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Model options ​

Choose the model when creating the completion adapter, then keep common generation settings on the Anvia request.

Select a model explicitly ​

ts
const model = anthropic.completionModel(
  'claude-sonnet-4-20250514',
)

Known Anthropic model IDs are included for editor autocomplete, while custom strings remain valid. This allows newly released models and compatible endpoints without waiting for a package release.

Avoid relying on the package default. An explicit model ID makes deployments, eval results, and rollbacks reproducible.

List direct API models ​

ts
const inventory = await anthropic.listModels()

for (const model of inventory.data) {
  console.log(model.id)
}

Use model listing for an admin inventory or selection UI. A listed ID does not prove that it supports tools, media, reasoning, or the context limits required by the workflow.

AnthropicVertexClient does not have listModels() because Vertex AI does not expose Anthropic's Models API.

Set portable request options ​

Use Anvia's normalized request fields whenever they cover the behavior:

ts
import { createCompletion } from '@anvia/core'

const result = await createCompletion(model, {
  instructions: 'Answer precisely and state uncertainty.',
  input: 'Summarize the deployment risk.',
  temperature: 0.2,
  maxTokens: 600,
})

temperature, maxTokens, tools, and tool choice are mapped to Anthropic request fields by the adapter. Keeping them normalized makes the calling code easier to move between providers.

Pass Anthropic-specific parameters ​

Use params only for an Anthropic Messages API option that has no Anvia field:

ts
const result = await createCompletion(model, {
  input: 'Draft a release note.',
  maxTokens: 400,
  params: {
    top_p: 0.9,
    stop_sequences: ['</release-note>'],
  },
})

The adapter forwards these values to the provider request. They are provider-specific and may override normalized fields when the same provider key is supplied. Keep them in the model integration layer, type-check their shape against Anthropic's SDK, and add a live test for every parameter the application depends on.

Choose by workload ​

Select and evaluate a model against the actual job rather than a name alone:

  • Test tool selection and argument accuracy for agents.
  • Test source fidelity for image and PDF understanding.
  • Measure latency and token usage for the expected prompt size.
  • Verify the required context and output limits through model metadata and live requests.
  • Pin an exact model ID when behavior must remain stable.

Record the selected provider and model in traces so regressions can be compared across model changes.

Built for Anvia.