Aider Polyglot
| Aider Polyglot | |
|---|---|
| Overview | |
| Full name | Aider Polyglot Coding Benchmark |
| Abbreviation | Aider Polyglot |
| Description | A challenging multi-language code generation benchmark testing LLMs on 225 difficult Exercism coding exercises across six programming languages |
| Release date | 2024-12-21 |
| Latest version | 1.0 |
| Benchmark updated | 2025-08-13 |
| Authors | Paul Gauthier |
| Organization | Aider AI |
| Technical Details | |
| Type | Code Generation, Code Editing |
| Modality | Text, Code |
| Task format | Code completion and editing |
| Number of tasks | 225 |
| Total examples | 225 |
| Evaluation metric | Percent Correct, Edit Format Accuracy |
| Domains | Software Engineering, Programming |
| Languages | C++, Go, Java, JavaScript, Python, Rust |
| Performance | |
| Human performance | Not reported |
| Baseline | 3.6% (GPT-4o-mini) |
| SOTA score | 84.9% (standard model), 92.9% (agent system) |
| SOTA model | o3-pro (high) / Refact.ai Agent |
| SOTA date | 2025-08 |
| Saturated | No |
| Resources | |
| Website | Official website |
| GitHub | Repository |
| Dataset | Download |
| Predecessor | Aider Code Editing Benchmark |
Aider Polyglot is a challenging code generation benchmark that evaluates large language models' ability to solve complex programming problems across six major programming languages. Released on December 21, 2024, by Aider AI creator Paul Gauthier, the benchmark consists of 225 carefully selected Exercism coding exercises designed to test models' capabilities in C++, Go, Java, JavaScript, Python, and Rust. It represents a significant evolution from the original Python-only Aider benchmark, providing better differentiation between frontier models through increased difficulty and language diversity.
Overview
Aider Polyglot was created to address the saturation of existing code generation benchmarks, where top models were achieving 80%+ scores, making meaningful comparisons difficult. The benchmark specifically tests whether AI can write new code that integrates seamlessly into existing codebases and successfully apply changes to source files without human intervention.
Design Philosophy
The benchmark was designed with several key objectives:
- Prevent Saturation: Re-calibrate scoring so top LLMs occupy 5-50% range
- Language Diversity: Test across multiple programming paradigms and syntax styles
- Real-World Relevance: Focus on practical code editing rather than generation from scratch
- Difficulty Balance: Select problems that challenge but don't completely stump models
Problem Selection Methodology
The 225 problems were carefully selected from 697 available Exercism exercises through empirical testing: 1. Seven top coding models attempted all 697 problems 2. Problems solved by 3 or fewer models were selected 3. Final set balanced difficulty across languages 4. Ensured sufficient headroom for future model improvements
Technical Specifications
Language Distribution
The benchmark's 225 problems are distributed across six programming languages:
| Language | Number of Problems | Percentage | Paradigm |
|---|---|---|---|
| JavaScript | 49 | 21.8% | Multi-paradigm, Dynamic |
| Java | 47 | 20.9% | Object-oriented, Static |
| Go | 39 | 17.3% | Concurrent, Static |
| Python | 34 | 15.1% | Multi-paradigm, Dynamic |
| Rust | 30 | 13.3% | Systems, Memory-safe |
| C++ | 26 | 11.6% | Multi-paradigm, Systems |
| Total | 225 | 100% | Various |
Evaluation Methodology
Primary Metrics
| Metric | Description | Significance |
|---|---|---|
| Percent Correct | Percentage of problems solved correctly | Primary performance indicator |
| Edit Format Accuracy | Percentage using correct diff format | Implementation quality measure |
| Cost | Average cost per problem attempt | Efficiency metric |
| Pass Rate | Problems passing all test cases | Functional correctness |
Edit Format Requirements
The benchmark requires models to:
- Generate precise search-and-replace instructions
- Use proper diff format for code modifications
- Apply changes without breaking existing code
- Maintain code style and conventions
Problem Characteristics
Problems in Aider Polyglot typically involve:
- Algorithm Implementation: Sorting, searching, graph algorithms
- Data Structure Manipulation: Trees, lists, maps, custom structures
- String Processing: Parsing, transformation, pattern matching
- Mathematical Computation: Number theory, geometry, statistics
- System Design: API design, class hierarchies, module organization
Performance Analysis
Official Leaderboard (August 13, 2025)
| Rank | Model | Percent Correct | Cost | Organization |
|---|---|---|---|---|
| 1 | o3-pro (high) | 84.9% | $146.32 | OpenAI |
| 2 | gemini-2.5-pro-preview (32k think) | 83.1% | $49.88 | Google DeepMind |
| 3 | o3 (high) | 81.3% | $21.23 | OpenAI |
| 4 | Grok-4 (high) | 79.6% | $59.62 | xAI |
| 5 | gemini-2.5-pro-preview (default think) | 79.1% | $19.29 | Google DeepMind |
| 6 | o1 (high) | 61.7% | $74.66 | OpenAI |
| 7 | Claude 3.5 Sonnet | 45.3% | $3.12 | Anthropic |
| 8 | Gemini Experimental | 38.2% | - | Google DeepMind |
| 9 | GPT-4o | ~25% | - | OpenAI |
| 10 | GPT-4o-mini | 3.6% | $0.14 | OpenAI |
Agent System Performance
In addition to standard model evaluations, agent systems have achieved higher scores:
| Agent System | Base Model | Score | Date | Notes |
|---|---|---|---|---|
| Refact.ai Agent | Claude 3.7 Sonnet | 92.9% | April 2025 | 30 steps, enforced test execution |
| Refact.ai Agent (Thinking) | Claude 3.7 Sonnet | 93.3% | April 2025 | With thinking mode enabled |
| Aider | Various | 60.4% | 2024 | Original agent baseline |
Note: Agent systems use iterative approaches with multiple attempts and self-correction, achieving higher scores than single-pass model evaluations.
