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Use Weave Leaderboards to evaluate and compare multiple models across multiple metrics and measure accuracy, generation quality, latency, or custom evaluation logic. A leaderboard helps you visualize model performance in a central location, track changes over time, and align on team-wide benchmarks. Leaderboards are ideal for:
  • Tracking model performance regressions
  • Coordinating shared evaluation workflows

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Create a Leaderboard

You can create a leaderboard via the Weave UI or programmatically.

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Using the UI

To create and customize leaderboards directly in the Weave UI:
  1. In the Weave UI, Navigate to the Leaders section. If it’s not visible, click More → Leaders.
  2. Click + New Leaderboard.
  3. In the Leaderboard Title field, enter a descriptive name (e.g., summarization-benchmark-v1).
  4. Optionally, add a description to explain what this leaderboard compares.
  5. Add columns to define which evaluations and metrics to display.
  6. Once you’re happy with the layout, save and publish your leaderboard to share it with others.

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Add columns

Each column in a leaderboard represents a metric from a specific evaluation. To configure a column, you specify:
  • Evaluation: Select an evaluation run from the dropdown (must be previously created).
  • Scorer: Choose a scoring function (e.g., jaccard_similarity, simple_accuracy) used in that evaluation.
  • Metric: Choose a summary metric to display (e.g., mean, true_fraction, etc.).
To add more columns, click Add Column. To edit a column, click its three-dot menu (⋯) on the right. You can:
  • Move before / after – Reorder columns
  • Duplicate – Copy the column definition
  • Delete – Remove the column
  • Sort ascending – Set the default sort for the leaderboard (click again to toggle descending)

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Python

Looking for a complete, runnable code sample? See the End-to-end Python example.
To create and publish a leaderboard:
  1. Define a test dataset. You can use the built-in Dataset, or define a list of inputs and targets manually: 
    dataset = [
    {"input": "...", "target": "..."},
    ...
]
  2. Define one or more scorers: 
    @weave.op
def jaccard_similarity(target: str, output: str) -> float:
    ...
  3. Create an Evaluation: 
    evaluation = weave.Evaluation(
    name="My Eval",
    dataset=dataset,
    scorers=[jaccard_similarity],
)
  4. Define models to be evaluated: 
    @weave.op
def my_model(input: str) -> str:
    ...
  5. Run the evaluation: 
     async def run_all():
     await evaluation.evaluate(model_vanilla)
     await evaluation.evaluate(model_humanlike)
     await evaluation.evaluate(model_messy)

asyncio.run(run_all())
  6. Create the leaderboard: 
    spec = leaderboard.Leaderboard(
    name="My Leaderboard",
    description="Evaluating models on X task",
    columns=[
        leaderboard.LeaderboardColumn(
            evaluation_object_ref=get_ref(evaluation).uri(),
            scorer_name="jaccard_similarity",
            summary_metric_path="mean",
        )
    ]
)
  7. Publish the leaderboard. 
    weave.publish(spec)
  8. Retrieve the results: 
    results = leaderboard.get_leaderboard_results(spec, client)
print(results)

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End-to-End Python example

The following example uses Weave Evaluations and creates a leaderboard to compare three summarization models on a shared dataset using a custom metric. It creates a small benchmark, evaluates each model, scores each model with Jaccard similarity, and publishes the results to a Weave leaderboard. 
import weave
from weave.flow import leaderboard
from weave.trace.ref_util import get_ref
import asyncio

client = weave.init("leaderboard-demo")

dataset = [
    {
        "input": "Weave is a tool for building interactive LLM apps. It offers observability, trace inspection, and versioning.",
        "target": "Weave helps developers build and observe LLM applications."
    },
    {
        "input": "The OpenAI GPT-4o model can process text, audio, and vision inputs, making it a multimodal powerhouse.",
        "target": "GPT-4o is a multimodal model for text, audio, and images."
    },
    {
        "input": "The W&B team recently added native support for agents and evaluations in Weave.",
        "target": "W&B added agents and evals to Weave."
    }
]

@weave.op
def jaccard_similarity(target: str, output: str) -> float:
    target_tokens = set(target.lower().split())
    output_tokens = set(output.lower().split())
    intersection = len(target_tokens & output_tokens)
    union = len(target_tokens | output_tokens)
    return intersection / union if union else 0.0

evaluation = weave.Evaluation(
    name="Summarization Quality",
    dataset=dataset,
    scorers=[jaccard_similarity],
)

@weave.op
def model_vanilla(input: str) -> str:
    return input[:50]

@weave.op
def model_humanlike(input: str) -> str:
    if "Weave" in input:
        return "Weave helps developers build and observe LLM applications."
    elif "GPT-4o" in input:
        return "GPT-4o supports text, audio, and vision input."
    else:
        return "W&B added agent support to Weave."

@weave.op
def model_messy(input: str) -> str:
    return "Summarizer summarize models model input text LLMs."

async def run_all():
    await evaluation.evaluate(model_vanilla)
    await evaluation.evaluate(model_humanlike)
    await evaluation.evaluate(model_messy)

asyncio.run(run_all())

spec = leaderboard.Leaderboard(
    name="Summarization Model Comparison",
    description="Evaluate summarizer models using Jaccard similarity on 3 short samples.",
    columns=[
        leaderboard.LeaderboardColumn(
            evaluation_object_ref=get_ref(evaluation).uri(),
            scorer_name="jaccard_similarity",
            summary_metric_path="mean",
        )
    ]
)

weave.publish(spec)

results = leaderboard.get_leaderboard_results(spec, client)
print(results)

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View and interpret the Leaderboard

After the script finishes running, view the leaderboard:
  1. In the Weave UI, go to the Leaders tab. If it’s not visible, click More, then select Leaders.
  2. Click on the name of your leaderboard—e.g. Summarization Model Comparison.
In the leaderboard table, each row represents a given model (model_humanlike, model_vanilla, model_messy). The mean column shows the average Jaccard similarity between the model’s output and the reference summaries.
A leaderboard in the Weave UI
For this example:
  • model_humanlike performs the best, with ~46% overlap.
  • model_vanilla (a naive truncation) gets ~21%.
  • model_messy an intentionally bad model, scores ~2%.