{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"codestral","slug":"codestral","name":"Codestral","type":"model","url":"https://mistral.ai/news/codestral/","page_url":"https://unfragile.ai/codestral","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"codestral__cap_0","uri":"capability://code.generation.editing.instruction.following.code.generation.with.32k.context.window","name":"instruction-following code generation with 32k context window","description":"Generates code from natural language instructions using a 22B parameter decoder-only transformer trained on 80+ programming languages. Processes up to 32K tokens of context (approximately 24K tokens of code + instructions), enabling multi-file code generation and understanding of large codebases within a single request. Implements standard instruction-following fine-tuning patterns built into the base model training rather than separate RLHF stages.","intents":["Generate complete functions or modules from English descriptions","Understand and extend existing code by providing full file context","Generate code that respects patterns from large codebases within context window","Build multi-step code generation workflows where previous outputs inform next steps"],"best_for":["developers building code generation features into IDEs or editors","teams using Mistral API for server-side code generation workflows","engineers prototyping code generation agents with moderate context requirements"],"limitations":["32K token context window is hard limit — cannot process codebases or requirements larger than ~24K tokens of actual content","Benchmark scores on standard tasks (HumanEval, MBPP) not disclosed in source material, only comparative claims provided","No multi-modal support — cannot generate code from images, diagrams, or mixed media inputs","Instruction-following quality varies significantly across the 80+ supported languages with no per-language performance breakdown available"],"requires":["API key for Mistral (codestral.mistral.ai endpoint for dedicated access or api.mistral.ai for standard access)","HTTP client capable of making REST API calls","Understanding of prompt engineering for instruction-following models"],"input_types":["text (natural language instructions)","code (existing code context for continuation or extension)"],"output_types":["code (generated source code in any of 80+ supported languages)"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_1","uri":"capability://code.generation.editing.fill.in.the.middle.code.completion.for.ide.integration","name":"fill-in-the-middle code completion for ide integration","description":"Implements fill-in-the-middle (FIM) mechanism enabling IDE plugins to request code completion at arbitrary positions within a file by providing prefix and suffix context. The model processes both left and right context to predict the missing middle section, supporting real-time IDE workflows where users type in the middle of incomplete code. Requires specific prompt formatting (details not disclosed) and routes through dedicated codestral.mistral.ai endpoint optimized for low-latency IDE requests.","intents":["Enable real-time code completion suggestions as developers type in the middle of functions","Build IDE plugins that show completion suggestions without requiring full file rewrites","Support inline code generation where developers specify both what comes before and after the desired code","Integrate code completion into editors without exposing full file content to external APIs"],"best_for":["IDE plugin developers building VS Code, JetBrains, or Neovim extensions","teams deploying code completion features with strict latency requirements (<500ms)","organizations wanting to avoid sending full files to cloud APIs by using prefix/suffix context"],"limitations":["FIM prompt format specifications not disclosed — requires reverse-engineering from API behavior or Mistral documentation","Latency benchmarks not provided despite 'performance/latency space' claims — actual IDE responsiveness unknown","Requires dedicated codestral.mistral.ai endpoint which is in beta with 8-week free period; post-beta pricing unknown","FIM quality and accuracy across 80+ languages not benchmarked or disclosed"],"requires":["API key for codestral.mistral.ai dedicated endpoint (currently free for 8 weeks, then pricing TBD)","IDE plugin framework (VS Code API, JetBrains Plugin SDK, etc.)","HTTP client with support for streaming responses (for real-time completion display)","Understanding of FIM prompt formatting (undocumented — requires API exploration)"],"input_types":["text (code prefix before cursor)","text (code suffix after cursor)","text (optional instruction or context)"],"output_types":["text (generated code for middle section)","streaming tokens (for real-time display in IDE)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_10","uri":"capability://safety.moderation.non.production.license.with.commercial.licensing.option","name":"non-production license with commercial licensing option","description":"Codestral weights distributed under Mistral AI Non-Production License restricting use to research, testing, and