{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"aider-polyglot","slug":"aider-polyglot","name":"Aider Polyglot","type":"benchmark","url":"https://aider.chat/docs/leaderboards","page_url":"https://unfragile.ai/aider-polyglot","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"aider-polyglot__cap_0","uri":"capability://code.generation.editing.multi.language.code.editing.evaluation.with.test.case.validation","name":"multi-language code editing evaluation with test case validation","description":"Evaluates AI models' ability to edit existing codebases by accepting natural language instructions and measuring whether generated edits pass functional test cases across 6+ programming languages (C++, Go, Java, JavaScript, Python, Rust). Uses Exercism platform exercises as test cases, executing generated code against test suites to determine pass/fail outcomes. Tracks both syntactic correctness (well-formed edit format) and functional correctness (test case passage) as distinct metrics.","intents":["Compare AI coding assistants on their ability to make correct edits to existing code","Measure multi-language code editing capability across different model architectures","Identify which models produce syntactically valid edits that also pass functional tests","Evaluate cost-performance tradeoffs between different LLM providers for code editing tasks"],"best_for":["AI model developers benchmarking code editing capabilities","Teams evaluating AI coding assistants for production use","Researchers studying multi-language code generation and editing","Organizations comparing cost-efficiency of different LLM providers for coding tasks"],"limitations":["Only 225 test cases total across all languages; no stratification by difficulty level or language distribution reported","Exercism exercises are public pedagogical problems, not representative of production codebases with cross-file dependencies or architectural complexity","High data contamination risk: no evidence that test set is held-out or that models were excluded from training on Exercism data","Methodology for 'Pass rate 1' metric is undocumented; only 'Pass rate 2' is clearly defined, creating opacity in scoring","No statistical significance testing, confidence intervals, or multiple runs reported; single-point measurements only","Benchmark only accepts diff-based edit format; alternative valid edit formats (full file replacement) may not be supported","Evaluation time ranges from 7-13+ hours per model depending on reasoning effort level, limiting rapid iteration","No measurement of code quality attributes (readability, maintainability, performance, security) beyond test case passage"],"requires":["API key for at least one supported LLM provider (OpenAI, Anthropic, Gemini, GROQ, xAI, Cohere, DeepSeek, or others)","Aider CLI tool (version 0.86.2.dev or later based on leaderboard data)","Python 3.8+ (for Aider runtime)","Network access to LLM provider APIs","Language-specific toolchains for test execution (C++ compiler, Go runtime, Java JDK, Node.js, Python interpreter, Rust toolchain)"],"input_types":["natural language instructions describing code modifications","existing source code in C++, Go, Java, JavaScript, Python, or Rust","test case specifications (implicit in Exercism exercises)"],"output_types":["structured edit output in diff format","pass/fail verdict per test case","aggregated pass rate metrics (Pass rate 1 and Pass rate 2)","well-formedness percentage (syntactic correctness of edit format)","error categorization (syntax errors, indentation errors, context window exhaustion, timeouts, lazy comments)"],"categories":["code-generation-editing","testing-quality","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_1","uri":"capability://code.generation.editing.diff.based.code.edit.format.validation.and.parsing","name":"diff-based code edit format validation and parsing","description":"Validates and parses AI-generated code edits in unified diff format, checking structural correctness before functional testing. Measures the percentage of responses that conform to expected diff syntax (line numbers, context lines, additions/deletions). Rejects malformed edits and categorizes formatting errors (indentation, syntax violations) separately from logic errors.","intents":["Determine what percentage of model outputs are in valid, parseable edit format","Identify models that struggle with diff format generation vs. those that produce well-formed edits","Separate format/syntax issues from functional logic errors in code generation failures","Measure model reliability for integration into automated code editing workflows"],"best_for":["Developers building AI-assisted code editing tools that depend on diff format parsing","Teams evaluating models for automated refactoring or code transformation pipelines","Researchers studying structured output generation from LLMs"],"limitations":["Only validates diff format; does not measure whether edits are semantically correct or preserve code behavior","No support for alternative edit formats (full file