SF-Bench vs. SWE-bench: Detailed Comparison

Two benchmarks, different purposes. Here’s when to use which.

🎯 Quick Comparison

📊 Detailed Comparison

1. Domain & Scope

SWE-bench

• Domain: Open-source Python projects
• Scope: General software engineering
• Examples: Django, scikit-learn, pandas
• Focus: Bug fixes, feature additions

SF-Bench

• Domain: Salesforce platform
• Scope: Enterprise CRM development
• Examples: Apex, LWC, Flow, Lightning Pages
• Focus: Business logic, platform constraints

When to use SWE-bench: Testing general programming capabilities
When to use SF-Bench: Testing Salesforce-specific capabilities

2. Task Types

SWE-bench Tasks

• Bug fixes in Python codebases
• Feature additions to open-source projects
• Code refactoring
• Test writing

SF-Bench Tasks

• Apex triggers and classes
• Lightning Web Components
• Flow automation
• Lightning Pages
• Experience Cloud sites
• Architecture design

Key Difference: SF-Bench covers multi-modal development (code + visual tools)

3. Execution Environment

SWE-bench

• Environment: Docker containers
• Setup: Clone repo, install dependencies
• Execution: Run test suite
• Validation: Test pass/fail

SF-Bench

• Environment: Salesforce scratch orgs
• Setup: Create scratch org, deploy metadata
• Execution: Deploy + run tests + verify outcomes
• Validation: Multi-level (deploy + tests + functional)

Key Difference: SF-Bench validates functional outcomes, not just test execution

4. Validation Methodology

SWE-bench Validation

1. Apply patch
2. Run test suite
3. Check: Tests pass? → PASS/FAIL

SF-Bench Validation

1. Apply solution
2. Deploy to scratch org (10%)
3. Run unit tests (20%)
4. Verify functional outcome (50%)
5. Test bulk operations (10%)
6. Check no manual tweaks (10%)
7. Score: 0-100 points

Key Difference: SF-Bench uses weighted scoring with functional validation

5. Task Complexity

SWE-bench

• Complexity: Varies (easy to hard)
• Context: GitHub issue descriptions
• Dependencies: Project-specific
• Size: Large codebases

SF-Bench

• Complexity: Controlled (lite to realistic)
• Context: Detailed task descriptions
• Dependencies: Salesforce platform
• Size: Focused tasks

Key Difference: SF-Bench tasks are curated for Salesforce development

6. Use Cases

When to Use SWE-bench

✅ Good for:

• General AI research
• Testing Python capabilities
• Open-source contribution simulation
• Large-scale evaluation

❌ Not ideal for:

• Salesforce-specific evaluation
• Enterprise CRM development
• Multi-modal development testing

When to Use SF-Bench

✅ Good for:

• Salesforce AI tool evaluation
• Enterprise CRM development
• Multi-modal development testing
• Business logic validation

❌ Not ideal for:

• General programming evaluation
• Non-Salesforce use cases
• Large-scale research (yet)

🔍 Methodology Comparison

SWE-bench Methodology

1. Task Selection: Real GitHub issues
2. Solution Generation: AI generates patch
3. Validation: Run test suite
4. Scoring: Pass/fail binary

Strengths:

• Large task set
• Real-world issues
• Reproducible

Limitations:

• Binary scoring
• No functional validation
• Python-only

SF-Bench Methodology

1. Task Selection: Curated Salesforce tasks
2. Solution Generation: AI generates solution
3. Validation: Multi-level validation
4. Scoring: Weighted 0-100 points

Strengths:

• Functional validation
• Multi-modal testing
• Weighted scoring
• Salesforce-specific

Limitations:

• Smaller task set (growing)
• Salesforce-only
• Requires Salesforce org

📈 Performance Comparison

What Models Score Well on SWE-bench?

• GPT-4: ~30-40% pass rate
• Claude 3.5: ~25-35% pass rate
• Gemini: ~20-30% pass rate

What Models Score Well on SF-Bench?

• Claude Sonnet 4.5: 41.67% overall, 6.0% functional
• Gemini 2.5 Flash: 25.0% overall
• More results pending

Note: Scores aren’t directly comparable due to different methodologies.

🎯 Which Benchmark Should You Use?

Use SWE-bench If:

• ✅ Testing general programming capabilities
• ✅ Evaluating Python-specific AI
• ✅ Research on open-source contributions
• ✅ Need large-scale evaluation

Use SF-Bench If:

• ✅ Testing Salesforce-specific AI
• ✅ Evaluating enterprise CRM development
• ✅ Testing multi-modal development
• ✅ Need functional validation

Use Both If:

• ✅ Comprehensive AI evaluation
• ✅ Comparing general vs. domain-specific
• ✅ Research on AI capabilities
• ✅ Full-spectrum benchmarking

🔬 Research Implications

For AI Researchers

SWE-bench:

• Tests general programming
• Large-scale evaluation
• Reproducible results

SF-Bench:

• Tests domain-specific capabilities
• Functional validation
• Real-world execution

Combined:

• Comprehensive evaluation
• General + domain-specific
• Full AI capability spectrum

📚 Further Reading

• SWE-bench Paper - Original SWE-bench research
• SF-Bench Methodology - Our validation approach
• Benchmark Details - Technical specifications

🤝 Collaboration

SF-Bench is inspired by SWE-bench and follows similar principles:

• ✅ Real-world tasks
• ✅ Functional validation
• ✅ Objective measurement
• ✅ Open source

We’re complementary, not competitive!

📊 Summary

Bottom Line: Use SWE-bench for general programming, SF-Bench for Salesforce development.

Ready to evaluate? Check out our Quick Start Guide!