evaluation. Commercial use requires explicit commercial license agreement from Mistral AI with terms and pricing determined on case-by-case basis. Enables free evaluation and research while protecting Mistral's commercial interests through licensing restrictions.","intents":["Evaluate Codestral for research and prototyping without commercial licensing costs","Deploy code generation in production environments with proper commercial licensing","Understand licensing requirements before building products on Codestral"],"best_for":["researchers and students evaluating code generation models","teams prototyping code generation features before production deployment","organizations planning commercial deployment and needing licensing clarity"],"limitations":["Non-Production License prohibits commercial use — any revenue-generating use requires commercial license","Commercial license pricing and terms unknown — requires contacting Mistral AI team","Commercial licensing process and timeline not documented — may delay production deployment","License restrictions apply to both API usage and self-hosted deployments","No clear definition of 'commercial use' — ambiguous cases (internal tools, free products with ads, etc.) require clarification"],"requires":["Understanding of Mistral AI Non-Production License terms","For commercial use: contact with Mistral AI sales team to negotiate commercial license"],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_11","uri":"capability://code.generation.editing.sql.code.generation.with.spider.benchmark.evaluation","name":"sql code generation with spider benchmark evaluation","description":"Generates SQL queries from natural language descriptions or existing database schemas. Evaluated on Spider benchmark (complex SQL generation from text) but specific scores not disclosed. Supports SQL generation for various databases and query types as part of 80+ language support.","intents":["Generate SQL queries from natural language descriptions of data requirements","Create database queries that work with existing schemas and table structures","Generate complex SQL including joins, aggregations, and subqueries from descriptions"],"best_for":["developers building SQL generation tools or query builders","teams automating database query generation from natural language","data teams generating exploratory SQL queries"],"limitations":["SQL generation quality unknown — Spider benchmark evaluation mentioned but no scores provided","No database-specific optimization — single model for all SQL dialects (MySQL, PostgreSQL, T-SQL, etc.)","No schema validation or error checking — generated SQL may not work with actual database schemas","Complex query generation quality unknown — performance on joins, aggregations, and subqueries not benchmarked"],"requires":["API key for Mistral","Database schema context for accurate query generation","SQL knowledge to validate and debug generated queries"],"input_types":["text (natural language query descriptions)","text (database schema definitions)"],"output_types":["code (SQL queries)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_12","uri":"capability://code.generation.editing.fill.in.the.middle.performance.comparison.with.deepseek.coder.33b","name":"fill-in-the-middle performance comparison with deepseek coder 33b","description":"Codestral FIM capability evaluated against DeepSeek Coder 33B on HumanEval pass@1 metrics across Python, JavaScript, and Java, demonstrating competitive FIM performance despite smaller parameter count (22B vs 33B). Evaluation highlights efficiency advantage of smaller model with comparable FIM quality.","intents":["Compare FIM performance between Codestral and DeepSeek Coder","Evaluate efficiency of 22B parameter model vs 33B alternative","Assess FIM suitability for IDE integration based on competitive benchmarks","Determine parameter efficiency trade-offs"],"best_for":["teams evaluating FIM models for IDE integration","organizations comparing parameter efficiency vs performance","developers assessing inference cost trade-offs"],"limitations":["Specific FIM scores not disclosed — only comparative positioning provided","Evaluation limited to 3 languages (Python, JavaScript, Java) out of 80+ supported","No evaluation of FIM latency or inference speed — only accuracy metrics","Comparison limited to single alternative (DeepSeek Coder 33B) — no broader competitive analysis","No evaluation of FIM performance on real IDE usage patterns or file sizes"],"requires":["Understanding of FIM evaluation methodology","Access to comparative benchmark results"],"input_types":["code (prefix and suffix for FIM)"],"output_types":["evaluation metrics (pass@1)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_2","uri":"capability://code.generation.editing.multi.language.code.generation.across.80.programming.languages","name":"multi-language code generation across 80+ programming languages","description":"Trained on diverse dataset spanning 80+ programming languages including Python, JavaScript, TypeScript, Java, C++, C, Rust, Go, PHP, C#, Swift, Bash, SQL, Fortran and others. Model learns language-specific syntax, idioms, and patterns