replacement, line-based edits, AST-based patches); models must output unified diff","Indentation errors are tracked separately but may indicate model confusion about language-specific whitespace rules","No measurement of edit minimality or elegance; accepts any syntactically valid diff regardless of efficiency"],"requires":["Model output in unified diff format (standard patch format)","Diff parser implementation (included in Aider evaluation harness)"],"input_types":["raw model output (text)","expected diff format specification"],"output_types":["boolean: well-formed or malformed","error category: syntax error, indentation error, or other format violation","aggregated well-formedness percentage across test set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_10","uri":"capability://automation.workflow.reproducibility.metadata.tracking.aider.version.commit.hash.test.date","name":"reproducibility metadata tracking (aider version, commit hash, test date)","description":"Tracks and reports metadata for each benchmark evaluation: Aider version (0.86.2.dev), commit hash (e.g., 32faf82, 5318380), and test date (2025-06-28 to 2025-08-25). Metadata enables reproducibility verification and tracking of evaluation environment changes over time. Leaderboard includes metadata for each result.","intents":["Verify reproducibility of benchmark results across different evaluation runs","Track changes in evaluation methodology or test cases over time","Identify which Aider version and commit hash produced specific results","Enable researchers to reproduce results or understand evaluation environment"],"best_for":["Benchmark maintainers tracking evaluation methodology changes","Researchers verifying reproducibility of published results","Teams auditing benchmark integrity and consistency"],"limitations":["Metadata is minimal; only Aider version, commit hash, and test date are tracked","No tracking of LLM provider versions or API changes; unclear if model versions changed during evaluation period","No tracking of hardware/infrastructure used for evaluation; execution environment is not standardized","No tracking of random seeds or other sources of non-determinism; unclear if results are deterministic","Test date range (2025-06-28 to 2025-08-25) is wide; unclear if all models were evaluated on same date or if evaluation was staggered"],"requires":["Git repository for Aider with commit history","Timestamp recording for each evaluation run"],"input_types":["Aider version","commit hash","evaluation timestamp"],"output_types":["metadata tuple: (aider_version, commit_hash, test_date)","leaderboard entry with metadata"],"categories":["automation-workflow","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_2","uri":"capability://code.generation.editing.test.case.execution.and.functional.correctness.measurement","name":"test case execution and functional correctness measurement","description":"Executes generated code edits against language-specific test suites (from Exercism exercises) and measures functional correctness by running test cases in sandboxed environments. Tracks pass/fail outcomes, timeout behavior, and context window exhaustion. Supports execution in C++, Go, Java, JavaScript, Python, and Rust with language-specific toolchains and test runners.","intents":["Measure whether AI-generated code edits actually work, not just whether they parse","Identify models that produce syntactically valid but logically incorrect code","Detect resource constraints (context window limits, execution timeouts) that prevent successful code generation","Compare functional correctness across different LLM providers and reasoning effort levels"],"best_for":["Benchmark maintainers evaluating code generation models","Teams selecting AI coding assistants based on correctness guarantees","Researchers studying the gap between syntactic and semantic correctness in LLM code generation"],"limitations":["Test cases are from Exercism (pedagogical exercises), not production code; may not reflect real-world complexity","Execution environment is isolated from actual deployment contexts; does not measure performance, security, or integration correctness","Timeout threshold is not documented; unclear how long models are allowed to run before being terminated","No measurement of partial correctness; exercises are binary pass/fail, not partial credit","Context window exhaustion is tracked but not analyzed; unclear which models hit limits or why","Test execution time ranges from 7-13+ hours per model, limiting rapid iteration and ablation studies"],"requires":["Language-specific test runners and compilers: C++ compiler (g++/clang), Go runtime, Java JDK, Node.js, Python 3.8+, Rust toolchain","Exercism test case definitions (included in benchmark)","Sandboxed execution environment (implementation details not documented)","Timeout and resource limit configuration"],"input_types":["generated code edits (in diff format)","test case specifications (Exercism exercises)","language identifier (C++, Go, Java, JavaScript, Python, Rust)"],"output_types":["pass/fail