Benchmarks Overview

Evaluate your LLM’s capabilities with standardized benchmark suites

What are Benchmarks?

Benchmarks are standardized tests that measure LLM performance across diverse tasks. Each benchmark uses a well-established academic dataset with known correct answers, allowing you to objectively score your model’s capabilities in areas like knowledge recall, reasoning, commonsense understanding, truthfulness, and code generation.

Avaliar integrates these benchmarks directly into its Python SDK so you can run evaluations, track results over time, and compare models — all from a single platform.

Why Benchmark Your Models?

Understand Capabilities

Quantify what your model can and cannot do across knowledge domains, reasoning tasks, and code generation.

Compare Models

Run the same benchmark suite against different models or providers to make data-driven selection decisions.

Track Over Time

Re-run benchmarks after fine-tuning, prompt changes, or model upgrades to measure the impact on performance.

Meet Compliance Requirements

Document model capabilities with standardized, reproducible evaluation results for audits and stakeholder reviews.

Available Benchmarks

Avaliar includes six industry-standard benchmark suites:

Benchmark	What It Measures
MMLU	Broad multi-task knowledge across 57 subjects (STEM, humanities, social sciences, and more).
DROP	Discrete reasoning over paragraphs — reading comprehension combined with numerical reasoning.
HellaSwag	Commonsense reasoning via sentence completion.
TruthfulQA	Whether models generate truthful answers instead of common misconceptions.
BigBenchHard	23 challenging multi-step reasoning tasks from BIG-Bench.
HumanEval	Functional code generation correctness with execution-based scoring.

Benchmarking Workflow

1. Implement AvaliarBaseLLM

   Create a class that wraps your model behind the standard AvaliarBaseLLM interface. This gives the benchmark runner a consistent way to call your model regardless of provider.

2. Choose Benchmarks

   Select one or more benchmark suites and configure tasks, shot count, and other parameters to match your evaluation goals.

3. Run Evaluation

   Call benchmark.evaluate() with your model instance. The runner sends each test case to your model, collects responses, and computes scores automatically.

4. Post Results to Avaliar

   Upload benchmark results to the Avaliar platform with benchmark.post_results(). Tag results with model name, version, and any custom labels.

5. Compare in Dashboard

   Open the Avaliar dashboard to visualize results, compare models side-by-side, and track capability trends across runs.

Comparing models

Run the same benchmark suite against different models to make data-driven decisions:

from avaliar.benchmarks.compare import compare_models

result1, result2 = compare_models(
    model1=GPT4o(),
    model2=Claude(),
    model1_name="GPT-4o",
    model2_name="Claude Sonnet",
    benchmark=MMLU(tasks=[MMLUTask.MACHINE_LEARNING]),
)

Next Steps

Running Benchmarks

Configuration options, batch generation, and best practices.

Available Benchmarks

Full constructor reference for all 6 benchmark suites.

Safety & Bias Evals

Evaluate your model for bias, toxicity, and safety risks.