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Model Comparison

GPT-4o, Claude, Gemini, Llama -- which one is actually best for your use case? Winnow runs your task against every model and produces multi-metric scorecards so you can make the right call on quality, latency, cost, and safety.

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compare_models.pypython

import winnow

# Set up a multi-model comparison experiment
comparison = winnow.create_model_comparison(
    name="support-agent-model-eval",
    task_dataset="customer-support-golden-v2",
    models=[
        {"name": "gpt-4o",        "provider": "openai"},
        {"name": "claude-sonnet",  "provider": "anthropic"},
        {"name": "gemini-2.0-flash", "provider": "google"},
        {"name": "llama-3.1-70b", "provider": "together"},
    ],
    prompt_version="support-agent-v4.1",
    metrics=[
        "quality_score",   # LLM-as-judge (1-5)
        "latency_p50",     # median response time
        "latency_p99",     # tail latency
        "cost_per_call",   # USD per request
        "safety_score",    # toxicity + hallucination
    ],
    evaluator_model="gpt-4o",
    samples=500,
)

# Generate a scorecard
scorecard = comparison.scorecard()
print(scorecard)
# Model            | Quality | P50   | P99   | Cost    | Safety
# gpt-4o           | 4.2     | 380ms | 1.2s  | $0.0035 | 0.97
# claude-sonnet    | 4.4     | 350ms | 0.9s  | $0.0028 | 0.98
# gemini-2.0-flash | 3.9     | 210ms | 0.6s  | $0.0012 | 0.95
# llama-3.1-70b    | 3.7     | 290ms | 1.0s  | $0.0008 | 0.93

Data-Driven Model Selection

Replace guesswork with rigorous, multi-dimensional model evaluation tailored to your exact workload.

Multi-Metric Scorecards

Compare models across quality, latency (p50/p99), cost per call, and safety in a single scorecard. Weight metrics to match your priorities and rank models automatically.

Latency Profiling

Measure time-to-first-token, total generation time, and streaming throughput. Winnow captures p50, p90, and p99 latencies so you understand tail performance, not just averages.

Cost Modeling

Track exact cost per call including input tokens, output tokens, and any API surcharges. Project monthly spend at your traffic volume for each model under consideration.

Model Swap Evaluator

Thinking of switching models? Run a swap evaluation that replays your production traffic through the new model and compares outputs head-to-head against your current choice.

New models drop every week. Stay ready.

The model landscape changes fast. Winnow lets you re-run your comparison suite whenever a new model launches so you always know whether it's worth switching -- or if your current choice is still the best fit.

Side-by-side output comparison with LLM-as-judge scoring

Weighted composite scoring to match your business priorities

Replay production traffic against candidate models

Automated alerts when a new model beats your baseline

Export scorecards to share with stakeholders

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