verdict per test case","aggregated pass rate (Pass rate 2 metric)","error type: timeout, context window exhaustion, test failure","execution time per case (194.0 seconds for gpt-5 high, 118.7 for gpt-5 medium)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_3","uri":"capability://data.processing.analysis.cost.per.case.measurement.and.cost.efficiency.ranking","name":"cost-per-case measurement and cost-efficiency ranking","description":"Measures and reports the monetary cost of evaluating each test case for each LLM provider, enabling cost-efficiency analysis. Aggregates per-case costs across 225 exercises to produce total evaluation cost. Includes cost data in leaderboard rankings alongside performance metrics, allowing direct comparison of cost-performance tradeoffs (e.g., gpt-5 medium at $17.69 vs. o3-pro at $146.32).","intents":["Compare cost-efficiency of different LLM providers for code editing tasks","Identify which models offer best performance-per-dollar","Budget for AI-assisted code editing workflows based on real API costs","Analyze whether expensive reasoning effort levels (high vs. medium) justify performance gains"],"best_for":["Organizations evaluating AI coding assistants for cost-sensitive deployments","Teams optimizing LLM API spend for code generation workflows","Researchers studying price-performance tradeoffs in LLM markets"],"limitations":["Cost reflects API pricing at time of evaluation (2025-06-28 to 2025-08-25); prices change frequently and may not reflect current rates","Cost includes only LLM API calls; does not include infrastructure, toolchain, or execution environment costs","No breakdown of cost by reasoning effort level or model size; only aggregate per-case cost reported","Exercism exercises are small (typically <100 lines of code); cost-per-case may not scale linearly to larger production codebases","No analysis of whether cost differences reflect token usage, reasoning time, or pricing model differences"],"requires":["Active API keys and billing accounts for LLM providers being evaluated","Cost tracking integration with LLM provider APIs (OpenAI, Anthropic, Gemini, etc.)","Standardized cost measurement methodology (not documented in available materials)"],"input_types":["LLM provider API calls (implicit)","test case count (225 exercises)","reasoning effort level (high, medium)"],"output_types":["cost per test case (e.g., $29.08 for gpt-5 high, $17.69 for gpt-5 medium)","total evaluation cost (cost per case × 225)","cost-efficiency ranking (performance per dollar)"],"categories":["data-processing-analysis","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.integration.and.model.comparison","name":"multi-provider llm integration and model comparison","description":"Integrates with 12+ LLM providers (OpenAI, Anthropic, Gemini, GROQ, LM Studio, xAI, Azure, Cohere, DeepSeek, Ollama, OpenRouter, GitHub Copilot, Vertex AI, Amazon Bedrock) via Aider CLI, enabling evaluation of diverse models on the same benchmark. Supports configurable reasoning effort levels (high, medium) per model. Leaderboard aggregates results across providers, allowing direct performance comparison.","intents":["Compare code editing performance across different LLM providers and model families","Evaluate proprietary models (gpt-5, o3-pro) against open-source alternatives (DeepSeek, Ollama)","Measure impact of reasoning effort configuration on performance and cost","Identify which provider offers best cost-performance for code editing tasks"],"best_for":["Teams evaluating multiple LLM providers for code editing workflows","Researchers comparing model families (OpenAI vs. Anthropic vs. Gemini) on code tasks","Organizations with multi-cloud or multi-provider strategies"],"limitations":["Requires separate API keys for each provider; no unified authentication","Provider availability and model versions change over time; leaderboard may include deprecated models","Reasoning effort levels (high, medium) are not standardized across providers; interpretation varies","No control for model training data or knowledge cutoff dates; contamination risk varies by provider","Evaluation time varies significantly by provider (gpt-5 medium: 7.4 hours, o3-pro: unknown but likely longer)","Some providers (LM Studio, Ollama) are self-hosted; evaluation environment and hardware not standardized"],"requires":["API keys for desired LLM providers (OpenAI, Anthropic, Gemini, GROQ, xAI, Cohere, DeepSeek, etc.)","Aider CLI tool with provider-specific integrations","Network access to provider APIs or local LLM infrastructure (for Ollama, LM Studio)"],"input_types":["model identifier (e.g., 'openai/gpt-5', 'anthropic/claude-3.5-sonnet')","reasoning effort level (high, medium)","API credentials"],"output_types":["performance metrics (pass rate, well-formedness percentage)","cost metrics (per-case cost, total evaluation cost)","error categorization (syntax, indentation, timeouts, context exhaustion)","leaderboard ranking across all