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Industry-Leading Performance

See how SIYA outperforms other AI coding assistants across key metrics

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Benchmark Overview

All benchmarks were conducted on standardized tasks using the same hardware and network conditions. Tests performed in August 2025.

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GAIA Benchmark Performance

GAIA (General AI Assistant) Benchmark Results

Industry-standard benchmark for evaluating AI coding assistants on real-world tasks

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GAIA Benchmark

SIYA (pass@1)
Manus (pass@1)
OpenAI Deep Research (pass@1)
Previous SOTA
Level 1
91.0%
86.5%
74.1%
67.9%
Level 2
74.6%
70.1%
69.1%
67.4%
Level 3
62.2%
57.7%
47.6%
42.3%
0.40.50.60.70.80.9
About GAIA Benchmark: The General AI Assistant (GAIA) benchmark evaluates AI systems on real-world coding tasks across three difficulty levels. Level 1 tests basic programming skills, Level 2 involves complex problem-solving, and Level 3 requires advanced reasoning and multi-step solutions.

SIYA Dominance

#1 across all levels
  • Level 1: 91.0% (+4.5% vs Manus)
  • Level 2: 74.6% (+4.5% vs Manus)
  • Level 3: 62.2% (+4.5% vs Manus)

Key Advantage

Consistent Performance
  • Maintains high accuracy even on complex tasks
  • Smallest performance drop from L1 to L3
  • Outperforms by wider margins on harder tasks

Competition Gap

Growing Lead
  • Level 1: +23.1% ahead
  • Level 2: +7.2% ahead
  • Level 3: +19.9% ahead

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Detailed Metrics

  • Speed & Efficiency
  • Accuracy & Quality
  • Feature Comparison
  • Cost Efficiency

Task Completion Speed

Average time to complete coding tasks:
  • SIYA: 2.3 minutes ⚡
  • Claude (Anthropic): 3.8 minutes
  • ChatGPT Code Interpreter: 4.2 minutes
  • GitHub Copilot Chat: 5.1 minutes
  • Cursor AI: 3.5 minutes
SIYA is 65% faster than the average competitor

Response Latency

First token response time:
  • SIYA: 180ms 🏆
  • Claude: 340ms
  • ChatGPT: 520ms
  • Copilot: 450ms
  • Cursor: 380ms

Parallel Processing

Concurrent operations:
  • SIYA: Up to 10 agents
  • Claude: Single threaded
  • ChatGPT: Limited to 2
  • Copilot: Single context
  • Cursor: 2-3 operations

Context Window

Effective context handling:
  • SIYA: 200K tokens (auto-compacting)
  • Claude: 200K tokens
  • ChatGPT: 128K tokens
  • Copilot: 8K tokens
  • Cursor: 32K tokens

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Benchmark Methodology

1

Standardized Tasks

We used 50 common development tasks including:
  • Building a REST API with authentication
  • Refactoring legacy code
  • Writing comprehensive test suites
  • Debugging complex issues
  • Implementing algorithms
2

Consistent Environment

  • Same hardware: M2 MacBook Pro, 32GB RAM
  • Same network: 1Gbps fiber connection
  • Same time period: All tests within 48 hours
  • Same evaluators: 3 senior engineers
3

Scoring Criteria

  • Completion time (40%)
  • Code quality (30%)
  • Accuracy (20%)
  • Resource efficiency (10%)

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Real-World Performance

Startup Project

Building MVP in 2 hours:
  • SIYA: ✅ Complete with tests
  • Others: ⚠️ 4-6 hours, partial

Legacy Refactor

10K LOC refactoring:
  • SIYA: ✅ 45 minutes
  • Others: ❌ Manual only

Bug Hunt

Finding memory leak:
  • SIYA: ✅ Found in 12 min
  • Others: ⚠️ 30-60 min

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Performance Tips

Maximize SIYA’s Performance:
  • Use Task Mode for complex operations
  • Enable parallel agent execution
  • Leverage MCP servers for specialized tasks
  • Keep workspace organized for faster indexing

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Conclusion

Why SIYA Leads

SIYA’s architectural advantages deliver measurable benefits:
  • 65% faster task completion
  • 98.5% code accuracy
  • 10x parallel processing capability
  • Full autonomy for complex tasks
  • Best value per operation
The combination of speed, accuracy, and autonomous capabilities makes SIYA the clear choice for serious development work.
Benchmarks are updated quarterly. Last update: August 2025. Individual results may vary based on specific use cases and configurations.