Performance Trends
Initial Release (December 2024)
- o1 topped the initial leaderboard with 61.7%
- Top models occupied the 5-50% range as intended
- Clear differentiation between model capabilities
Evolution (2024-2025)
- Rapid improvements in reasoning models
- Introduction of "thinking" modes in models
- Agent systems pushing boundaries beyond 90%
Language-Specific Performance
While detailed per-language scores aren't publicly available, analysis suggests:
- Easiest: Python, JavaScript (familiar syntax, extensive training data)
- Moderate: Java, Go (structured, well-documented)
- Hardest: Rust, C++ (complex memory management, ownership semantics)
Model Categories Performance
| Category | Score Range | Examples | Characteristics |
|---|---|---|---|
| Frontier Reasoning | 75-85% | o3-pro, gemini-2.5-pro | Advanced reasoning, high compute |
| Agent Systems | 85-93% | Refact.ai Agent | Iterative, self-correcting |
| Top Tier | 45-75% | Claude 3.5, o1 | Strong general capability |
| Mid Tier | 15-45% | GPT-4o, older models | Good but limited on complex tasks |
| Entry Level | <15% | GPT-4o-mini, open models | Basic capability, frequent failures |
Comparison with Other Benchmarks
vs Original Aider Benchmark
| Aspect | Original Aider | Aider Polyglot |
|---|---|---|
| Languages | Python only | 6 languages |
| Problems | 133 (all Exercism Python) | 225 (hardest from 697) |
| Top Score | 84.2% (saturating) | 84.9% (room for growth) |
| Difficulty | Moderate | High |
| Release | 2023 | December 21, 2024 |
vs Other Code Benchmarks
SWE-bench
- Similarity: Real-world code editing tasks
- Difference: Aider Polyglot uses cleaner, self-contained problems
- Advantage: More reliable evaluation, less ambiguity
HumanEval
- Similarity: Code generation from description
- Difference: Aider Polyglot requires editing existing code
- Advantage: Better reflects real development workflows
MBPP
- Similarity: Multiple programming problems
- Difference: Aider Polyglot spans multiple languages
- Advantage: Tests language-agnostic programming ability
Implementation Details
Testing Infrastructure
The benchmark employs:
- Exercism Test Suites: Comprehensive unit tests for each problem
- Language-Specific Runners: Native testing frameworks for each language
- Automated Validation: Immediate feedback on solution correctness
- Diff Application: Tools to apply model-generated edits
Problem Structure
Each problem includes: 1. Starter Code: Initial implementation skeleton 2. Test Suite: Comprehensive unit tests 3. Instructions: Problem description and requirements 4. Expected Output: Reference solution behavior
Evaluation Process
| Step | Action | Validation |
|---|---|---|
| 1 | Model receives problem description and starter code | Input formatting check |
| 2 | Model generates edit instructions | Diff format validation |
| 3 | Edits applied to source files | Syntax verification |
| 4 | Test suite executed | Functional correctness |
| 5 | Results recorded | Performance metrics |
Significance and Impact
Industry Implications
- Model Development: Drives improvements in code reasoning capabilities
- Tool Integration: Influences AI coding assistant design
- Hiring Standards: Provides objective measure of coding capability
- Research Direction: Guides focus on multi-language proficiency
Research Value
The benchmark provides insights into:
- Cross-Language Transfer: How well models generalize across languages
- Problem-Solving Strategies: Common approaches and failure modes
- Edit Format Understanding: Models' ability to work with existing code
- Reasoning Depth: Correlation between thinking time and performance
Limitations and Criticisms
Current Limitations
| Limitation | Description | Impact |
|---|---|---|
| Limited Languages | Only 6 languages covered | Misses domain-specific languages |
| Exercism Focus | All problems from one source | Potential style bias |
| Static Dataset | Fixed 225 problems | Risk of overfitting |
| Edit Format | Specific diff requirement | May not match all workflows |
| No Debugging | Only generation tested | Misses fix/refactor capabilities |
Methodological Concerns
- Problem Selection Bias: Selection based on initial model performance
- Language Imbalance: Uneven distribution across languages
- Test Coverage: May not capture all programming skills
- Version Control: No git integration or collaboration testing
Future Directions
Planned Improvements
- Language Expansion: Adding TypeScript, Swift, Kotlin
- Problem Diversity: Including debugging, refactoring tasks
- Dynamic Generation: Procedural problem creation
- Collaboration Testing: Multi-agent coding scenarios
Research Opportunities
- Language-Specific Optimization: Tailored approaches per language
- Transfer Learning: Leveraging knowledge across languages
- Edit Strategy: Optimal diff generation techniques
- Error Recovery: Handling compilation and runtime errors
Related Benchmarks
- SWE-bench: Software engineering tasks
- HumanEval: Python code generation
- MBPP: Python programming problems
- CodeContests: Competitive programming
- APPS: Algorithmic problem solving
- MultiPL-E: Multi-language evaluation
- CodeXGLUE: Code understanding and generation
See Also
- Code Generation
- Programming Language Models
- AI Pair Programming
- Exercism
- Software Engineering AI
- Multi-language Programming
References
Cite error: <ref> tag with name "aider_polyglot" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "github" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "leaderboard" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "refact" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "medium_refact" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "llmdb" defined in <references> has group attribute "" which does not appear in prior text.
Cite error: <ref> tag with name "claude37" defined in <references> has group attribute "" which does not appear in prior text.