providers"],"categories":["tool-use-integration","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_5","uri":"capability://search.retrieval.leaderboard.publication.and.performance.tracking","name":"leaderboard publication and performance tracking","description":"Maintains a public leaderboard (https://aider.chat/docs/leaderboards) ranking models by code editing performance, cost, and well-formedness metrics. Leaderboard includes metadata (test date, Aider version, commit hash, reasoning effort level) enabling reproducibility tracking. Updates with new model evaluations over time (data from 2025-06-28 to 2025-08-25 visible in current leaderboard).","intents":["Track performance trends of AI coding assistants over time","Identify top-performing models for code editing tasks","Compare models on standardized metrics (pass rate, cost, well-formedness)","Enable model developers to benchmark against published results"],"best_for":["AI model developers tracking competitive performance","Organizations selecting coding assistants based on published benchmarks","Researchers studying trends in code generation model capabilities"],"limitations":["Submission process is undocumented; unclear how new models are added to leaderboard","No historical data or trend analysis; only current leaderboard snapshot visible","Leaderboard includes only models evaluated by Aider team; no community submissions visible","No statistical significance testing or confidence intervals; single-point measurements only","Reasoning effort levels (high, medium) are not standardized; comparison across providers may conflate model capability with compute allocation","No baseline or random performance reported; difficult to assess absolute performance levels","Data contamination risk not addressed; unclear if test set is held-out or if models were trained on Exercism data"],"requires":["Public internet access to https://aider.chat/docs/leaderboards","Aider team evaluation infrastructure (for adding new models)"],"input_types":["model evaluation results (pass rate, cost, error metrics)","metadata (test date, Aider version, commit hash, reasoning effort)"],"output_types":["ranked leaderboard (sorted by pass rate or cost-efficiency)","performance metrics per model (pass rate 1, pass rate 2, well-formedness %, cost)","error breakdown (syntax errors, indentation errors, timeouts, context exhaustion, lazy comments)","metadata (test date range, Aider version, commit hash)"],"categories":["search-retrieval","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_6","uri":"capability://code.generation.editing.error.categorization.and.diagnostic.reporting","name":"error categorization and diagnostic reporting","description":"Categorizes code generation failures into specific error types: syntax errors, indentation errors, context window exhaustion, test timeouts, and lazy comments (incomplete implementations). Reports error counts per model, enabling diagnostic analysis of failure modes. Distinguishes between format errors (malformed diff output) and functional errors (test case failures).","intents":["Diagnose why models fail on code editing tasks (syntax vs. logic vs. resource constraints)","Identify systematic weaknesses (e.g., indentation handling in specific languages)","Detect resource constraints (context window limits, execution timeouts) affecting model performance","Compare error profiles across models to understand capability differences"],"best_for":["Model developers debugging code generation failures","Teams selecting models based on failure mode profiles","Researchers studying systematic weaknesses in LLM code generation"],"limitations":["Error categories are coarse-grained; no sub-categorization by language or exercise type","Lazy comments (incomplete implementations) are tracked but not analyzed; unclear what patterns trigger this behavior","No analysis of whether errors are systematic (e.g., always failing on specific language features) or random","Context window exhaustion is tracked but not analyzed; unclear which models hit limits or why","No measurement of error recovery; unclear if models can correct errors in multi-turn interactions"],"requires":["Error detection and categorization logic (included in Aider evaluation harness)","Test case execution environment with timeout and resource monitoring"],"input_types":["model output (code edits)","test case results (pass/fail, error messages)","execution logs (timeouts, context window usage)"],"output_types":["error count per category: syntax errors, indentation errors, context window exhaustion, test timeouts, lazy comments","error rate per model (e.g., 0 syntax errors, 0 indentation errors, 0 context exhaustion, 3 timeouts, 3 lazy comments for gpt-5 high)","error distribution across test set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_7","uri":"capability://data.processing.analysis.exercism.based.test.case.dataset.with.225.exercises","name":"exercism-based test case dataset with 225 exercises","description":"Uses 225 coding exercises from the Exercism platform as test cases, covering 6+ programming languages (C++, Go, Java, JavaScript, Python, Rust). Exercises are