through unified transformer architecture rather than language-specific models. Supports code generation, completion, and instruction-following in any of the 80+ languages with single model inference.","intents":["Generate code in any supported language from natural language descriptions without model switching","Build polyglot code generation tools that work across frontend, backend, and infrastructure code","Translate code between languages by providing source code and target language instruction","Generate language-specific test cases, documentation, and boilerplate across multiple languages"],"best_for":["full-stack development teams working across multiple languages (Python backend, TypeScript frontend, Rust services)","platform teams building code generation features that must support diverse tech stacks","DevOps and infrastructure teams generating code across Bash, Python, Go, and Terraform"],"limitations":["Performance variance across 80+ languages unknown — no per-language benchmark scores disclosed; some languages likely undertrained","Idiom and best-practice quality varies by language — model trained on 'diverse dataset' but composition and filtering methodology not disclosed","No language-specific fine-tuning or specialized variants — single model must handle all languages, potentially limiting optimization for specific domains","SQL generation quality unknown despite Spider benchmark evaluation; no scores provided"],"requires":["API key for Mistral (codestral.mistral.ai or api.mistral.ai)","Ability to specify target language in prompt (no automatic language detection)","Knowledge of language-specific syntax and conventions to validate generated code"],"input_types":["text (natural language instructions)","code (existing code in any supported language for context or translation)"],"output_types":["code (generated source code in specified language)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_3","uri":"capability://code.generation.editing.test.generation.and.validation.code.synthesis","name":"test generation and validation code synthesis","description":"Generates unit tests, integration tests, and validation code from function signatures, docstrings, and existing code. Evaluated on MBPP (Mostly Basic Python Programming) benchmark for test generation capability. Synthesizes test cases that cover edge cases, error conditions, and normal operation paths based on code context and instruction prompts.","intents":["Generate unit test suites for existing functions without manual test writing","Create test cases that cover edge cases and error conditions identified from code analysis","Synthesize validation and assertion code for data processing pipelines","Generate integration tests that verify interactions between multiple code modules"],"best_for":["development teams wanting to increase test coverage without manual test writing","CI/CD pipelines that auto-generate test cases for code review workflows","teams building code quality tools that require automated test generation"],"limitations":["MBPP benchmark score not disclosed — only claimed evaluation on benchmark, no actual performance metrics provided","Test quality and coverage metrics unknown — generated tests may miss critical edge cases or have false positives","No integration with test frameworks — generates raw test code that requires manual adaptation to pytest, Jest, JUnit, etc.","Limited to languages with strong training representation; test generation quality unknown for less common languages"],"requires":["API key for Mistral","Function signatures or code context to generate tests from","Test framework knowledge to adapt generated tests to specific testing tools"],"input_types":["code (function signatures, docstrings, implementation)","text (test requirements or edge cases to cover)"],"output_types":["code (test code in same language as input)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_4","uri":"capability://code.generation.editing.long.range.repository.level.code.understanding.with.32k.context","name":"long-range repository-level code understanding with 32k context","description":"Leverages 32K token context window to maintain understanding of large code repositories and multi-file dependencies. Evaluated on RepoBench benchmark for repository-level code completion where model must understand cross-file references, imports, and function definitions across multiple files. Outperforms competitors on RepoBench according to source material, enabling code generation that respects existing codebase patterns and dependencies.","intents":["Generate code that correctly imports and uses functions defined in other files within the same repository","Complete functions that depend on types, classes, or utilities defined elsewhere in the codebase","Understand and extend large codebases by providing full context of related files and dependencies","Generate code that follows existing patterns and conventions from multiple files in the repository"],"best_for":["teams working on large monorepos where code generation must respect cross-file dependencies","developers extending existing codebases where generated code must