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Ensemble runs your prompt through 3 models in parallel, then uses a 4th call to synthesize the best answer. Use it when accuracy matters more than speed or cost.

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Request

{
  "model": "ensemble",
  "messages": [{"role": "user", "content": "Is microservices worth it for a 3-person startup?"}]
}

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Response

{
  "choices": [{"message": {"role": "assistant", "content": "For a 3-person startup..."}}],
  "ensemble": {
    "models": ["gpt-5", "claude-sonnet-4.6", "gemini-3.1-pro"],
    "synthesis": "consensus"
  },
  "cost": {"this_request": "$0.040"}
}

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Variants

Model IDModels usedCost
ensembleGPT-5 + Claude Sonnet 4.6 + Gemini 3.1 Pro$0.04/req
ensemble-qualityGPT-5 + Claude Opus 4.6 + Gemini 3.1 Pro$0.05/req

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When to use

  • High-stakes decisions where hallucinations are costly
  • Getting consensus across model families
  • Verifying that an answer isn’t model-specific bias
Ensemble accepts all standard chat parameters — temperature, max_tokens, sessions, etc.