pedagogical in nature, ranging from basic syntax to intermediate algorithms. Test cases include both input/output specifications and language-specific test runners. Dataset is fixed and public, enabling reproducible evaluation.","intents":["Provide standardized, language-diverse test cases for code editing evaluation","Enable reproducible benchmarking across different models and time periods","Measure code editing ability on real-world pedagogical exercises","Support multi-language evaluation with consistent test infrastructure"],"best_for":["Benchmark maintainers seeking standardized test cases","Researchers studying multi-language code generation","Teams evaluating models on diverse programming languages"],"limitations":["Only 225 exercises total; limited statistical power for language-specific analysis (likely ~37 exercises per language)","Exercism exercises are pedagogical, not representative of production codebases with cross-file dependencies, architectural complexity, or performance constraints","Exercise difficulty is not stratified; all exercises treated equally in aggregate metrics","Language distribution across 225 exercises is not documented; unclear if distribution is balanced","HIGH DATA CONTAMINATION RISK: Exercism is a public platform with widely available exercises; no evidence that models were excluded from training on this data or that test set is held-out","Exercises are isolated; no measurement of cross-file refactoring, architectural understanding, or integration correctness","No measurement of code quality attributes (readability, maintainability, performance, security) beyond test case passage"],"requires":["Access to Exercism exercise definitions and test cases (included in Aider benchmark)","Language-specific test runners and compilers"],"input_types":["exercise description (natural language problem statement)","starter code (partial implementation or skeleton)","test case specifications (language-specific)"],"output_types":["pass/fail verdict per exercise","test output (stdout, stderr)","execution time per exercise"],"categories":["data-processing-analysis","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_8","uri":"capability://tool.use.integration.aider.cli.integration.for.benchmark.execution","name":"aider cli integration for benchmark execution","description":"Provides command-line interface (Aider CLI) for executing benchmark evaluations locally or remotely. CLI accepts model identifier, reasoning effort level, and API credentials, then orchestrates test case execution, result collection, and leaderboard submission. Supports 12+ LLM providers via unified interface. Version 0.86.2.dev or later includes benchmark evaluation capabilities.","intents":["Run benchmark evaluations locally without manual test case management","Evaluate custom or proprietary models on the benchmark","Integrate benchmark execution into CI/CD pipelines or automated evaluation workflows","Submit results to public leaderboard for comparison with other models"],"best_for":["Model developers evaluating their models on the benchmark","Teams automating code editing evaluation workflows","Researchers running large-scale model comparisons"],"limitations":["CLI submission process is undocumented; unclear how to submit results to leaderboard","Evaluation time is long (7-13+ hours per model); not suitable for rapid iteration","Requires API keys for LLM providers; no support for local-only evaluation (except Ollama, LM Studio)","No built-in parallelization; evaluation is sequential across 225 test cases","Error handling and retry logic are not documented; unclear how transient API failures are handled","No support for custom test cases; only Exercism exercises are supported"],"requires":["Aider CLI tool (version 0.86.2.dev or later)","Python 3.8+ runtime","API keys for desired LLM providers","Language-specific toolchains (C++ compiler, Go runtime, Java JDK, Node.js, Python, Rust)","Network access to LLM provider APIs"],"input_types":["model identifier (e.g., 'openai/gpt-5')","reasoning effort level (high, medium)","API credentials (environment variables or config file)"],"output_types":["evaluation results (pass rate, cost, error metrics)","detailed test case results (per-exercise pass/fail)","leaderboard submission (if enabled)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__cap_9","uri":"capability://data.processing.analysis.reasoning.effort.level.configuration.and.cost.performance.tradeoff.analysis","name":"reasoning effort level configuration and cost-performance tradeoff analysis","description":"Supports configurable reasoning effort levels (high, medium) per model, enabling cost-performance tradeoff analysis. High effort typically allocates more compute (longer inference time, more tokens) for potentially better performance. Leaderboard reports both effort levels separately, revealing performance and cost differences (e.g., gpt-5 high: 88.0% at $29.08 vs. gpt-5 medium: 86.7% at $17.69).","intents":["Analyze cost-performance tradeoffs between reasoning effort levels","Identify whether