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Tutorial 05 — Comparing Two Models

By the end you'll have A/B-tested two Claude models on the same task through the experiment layer and read the cross-variant comparison report. Agents are stochastic, so eyeballing two separate runs is noise — the experiment layer runs both arms under identical conditions and scores them side by side. ~10 minutes.

Prerequisites

  • A working coder-eval checkout with an API key configured — see Tutorial 01. This tutorial uses the built-in tasks/hello_date.yaml.

1. Define the experiment

An experiment runs the same task once per variant and reports the arms side by side — one config file, no copy-pasted tasks. Create experiments/haiku-vs-sonnet.yaml:

experiment_id: haiku-vs-sonnet
description: "Compare Haiku vs Sonnet on the same task"

defaults:
  agent:
    type: claude-code
    permission_mode: bypassPermissions

variants:
  - variant_id: haiku
    agent:
      model: claude-haiku-4-5-20251001
  - variant_id: sonnet
    agent:
      model: claude-sonnet-4-6

A variant declares only what differs — here just agent.model. Everything else merges in from defaults and the task itself, so both arms are identical except for the model. (The repo ships experiments/model-comparison.yaml as a Sonnet-vs-Opus version of the same shape.)

2. Run both arms

uv run coder-eval run tasks/hello_date.yaml -e experiments/haiku-vs-sonnet.yaml

Bare names resolve too: -e haiku-vs-sonnet finds experiments/haiku-vs-sonnet.yaml.

3. Read the comparison

The run writes the cross-variant report automatically when it finishes — there's no separate report command for experiments:

cat runs/latest/experiment.md

It ranks the variants by weighted score and shows per-task results, durations, and token usage side by side. Drill down from there:

  • runs/latest/experiment.json — the full result data.
  • runs/latest/<variant_id>/variant.md (e.g. runs/latest/haiku/variant.md) — per-variant aggregate detail.

4. Add statistical power (optional)

Agents are stochastic — one run per arm is noise, not signal. Repeat each arm to see variance instead of a point estimate:

uv run coder-eval run tasks/hello_date.yaml -e haiku-vs-sonnet --repeats 5

(or set repeats: 5 under defaults: in the experiment file). The report then aggregates per-replicate scores per variant.

Where to go deeper

  • Variants, prompt mutations, replicates, skill on/off comparisons → A/B Experiments
  • Write the task you're comparing on → Tutorial 04
  • Full CLI & config reference → User Guide