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GAIA: General AI Assistants Benchmark(GAIA)
Real-world question benchmark requiring fundamental abilities like reasoning, multi-modality, web browsing, and tool-use proficiency. Conceptually simple for humans yet challenging for AI.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Real-world question benchmark requiring fundamental abilities like reasoning, multi-modality, web browsing, and tool-use proficiency
Why: Conceptually simple for humans yet challenging for AI - tests gap between human (92%) and current best AI (65%) performance
Key Insight: H2O.ai h2oGPTe leads at 65% accuracy, outperforming Google (49%), Microsoft (38%) - first to achieve C-grade on general intelligence
⚡ Quick Implementation
1Setup:Install GAIA benchmark framework and configure evaluation environment
2Select:Choose difficulty level (Level 1: <5 steps, Level 2: 5-10 steps, Level 3: up to 50 steps)
3Configure:Enable tool access: web browsing, file handling, multimodal processing
4Evaluate:Run agent on 466 questions requiring reasoning, multimodality, and tool use
5Analyze:Compare performance against human baseline (92%) and current SOTA (65%)
Example: gaia_eval = GAIA(agent=ai_assistant, levels=[1,2,3], tools=[web_browser, file_handler], timeout=3600)
📋 Do's & Don'ts
✅Test across all three difficulty levels for comprehensive capability assessment
✅Enable full tool suite: web browsing, file handling, multimodal understanding
✅Focus on real-world tasks requiring multi-step reasoning and coordination
✅Validate against human performance baseline of 92% accuracy
✅Use development set (166 questions) for model tuning and validation set for final eval
❌Expect high scores without proper tool integration - GPT-4 achieves only 15%
❌Skip multimodal components - questions include images and spreadsheets
❌Attempt brute-force approaches - answers require full task execution
❌Ignore Level 3 questions - they test advanced long-term planning capabilities
❌Train on test questions - answers are withheld to maintain benchmark integrity
🚦 When to Use
Use When
- • Evaluating general-purpose AI assistant capabilities
- • Testing real-world reasoning and tool use proficiency
- • Assessing multimodal understanding and web browsing skills
- • Benchmarking against human-level general intelligence
- • Research on fundamental AI abilities and limitations
Avoid When
- • Domain-specific task evaluation (use specialized benchmarks)
- • Simple question-answering without tool requirements
- • Performance testing without multimodal capabilities
- • Rapid evaluation needs (questions can take up to 50 human steps)
- • Systems without web access or file handling capabilities
📊 Key Metrics
Overall Accuracy
Primary metric: percentage of correctly answered questions across all levels
Level-wise Performance
Breakdown by difficulty: Level 1 (simple), Level 2 (moderate), Level 3 (complex)
Human-AI Gap
Current gap: 27% (Human 92% vs h2oGPTe 65%)
Tool Use Effectiveness
Success rate in web browsing, file handling, and multimodal tasks
Multi-step Reasoning
Ability to coordinate reasoning across 5-50 steps depending on level
Task Completion Rate
Percentage of tasks completed vs. partial solutions or failures
💡 Top Use Cases
AGI Capability Assessment: H2O.ai h2oGPTe achieves 65% - first C grade on general intelligence test
Industry Benchmarking: Standard for evaluating general AI assistants vs specialized chatbots
Academic Research: Meta/HuggingFace/AutoGPT collaboration for fundamental AI ability measurement
Tool Integration Testing: Comprehensive evaluation of web browsing, file handling, and multimodal reasoning
Real-world Task Simulation: 466 carefully designed questions reflecting authentic assistant scenarios
References & Further Reading
Deepen your understanding with these curated resources
Official Leaderboards & Evaluation
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🌍
GAIA: General AI Assistants Benchmark(GAIA)
Real-world question benchmark requiring fundamental abilities like reasoning, multi-modality, web browsing, and tool-use proficiency. Conceptually simple for humans yet challenging for AI.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Real-world question benchmark requiring fundamental abilities like reasoning, multi-modality, web browsing, and tool-use proficiency
Why: Conceptually simple for humans yet challenging for AI - tests gap between human (92%) and current best AI (65%) performance
Key Insight: H2O.ai h2oGPTe leads at 65% accuracy, outperforming Google (49%), Microsoft (38%) - first to achieve C-grade on general intelligence
⚡ Quick Implementation
1Setup:Install GAIA benchmark framework and configure evaluation environment
2Select:Choose difficulty level (Level 1: <5 steps, Level 2: 5-10 steps, Level 3: up to 50 steps)
3Configure:Enable tool access: web browsing, file handling, multimodal processing
4Evaluate:Run agent on 466 questions requiring reasoning, multimodality, and tool use
5Analyze:Compare performance against human baseline (92%) and current SOTA (65%)
Example: gaia_eval = GAIA(agent=ai_assistant, levels=[1,2,3], tools=[web_browser, file_handler], timeout=3600)
📋 Do's & Don'ts
✅Test across all three difficulty levels for comprehensive capability assessment
✅Enable full tool suite: web browsing, file handling, multimodal understanding
✅Focus on real-world tasks requiring multi-step reasoning and coordination
✅Validate against human performance baseline of 92% accuracy
✅Use development set (166 questions) for model tuning and validation set for final eval
❌Expect high scores without proper tool integration - GPT-4 achieves only 15%
❌Skip multimodal components - questions include images and spreadsheets
❌Attempt brute-force approaches - answers require full task execution
❌Ignore Level 3 questions - they test advanced long-term planning capabilities
❌Train on test questions - answers are withheld to maintain benchmark integrity
🚦 When to Use
Use When
- • Evaluating general-purpose AI assistant capabilities
- • Testing real-world reasoning and tool use proficiency
- • Assessing multimodal understanding and web browsing skills
- • Benchmarking against human-level general intelligence
- • Research on fundamental AI abilities and limitations
Avoid When
- • Domain-specific task evaluation (use specialized benchmarks)
- • Simple question-answering without tool requirements
- • Performance testing without multimodal capabilities
- • Rapid evaluation needs (questions can take up to 50 human steps)
- • Systems without web access or file handling capabilities
📊 Key Metrics
Overall Accuracy
Primary metric: percentage of correctly answered questions across all levels
Level-wise Performance
Breakdown by difficulty: Level 1 (simple), Level 2 (moderate), Level 3 (complex)
Human-AI Gap
Current gap: 27% (Human 92% vs h2oGPTe 65%)
Tool Use Effectiveness
Success rate in web browsing, file handling, and multimodal tasks
Multi-step Reasoning
Ability to coordinate reasoning across 5-50 steps depending on level
Task Completion Rate
Percentage of tasks completed vs. partial solutions or failures
💡 Top Use Cases
AGI Capability Assessment: H2O.ai h2oGPTe achieves 65% - first C grade on general intelligence test
Industry Benchmarking: Standard for evaluating general AI assistants vs specialized chatbots
Academic Research: Meta/HuggingFace/AutoGPT collaboration for fundamental AI ability measurement
Tool Integration Testing: Comprehensive evaluation of web browsing, file handling, and multimodal reasoning
Real-world Task Simulation: 466 carefully designed questions reflecting authentic assistant scenarios
References & Further Reading
Deepen your understanding with these curated resources
Official Leaderboards & Evaluation
Contribute to this collection
Know a great resource? Submit a pull request to add it.