expensive reasoning effort justifies performance gains","Optimize model selection for cost-sensitive deployments","Compare models at equivalent reasoning effort levels"],"best_for":["Organizations optimizing LLM API spend for code editing","Teams evaluating whether high-effort reasoning is worth the cost","Researchers studying compute-performance tradeoffs in LLMs"],"limitations":["Reasoning effort semantics are not standardized across providers; high/medium may mean different things for OpenAI vs. Anthropic vs. Gemini","No analysis of what reasoning effort actually does (token count, inference time, reasoning steps); only aggregate cost and performance reported","Limited data points: only 2 effort levels tested (high, medium); no fine-grained analysis","Effort level impact varies by model: gpt-5 shows 1.3% performance difference (88.0% vs. 86.7%), but o3-pro data is incomplete","No measurement of whether effort level affects error types (e.g., does high effort reduce syntax errors but not timeouts?)"],"requires":["LLM provider support for reasoning effort configuration (OpenAI, Anthropic, Gemini, etc.)","Aider CLI with reasoning effort parameter"],"input_types":["reasoning effort level (high, medium)","model identifier"],"output_types":["performance metrics (pass rate) per effort level","cost metrics (per-case cost) per effort level","cost-performance ratio (performance per dollar)"],"categories":["data-processing-analysis","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider-polyglot__headline","uri":"capability://testing.quality.ai.coding.assistant.benchmark","name":"ai coding assistant benchmark","description":"Aider Polyglot is a benchmark for evaluating AI coding assistants' ability to edit code correctly across multiple programming languages, providing a comprehensive assessment of their performance in real-world coding tasks.","intents":["best AI coding assistant benchmark","benchmark for AI code editors","AI coding assistant performance evaluation","top AI coding benchmarks","AI coding assistant testing framework"],"best_for":["developers evaluating AI coding tools","researchers assessing AI performance"],"limitations":["does not measure code generation","limited to editing tasks"],"requires":["access to Aider tool"],"input_types":["codebases","instructions"],"output_types":["performance scores","leaderboard rankings"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":62,"verified":false,"data_access_risk":"high","permissions":["API key for at least one supported LLM provider (OpenAI, Anthropic, Gemini, GROQ, xAI, Cohere, DeepSeek, or others)","Aider CLI tool (version 0.86.2.dev or later based on leaderboard data)","Python 3.8+ (for Aider runtime)","Network access to LLM provider APIs","Language-specific toolchains for test execution (C++ compiler, Go runtime, Java JDK, Node.js, Python interpreter, Rust toolchain)","Model output in unified diff format (standard patch format)","Diff parser implementation (included in Aider evaluation harness)","Git repository for Aider with commit history","Timestamp recording for each evaluation run","Language-specific test runners and compilers: C++ compiler (g++/clang), Go runtime, Java JDK, Node.js, Python 3.8+, Rust toolchain"],"failure_modes":["Only 225 test cases total across all languages; no stratification by difficulty level or language distribution reported","Exercism exercises are public pedagogical problems, not representative of production codebases with cross-file dependencies or architectural complexity","High data contamination risk: no evidence that test set is held-out or that models were excluded from training on Exercism data","Methodology for 'Pass rate 1' metric is undocumented; only 'Pass rate 2' is clearly defined, creating opacity in scoring","No statistical significance testing, confidence intervals, or multiple runs reported; single-point measurements only","Benchmark only accepts diff-based edit format; alternative valid edit formats (full file replacement) may not be supported","Evaluation time ranges from 7-13+ hours per model depending on reasoning effort level, limiting rapid iteration","No measurement of code quality attributes (readability, maintainability, performance, security) beyond test case passage","Only validates diff format; does not measure whether edits are semantically correct or preserve code behavior","No support for alternative edit formats (full file replacement, line-based edits, AST-based patches); models must output unified diff","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:19.836Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=aider-polyglot","compare_url":"https://unfragile.ai/compare?artifact=aider-polyglot"}},"signature":"Pktf0zCcCWHNBLlDtQ/t082tyuLZGRL0j6aXDfNmTgODWu6yJoCSVTyh0XbIgvDaj443DL0cwgnghKxYFK6pAg==","signedAt":"2026-06-21T21:02:05.606Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aider-polyglot","artifact":"https://unfragile.ai/aider-polyglot","verify":"https://unfragile.ai/api/v1/verify?slug=aider-polyglot","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}