Aider Polyglot
BenchmarkFreeMulti-language AI coding benchmark — tests code editing ability across 10+ languages.
Capabilities9 decomposed
multi-language code editing correctness evaluation
Medium confidenceExecutes 225 real-world coding exercises across 6+ programming languages (C++, Go, Java, JavaScript, Python, Rust) and measures whether an AI model can correctly modify existing codebases given natural language instructions. Uses execution-based validation (running test cases) rather than syntactic checking, capturing both logical correctness and structural validity of generated diffs. Tracks dual pass-rate metrics to distinguish between strict and lenient correctness criteria.
Uses execution-based validation (running actual test cases) rather than syntactic or semantic checking, combined with dual pass-rate metrics to distinguish logical correctness from structural validity. Covers 6+ languages in a single benchmark, enabling direct comparison of polyglot coding capability. Tracks detailed error categories (syntax errors, indentation errors, context window exhaustion, timeouts) to diagnose failure modes.
More realistic than code-generation-only benchmarks because it tests code editing (understanding and modifying existing code) rather than generation from scratch, and execution-based validation is more rigorous than AST-matching or string similarity metrics used by competitors.
cost-performance tradeoff analysis across reasoning effort levels
Medium confidenceEvaluates the same AI models at different reasoning effort settings (high, medium, etc.) and correlates performance gains with API cost per evaluation run. Captures total cost per model configuration (e.g., $29.08 for gpt-5 high vs $17.69 for gpt-5 medium) and execution time per test case, enabling builders to optimize for their cost constraints. Leaderboard displays both metrics side-by-side for direct comparison.
Explicitly tracks and displays API cost alongside performance metrics on the leaderboard, enabling direct cost-performance comparison. Captures execution time per test case, allowing builders to estimate total evaluation cost before running benchmarks. Evaluates models at multiple reasoning effort levels to quantify the cost-benefit tradeoff.
Most code benchmarks report only accuracy metrics; Aider Polyglot uniquely surfaces cost data, making it actionable for production deployment decisions where budget constraints are real. Competitors like HumanEval or CodeXGLUE do not track or report API costs.
diff-format code generation with structural validity checking
Medium confidenceValidates that AI-generated code edits conform to diff format specifications (unified diff or similar patch format) before execution. Tracks the percentage of well-formed responses (91.6% for gpt-5 high) separately from logical correctness, enabling diagnosis of whether failures are due to malformed output (structural) or incorrect logic. Captures specific error types: syntax errors, indentation errors, and context window exhaustion.
Separates structural validity (is the diff well-formed?) from logical correctness (does the code work?), providing two independent pass-rate metrics. Tracks specific error categories (syntax, indentation, context exhaustion, timeout) rather than lumping all failures together, enabling root-cause analysis.
Most code benchmarks report only pass/fail; Aider Polyglot's dual-metric approach (well-formed % vs correct %) reveals whether a model's failures are due to format issues (fixable with output repair) or logic errors (require retraining). This distinction is actionable for production systems.
polyglot performance comparison with language-agnostic metrics
Medium confidenceAggregates results across 6+ programming languages into a single overall pass-rate score, enabling comparison of models' general code editing capability independent of language. Does not provide per-language breakdowns on the public leaderboard, but the benchmark infrastructure supports language-specific evaluation. Allows builders to identify whether a model is universally strong or has language-specific weaknesses.
Evaluates code editing across 6+ languages in a single benchmark, unlike language-specific benchmarks (HumanEval for Python, CodeXGLUE for Java, etc.). Aggregates results into a language-agnostic metric, enabling direct comparison of models' polyglot capability.
Competitors typically benchmark single languages; Aider Polyglot's multi-language approach is more realistic for teams using multiple languages and reveals whether models generalize across language families or have language-specific weaknesses.
real-world coding exercise dataset with execution-based validation
Medium confidenceUses 225 Exercism coding problems as the benchmark dataset, which are real-world style exercises (not synthetic or toy problems) covering algorithmic, data structure, and practical coding tasks. Validates correctness by executing the modified code against test cases, rather than using string matching or AST comparison. This execution-based approach catches logical errors that syntactic validators would miss (e.g., off-by-one errors, incorrect algorithm logic).
Uses Exercism (a real-world coding exercise platform) rather than synthetic benchmarks, and validates correctness through code execution rather than string matching or AST comparison. This execution-based approach catches logical errors that syntactic validators miss.