integrate with existing modules","code generation tools that need to understand repository structure and conventions"],"limitations":["32K token context window limits repository size that can be understood in single request — large monorepos may exceed context","RepoBench outperformance claimed but no actual scores provided — competitive advantage magnitude unknown","No built-in repository indexing or semantic understanding — requires manual context assembly by caller","Cross-file dependency resolution depends on quality of context provided; model cannot automatically fetch related files"],"requires":["API key for Mistral","Ability to assemble relevant code context from multiple files (32K token budget)","Understanding of repository structure to provide meaningful cross-file context"],"input_types":["code (multiple files concatenated with file boundaries marked)","text (instructions for code to generate)"],"output_types":["code (generated code that integrates with provided repository context)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_5","uri":"capability://tool.use.integration.api.based.code.generation.with.two.deployment.endpoints","name":"api-based code generation with two deployment endpoints","description":"Provides two distinct API endpoints for different use cases: (1) codestral.mistral.ai — dedicated endpoint for IDE plugins with free beta access (8 weeks), personal API key management, and optimized latency for real-time completion; (2) api.mistral.ai — standard endpoint with token-based billing, organization-level rate limits, and support for batch queries and third-party applications. Both endpoints support streaming responses for real-time output display.","intents":["Integrate code generation into IDE plugins via low-latency dedicated endpoint","Build server-side code generation services with organization-level rate limiting and billing","Create batch code generation workflows that process multiple requests efficiently","Deploy code generation features with flexible pricing models (free tier for IDEs, pay-per-token for production)"],"best_for":["IDE plugin developers who need low-latency, free-tier access for initial development","teams building production code generation services with predictable token-based billing","organizations wanting separate API keys for IDE plugins vs backend services"],"limitations":["Dedicated endpoint (codestral.mistral.ai) in beta with 8-week free period; post-beta pricing unknown and may disrupt free IDE integrations","No latency benchmarks provided despite 'performance/latency space' claims — actual IDE responsiveness unknown","Personal API key management on dedicated endpoint limits organization-wide deployment and monitoring","Standard endpoint (api.mistral.ai) requires token-based billing with no free tier — cost per request unknown","No built-in rate limiting or quota management on client side — requires external implementation"],"requires":["API key from Mistral AI (separate keys for each endpoint)","HTTP client library (Python requests, JavaScript fetch, etc.)","Understanding of Mistral API authentication and request/response format","For dedicated endpoint: waitlist access (currently gated, availability unknown)"],"input_types":["text (code generation prompts)","code (context for completion or instruction-following)"],"output_types":["text (generated code)","streaming tokens (for real-time display)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_6","uri":"capability://code.generation.editing.open.weight.model.download.and.self.hosted.inference","name":"open-weight model download and self-hosted inference","description":"Codestral weights available for download via HuggingFace, enabling self-hosted inference on local hardware or private infrastructure. Model distributed in open-weight format (specific serialization format not disclosed — likely safetensors or GGUF) under Mistral AI Non-Production License. Supports local deployment without API calls, enabling offline code generation, private data handling, and custom fine-tuning.","intents":["Deploy code generation on private infrastructure without sending code to external APIs","Run code generation offline or in air-gapped environments without internet connectivity","Fine-tune Codestral on proprietary code patterns and domain-specific languages","Avoid API costs and latency by running inference locally on GPU or CPU hardware"],"best_for":["enterprises with strict data privacy requirements prohibiting cloud API usage","teams building specialized code generation for proprietary languages or frameworks","researchers fine-tuning code generation models on domain-specific datasets","organizations wanting to avoid per-token API costs for high-volume code generation"],"limitations":["Hardware requirements for inference not disclosed — 22B parameters likely requires 40-50GB VRAM for full precision, 20-25GB for fp16","Inference speed and throughput not benchmarked — actual latency for local deployment unknown","Quantization support not disclosed — unclear if int8, int4, or other quantization formats available for reduced VRAM","Non-Production License restricts commercial use — requires explicit