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A Model selects the language model that will generate answers to your survey. Its service identifies where EDSL sends the requests; its parameters control supported generation settings, such as response length. An Agent supplies a respondent profile, while the Model supplies the language model answering from that profile. Use one Model to run a study or a ModelList to compare several model configurations on the same inputs. Select from the current catalog because model availability and supported settings change.

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Discover and select models

Model.services() lists inference services; Model.available() lists models you can select. Run each expression in its own notebook cell to display both tables. Discovery may contact provider or Expected Parrot services. The model catalog includes availability and pricing. See Managing Keys for hosted and provider credentials. Model() selects the configured default. Inspect model.model and model.parameters and save the configuration with your study. For a named selection, use Model(model_name, service_name=provider) with values you have verified in the catalog.

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Compare models on matched inputs

Construct a ModelList from your selected configurations and apply it with job.by(models). Hold the questions, scenarios, and agent instructions constant so differences can be traced to model configuration. See Model comparisons. Do not assume settings such as temperature, token limits, or reasoning effort have the same meaning or valid values across providers. Unsupported values can fail at execution time even when a configuration serializes successfully.

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Reasoning and provider configuration

OpenAI adapters accept reasoning settings, with request formatting depending on the adapter. Explicit reasoning_effort is forwarded for provider validation; with the Responses adapter, an explicit effort overrides the effort inside a reasoning dictionary. Supported values depend on the selected model. See Provider configuration for selecting services and inspecting parameters, and the adapter source for implementation details. Record effective settings with results and check provider documentation before adopting a model-specific option.

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Connect to your own model server

Use EDSL’s openai_compatible service to connect to a server that exposes an OpenAI-compatible chat-completions endpoint. Start the server first, then replace MODEL_NAME with a model identifier it serves and SERVER_BASE_URL with its API base URL, including /v1 if required by that server.
If the server requires authentication, set an environment variable containing its key and pass that variable’s name as api_key_env. EDSL saves the variable name with the model configuration, without including its secret value. With a survey you have already constructed, run it through the configured endpoint:
disable_remote_inference=True runs orchestration on your machine; requests go to the configured server, which may be local or remote. disable_remote_cache=True disables Expected Parrot’s remote cache. The CLI equivalent for local orchestration is --local. Supported model parameters and response formats depend on the server and model. Use the server’s own documentation for installation and tuning—for example, the llama.cpp server guide. For an offline execution check, use the test model in the Run your first survey tutorial. Test responses demonstrate the data flow and do not measure model performance.

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Common methods

You can practice configuring multiple models locally:
These test models return fixed answers. Replace them with configurations from the catalog for a real model comparison, then pass the list to job.by(models).

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Narrow the catalog and reuse selections

Model.available(service_name=provider) restricts discovery to a service; add search_term to filter names. Model.search_models(search_term) provides name search. To inspect only services for which you have configured provider keys, use Model.services_with_local_keys(); Model.available_with_local_keys() lists their models. These are discovery methods, not guarantees that a particular API call will succeed. Model.check_working_models(works_with_text=True, works_with_images=True) retrieves Expected Parrot’s working-model catalog with capability filters. It reads catalog information rather than running your survey. Use output_format="scenario_list" when you want catalog rows instead of model configurations. For PDF, video, and audio inputs, choose a model that supports the specific media type; the image filter only describes image support. See Files and media as scenarios for examples, including Google video and audio inputs. A ModelList can be inspected with to_scenario_list() or to_dataset(), filtered with filter(expression), and saved with save("models.ep") for later ModelList.load("models.ep"). ModelList.from_names(names) constructs configurations from a list of model names; use explicit Model(name, service_name=provider) objects when the provider needs to be unambiguous.