Choosing the Right Model

// Good use case for Haiku: Simple classification
const job = await api.runModelInference({
  data_plane_id: 'dp_your_data_plane_id',
  model: 'anthropic.claude-haiku-4.5',
  messages: [
    { role: 'user', text: 'Is this email spam? Subject: "You won $1M!"' }
  ],
  inference_config: {
    output_format_schema: {
      type: 'object',
      properties: {
        is_spam: { type: 'boolean' },
        confidence: { type: 'number', minimum: 0, maximum: 1 }
      },
      required: ['is_spam', 'confidence']
    }
  }
});

// Good use case for Sonnet: Content analysis
const job = await api.runModelInference({
  data_plane_id: 'dp_your_data_plane_id',
  model: 'anthropic.claude-sonnet-4.5',
  messages: [
    { role: 'system', text: 'Analyze dataset descriptions for quality and completeness.' },
    { role: 'user', text: 'Dataset: Customer transactions\nColumns: user_id, amount, date, category' }
  ],
  inference_config: {
    output_format_schema: {
      type: 'object',
      properties: {
        summary: { type: 'string' },
        completeness_score: { type: 'number', minimum: 0, maximum: 1 },
        missing_elements: { type: 'array', items: { type: 'string' } },
        suggestions: { type: 'array', items: { type: 'string' } }
      },
      required: ['summary', 'completeness_score']
    }
  }
});

// Good use case for Opus: Complex analysis
const job = await api.runModelInference({
  data_plane_id: 'dp_your_data_plane_id',
  model: 'anthropic.claude-opus-4.5',
  messages: [
    {
      role: 'system',
      text: 'You are a data governance expert. Analyze datasets for compliance risks.'
    },
    {
      role: 'user',
      text: `Analyze this dataset schema for privacy compliance:
        - email (string)
        - phone (string)
        - purchase_history (array)
        - ip_address (string)
        - device_fingerprint (string)`
    }
  ],
  inference_config: {
    output_format_schema: {
      type: 'object',
      properties: {
        risk_level: { type: 'string', enum: ['low', 'medium', 'high', 'critical'] },
        pii_fields: {
          type: 'array',
          items: {
            type: 'object',
            properties: {
              field: { type: 'string' },
              pii_type: { type: 'string' },
              risk: { type: 'string' }
            },
            required: ['field', 'pii_type', 'risk']
          }
        },
        compliance_concerns: { type: 'array', items: { type: 'string' } },
        recommendations: { type: 'array', items: { type: 'string' } }
      },
      required: ['risk_level', 'pii_fields', 'compliance_concerns', 'recommendations']
    }
  }
});

async function compareModels(prompt: string, schema: object) {
  const models = [
    'anthropic.claude-haiku-4.5',
    'anthropic.claude-sonnet-4.5',
    'anthropic.claude-opus-4.5'
  ];

  const results = await Promise.all(
    models.map(async (model) => {
      const start = Date.now();
      const job = await api.runModelInference({
        data_plane_id: 'dp_your_data_plane_id',
        model,
        messages: [{ role: 'user', text: prompt }],
        inference_config: { output_format_schema: schema }
      });

      const result = await waitForJob(job.id);
      const duration = Date.now() - start;

      return {
        model,
        duration,
        tokens: result.result?.usage.total_tokens,
        output: result.result?.structured_output
      };
    })
  );

  console.table(results.map(r => ({
    model: r.model,
    duration_ms: r.duration,
    tokens: r.tokens
  })));

  return results;
}