Add OpenRouterModel as OpenAIChatModel subclass

[ajac-zero]

Hi! This pull request takes a shot at implementing a dedicated OpenRouterModel model, for issue #2936.

The differentiator for this PR is that this implementation minimizes code duplication as much as possible by delegating the main logic to OpenAIChatModel, such that the new model class serves as a convenience layer for OpenRouter specific features.

The main thinking behind this solution is that as long as the OpenRouter API is still fully accessible via the openai package, it would be inefficient to reimplement the internal logic using this same package again. We can instead use hooks to achieve the requested features.

I would like to get some thoughts on this implementation before starting to update the docs.

Addressed issues

1. Store OpenRouter provider metadata in ModelResponse vendor details #1849

Provider metadata can now be accessed via the 'downstream_provider' key in ModelMessage.provider_details:

from pydantic_ai import ModelRequest
from pydantic_ai.direct import model_request_sync
from pydantic_ai.models.openrouter import OpenRouterModel

model = OpenRouterModel('moonshotai/kimi-k2-0905')

response = model_request_sync(model, [ModelRequest.user_text_prompt('Who are you')])

assert response.provider_details is not None
print(response.provider_details['downstream_provider'])  # <-- Final provider that was routed to
# Output: AtlasCloud

2. Can I get thinking part from openrouter provider using google/gemini-2.5-pro? #2999

The new OpenRouterModelSettings allows for the reasoning parameter by OpenRouter, the thinking can then be accessed as a ThinkingPart in the model response:

from pydantic_ai import ModelRequest
from pydantic_ai.direct import model_request_sync
from pydantic_ai.models.openrouter import OpenRouterModel, OpenRouterModelSettings

model = OpenRouterModel('google/gemini-2.5-pro')

settings = OpenRouterModelSettings(openrouter_reasoning={'effort': 'high'})

response = model_request_sync(model, [ModelRequest.user_text_prompt('Who are you')], model_settings=settings)

print(response.parts[0])
# Output: ThinkingPart(content='**Identifying the Core Inquiry**\n\nI\'m grappling with the core question: "Who am I?" Initially, I\'m identifying the root of the query. The user wants a fundamental identity explained, and I\'ve begun by pinpointing the key words and associations. AI, specifically. Next step, I\'ll move onto broadening this.\n\n\n**Clarifying My Nature**\n\nI\'m now dissecting the definition of "language model," focusing on what that *means* in practical terms. I\'ve moved past simply stating the term and am now delving into how my functions—answering, generating, translating—are executed. This requires explaining my training on vast datasets and my lack of personal experience, which is key to the identity question. I am trying to find the right framing for this complex process.\n\n\n**Formulating a Direct Response**\n\nI\'m now trying to directly answer the question, avoiding technical jargon where possible. I\'m organizing my response. The essential elements have been identified: My nature, my capabilities, and what I *cannot* do. I\'m thinking of ways to explain these facts in a concise, accessible format, focusing on clarity for the user.\n\n\n**Constructing a Detailed Answer**\n\nI\'m now translating the structured plan into actual sentences. I\'m working on the opening, the "I am..." statement, and aiming for a direct, clear tone. Then, I am carefully crafting the explanation of my capabilities and limitations to avoid misunderstandings. I\'m actively searching for concise and impactful language.\n\n\n**Drafting the Final Response**\n\n\\n\\n\n\nI\'m now integrating all the elements I\'ve identified. I\'m beginning the final draft. I\'m focusing on flow and readability, weaving the key points—my nature, my origin, my abilities, and my constraints—into a cohesive narrative. The goal is a concise and informative self-description, tailored to the user\'s inquiry.\n\n\n', id='reasoning', provider_name='openrouter')

3. Handle error response from OpenRouter as exception instead of validation failure #2323, OpenRouter uses non-compatible finish reason #2844

These are dependent on some downstream logic from OpenRouter or their own downstream providers (that a response of type 'error' will have a >= 400 status code), but for most cases I would say it works as one would expect:

from pydantic_ai import ModelHTTPError, ModelRequest
from pydantic_ai.direct import model_request_sync
from pydantic_ai.models.openrouter import OpenRouterModel, OpenRouterModelSettings

model = OpenRouterModel('google/gemini-2.5-pro')

settings = OpenRouterModelSettings(
    openrouter_preferences={'only': ['azure']}  # Gemini is not available in Azure; Guaranteed failure.
)

try:
    response = model_request_sync(model, [ModelRequest.user_text_prompt('Who are you')], model_settings=settings)
except ModelHTTPError as e:
    print(e)
# status_code: 404, model_name: google/gemini-2.5-pro, body: {'message': 'No allowed providers are available for the selected model.', 'code': 404}

4. Add OpenRouterModel #1870 (comment)

Add some additional type support to set the provider routing options from OpenRouter:

from pydantic_ai import ModelRequest
from pydantic_ai.direct import model_request_sync
from pydantic_ai.models.openrouter import OpenRouterModel, OpenRouterModelSettings

model = OpenRouterModel('moonshotai/kimi-k2-0905')

settings = OpenRouterModelSettings(
    openrouter_preferences={
        'order': ['moonshotai', 'deepinfra', 'fireworks', 'novita'],
        'allow_fallbacks': True,
        'require_parameters': True,
        'data_collection': 'allow',
        'zdr': True,
        'only': ['moonshotai', 'fireworks'],
        'ignore': ['deepinfra'],
        'quantizations': ['fp8'],
        'sort': 'throughput',
        'max_price': {'prompt': 1},
    }
)

response = model_request_sync(model, [ModelRequest.user_text_prompt('Who are you')], model_settings=settings)
assert response.provider_details is not None
print(response.provider_details['downstream_provider'])
# Output: Fireworks