HumanEval and CodeXGLUE use synthetic or curated problems; Aider Polyglot's use of Exercism provides more realistic, diverse problems. Execution-based validation is more rigorous than string-matching approaches used by some competitors, but introduces sandboxing complexity.
leaderboard with model versioning and commit tracking
Medium confidenceMaintains a public leaderboard of AI model performance on the benchmark, with each entry tagged with the model name, version, reasoning effort level, and exact commit hash of the benchmark code used. Enables reproducibility and tracking of performance changes over time as models are updated. Leaderboard is sortable and expandable to show detailed metrics per model.
Records exact benchmark code commit hash and model version for each leaderboard entry, enabling reproducibility and tracking of performance changes over time. Supports multiple reasoning effort levels for the same model, revealing cost-performance tradeoffs.
Most benchmarks publish results but do not track versions or commit hashes; Aider Polyglot's versioning approach enables reproducibility and historical tracking. However, the leaderboard lacks documentation on submission process and update frequency, limiting transparency.
context window exhaustion and timeout tracking
Medium confidenceMonitors whether models exhaust their context window during evaluation (e.g., prompt + code + instructions exceed token limit) and tracks test cases that timeout during execution. Records these as separate error categories distinct from logical errors or format violations. Enables diagnosis of whether a model's failures are due to capacity constraints rather than capability limitations.
Explicitly tracks context window exhaustion and execution timeouts as separate error categories, enabling diagnosis of whether failures are due to capacity constraints or logical errors. Most benchmarks do not report these metrics.
Competitors like HumanEval do not track context window exhaustion; Aider Polyglot's explicit tracking reveals whether performance gaps are due to model capability or infrastructure constraints, which is actionable for deployment decisions.
aider integration for benchmark execution
Medium confidenceThe benchmark is built into and executed through the Aider CLI tool, which is an AI pair programming assistant. Aider handles model API calls, diff generation, code execution, and test validation. Builders can run the benchmark locally using `aider --model <provider>/<model>` syntax, which automatically orchestrates the entire evaluation pipeline. Supports 15+ LLM providers (OpenAI, Anthropic, Gemini, GROQ, xAI, Azure, Cohere, DeepSeek, Ollama, OpenRouter, GitHub Copilot, Vertex AI, Amazon Bedrock, and others).
Benchmark is integrated into Aider, an existing AI pair programming tool, rather than being a standalone evaluation framework. This enables builders to run benchmarks using the same tool they use for development, and supports 15+ LLM providers through Aider's provider abstraction layer.
Competitors like HumanEval require custom code to run against different models; Aider Polyglot's integration into Aider provides a unified CLI interface for benchmarking across providers. However, this also creates a dependency on Aider's implementation and versioning.
dual pass-rate metrics for strict vs lenient evaluation
Medium confidenceReports two separate pass-rate metrics for each model (e.g., 52.0% and 88.0% for gpt-5 high), which appear to represent strict and lenient correctness criteria. The distinction between these metrics is undocumented, but likely reflects different levels of test case strictness (e.g., exact output match vs output semantically correct). Enables builders to understand performance under different evaluation standards.
Reports two separate pass-rate metrics (strict and lenient) rather than a single binary pass/fail, providing nuance about model performance. However, the distinction is undocumented, limiting interpretability.
Most benchmarks report a single pass rate; Aider Polyglot's dual-metric approach reveals whether models are 'close' to correct even if not perfect. However, the lack of documentation on what these metrics mean limits their usefulness compared to clearly defined evaluation criteria.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓AI model developers benchmarking code editing capabilities
- ✓Teams evaluating AI pair programming tools for multi-language codebases
- ✓Researchers studying code generation quality across programming languages
- ✓Organizations comparing LLM providers on practical coding tasks
- ✓Cost-conscious teams deploying AI coding assistants at scale
- ✓Startups evaluating LLM providers to optimize burn rate
- ✓Researchers studying efficiency frontiers of reasoning models vs base models
- ✓Platform operators deciding which model tiers to expose to users
Known Limitations
- ⚠Measures only small, isolated coding exercises (Exercism problems) — does not evaluate refactoring large codebases, maintaining architectural consistency, or handling cross-file dependencies
- ⚠No per-language performance breakdown provided — unclear if certain languages are systematically easier or harder
- ⚠High data contamination risk — Exercism exercises are public and likely present in LLM training data, potentially inflating scores through memorization rather than generalization
- ⚠Single-turn evaluation only — does not measure iterative refinement or multi-turn error recovery
- ⚠No statistical significance testing or confidence intervals — each leaderboard entry represents a single evaluation run
- ⚠Distinction between two pass-rate metrics (52% vs 88% for gpt-5 high) is undocumented, making interpretation ambiguous
Requirements
Input / Output
UnfragileRank
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About
Benchmark for AI coding assistants across multiple programming languages. Tests code editing ability: given a codebase and instructions, can the AI make correct changes? Evaluates 10+ languages. Maintained by the aider team.
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