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LLM outputs vary from run to run—ask the same question twice and you might get different quality answers. To find out which model actually performs best, you need to test each one multiple times and look at the spread. Variants let you test different models side-by-side. Groups repeat each test so you see the full distribution, not just one lucky or unlucky result.

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Variants

Pass the configurations you want to test: 
import hud

async with hud.eval(variants={"model": ["gpt-4o", "claude-sonnet-4-5"]}) as ctx:
    response = await client.chat.completions.create(
        model=ctx.variants["model"],
        messages=[{"role": "user", "content": "What is 2+2?"}]
    )
    ctx.reward = 1.0 if "4" in response.choices[0].message.content else 0.0

for result in ctx.results:
    print(f"{result.variants}: reward={result.reward}")

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Groups

Run each variant multiple times to get a distribution: 
async with hud.eval(
    variants={"model": ["gpt-4o", "claude-sonnet-4-5"]},
    group=5  # 10 runs total: 2 models × 5 each
) as ctx:
    ...
The hud.eval manager will parallelize your evals automatically and show the distribution across all your runs on hud.ai.

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Remote Rollouts

Once you’ve deployed an environment and made an evalset, run evals via CLI: 
hud eval my-evalset --model gpt-4o --group-size 5
Or run directly on the platform—see Running at Scale.