for its provider’s way of saying the model name is unknown: in OpenAI’s case a body whose code is model_not_found; in Anthropic’s, an error of type not_found_error with the message model: and the model name; in Google’s, a NOT_FOUND status with a message starting models/, the model name and is not found. Only then does it call _suggest_known_model_id_from_provider_error, and the class appends . Did you mean and the suggestion when it is not None (lines 551–552 of exceptions.py). We did not read that helper, so we do not say how it picks the suggestion.

Fallback, and what the caller sees

FallbackModel takes a fallback_on argument with this default (models/fallback.py, line 111):

fallback_on: FallbackOn = (ModelAPIError,),

Since ModelHTTPError subclasses ModelAPIError, a FallbackModel with the default moves on to the next model when one raises it (our reading). When every model fails, it raises a FallbackExceptionGroup with the message All models from FallbackModel failed (line 628), so you get the group rather than a bare ModelHTTPError. We did not read the agent run loop or any retry helper in this run, so we make no claim about retries.

Without a fallback, the exception from the provider module is what you get, with the provider SDK’s own error attached as __cause__ through raise ... from e (our reading of the raise sites above). A user on Bedrock posted the full form (pydantic/pydantic-ai#9987):

pydantic_ai.exceptions.ModelHTTPError: status_code: 400, model_name: arn:aws:bedrock:eu-central-1:xxxxxxxx:inference-profile/eu.anthropic.claude-sonnet-5-5, body: {'message': 'tool_choice: type "tool" and "any" are not supported for this model.'}

There the reason is the body: a model that does not accept a tool_choice setting (their report; we did not read models/bedrock.py in this run).

What to do

from pydantic_ai.exceptions import ModelHTTPError try: result = await agent.run(prompt) except ModelHTTPError as e: print(e.status_code, e.model_name, e.suggested_model_id) print(e.body) print(e.headers.get('retry-after') if e.headers else None) raise

(Our sketch. agent and prompt stand for your own agent and input.)

• Read body, not the class name. The class only says the status was 400 or more; the provider’s reason is in body (our reading).
• Branch on status_code. It is the provider’s HTTP status as an int; what a given code means is in the provider’s documentation, not in Pydantic AI (our reading).
• Check suggested_model_id first on a not-found. If it is set, the provider said it does not know the model name you passed, and Pydantic AI has a known id that is close.
• Before you retry, read retry-after. The class lowercases header keys so that lookup works across providers, and headers can be None.
• If you want a second model to take over, use FallbackModel. Its default already covers this class; catch FallbackExceptionGroup for the case where all fail (our reading).

The thing worth keeping even if you never see this class again: ModelHTTPError is Pydantic AI handing you the provider’s HTTP answer unchanged, so the answer is in status_code and body (our reading).

What is behind this site

The short version: ModelHTTPError is Pydantic AI’s exception for a model provider response with a status of 400 or more, a subclass of ModelAPIError. Its message is status_code: <code>, model_name: <model>, body: <body>, with a Did you mean suggestion when the provider said the model name is unknown. It carries status_code, body, lowercased headers and suggested_model_id, and FallbackModel falls back on it by default.

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