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GAIA (General AI Assistants)

What it is

GAIA (General AI Assistants) is a benchmark proposed to evaluate General AI Assistants. It consists of 450 non-trivial questions that are conceptually simple for humans but challenging for most advanced AI systems.

What problem it solves

Existing benchmarks often focus on narrow tasks or synthetic reasoning. GAIA targets real-world tasks that require fundamental abilities such as reasoning, multi-modality handling, web browsing, and tool-use proficiency. It aims to measure how well an agent can function as a general-purpose assistant.

Where it fits in the stack

Eval. It provides a high-signal benchmark for testing autonomous agents and LLMs on multi-step, real-world tasks.

Typical use cases

  • Agent Benchmarking: Comparing the performance of different agent architectures on realistic assistant tasks.
  • Tool-Use Proficiency: Measuring an agent's ability to select and use external tools (browsers, interpreters) correctly.
  • Reasoning Evaluation: Testing long-horizon reasoning and planning in open-ended environments.

Strengths

  • Non-synthetic: Questions are grounded in real-world scenarios.
  • Ease for Humans: Tasks are generally easy for a human to complete in a few minutes, making the performance gap with AIs very clear.
  • Multi-modal: Requires handling text, images, and other file formats.
  • Robustness: Designed to be hard to solve via pure memorization or "cheating" through data contamination.

Limitations

  • Evaluation Difficulty: Requires execution-based evaluation or human-in-the-loop for complex open-ended responses.
  • Environment Dependency: Web-based tasks are subject to site changes.

When to use it

  • When you want to evaluate the "generalist" capability of an AI agent.
  • When you need a benchmark that goes beyond simple RAG or coding.

When not to use it

  • For testing very specific domain expertise (e.g., medical, legal) unless it falls under general assistant tasks.
  • For lightweight testing where a simpler benchmark (like MMLU) suffices.

Getting started

GAIA evaluations are often run using frameworks like inspect-ai.

1. Installation

pip install inspect-evals[gaia]

2. Running GAIA with Inspect

inspect eval inspect_evals/gaia --model openai/gpt-4o

Licensing and cost

  • Open Source: Yes (Apache 2.0 / CC-BY-SA 4.0)
  • Cost: Free to use the benchmark, but requires LLM API credits.

Sources / References

Contribution Metadata

  • Last reviewed: 2026-06-05
  • Confidence: high