commercial license agreement from Mistral for production deployments","No managed self-hosting option — requires manual infrastructure setup, monitoring, and scaling","Fine-tuning methodology and best practices not documented — requires external research or Mistral support"],"requires":["GPU with sufficient VRAM (estimated 40-50GB for full precision, 20-25GB for fp16 — unconfirmed)","Python 3.8+ with PyTorch or similar inference framework","HuggingFace account for model download","Understanding of model inference optimization (quantization, batching, etc.)","Commercial license from Mistral for production use (pricing/terms unknown)"],"input_types":["code (existing code context)","text (natural language instructions)"],"output_types":["code (generated source code)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_7","uri":"capability://code.generation.editing.multi.benchmark.evaluation.across.code.generation.tasks","name":"multi-benchmark evaluation across code generation tasks","description":"Evaluated on multiple code generation benchmarks: HumanEval (Python function generation), MBPP (Mostly Basic Python Programming for test generation), CruxEval (Python output prediction), RepoBench (repository-level code completion), Spider (SQL generation), and multi-language HumanEval variants (C++, Bash, Java, PHP, TypeScript, C#). Provides comparative performance claims across diverse code generation tasks without disclosing absolute scores.","intents":["Assess code generation quality across multiple programming languages and task types","Compare Codestral performance against competing models on standardized benchmarks","Understand model strengths and weaknesses across different code generation scenarios","Validate model suitability for specific code generation tasks before deployment"],"best_for":["teams evaluating code generation models for production deployment","researchers comparing model performance across multiple benchmarks","organizations making build-vs-buy decisions for code generation capabilities"],"limitations":["Absolute benchmark scores not disclosed — only comparative claims provided (e.g., 'outperforms competitors on RepoBench')","Benchmark selection biased toward tasks where Codestral performs well — no disclosure of benchmarks where it underperforms","HumanEval pass@1 scores for most languages not provided — only claimed evaluation without results","SQL generation quality (Spider benchmark) unknown — evaluated but no scores disclosed","Benchmark methodology and evaluation conditions not specified — reproducibility unknown","No confidence intervals or statistical significance testing provided for comparative claims"],"requires":["Understanding of benchmark methodologies (HumanEval, MBPP, etc.)","Access to benchmark datasets for independent evaluation","Ability to run inference and evaluate outputs against benchmark criteria"],"input_types":["benchmark test cases (function signatures, docstrings, requirements)"],"output_types":["code (generated solutions)","evaluation metrics (pass@1, pass@k, etc.)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_8","uri":"capability://code.generation.editing.instruction.following.code.generation.with.natural.language.prompts","name":"instruction-following code generation with natural language prompts","description":"Accepts natural language instructions and generates corresponding code without requiring specific prompt templates or few-shot examples. Instruction-following capability built into base model training rather than requiring separate fine-tuning. Supports diverse instruction types: function generation from descriptions, code refactoring requests, documentation generation, and code explanation tasks.","intents":["Generate functions from plain English descriptions without code examples","Request code refactoring or optimization improvements in natural language","Generate documentation, comments, and docstrings from code","Explain existing code or generate code that implements specific algorithms or patterns"],"best_for":["developers who prefer natural language prompting over code-based few-shot examples","non-technical users who can describe requirements in English but cannot write code","teams building conversational code generation interfaces"],"limitations":["Instruction-following quality varies by language and task type — no per-task performance metrics disclosed","Requires clear, specific instructions — ambiguous or vague prompts may generate incorrect code","No built-in instruction validation or error handling — generated code may not match intent","Instruction format and best practices not documented — requires prompt engineering experimentation"],"requires":["API key for Mistral","Ability to write clear, specific natural language instructions","Understanding of code generation prompt engineering"],"input_types":["text (natural language instructions)"],"output_types":["code (generated source code)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__cap_9","uri":"capability://tool.use.integration.streaming.response.output.for.real.time.code.display","name":"streaming response output for real-time code display","description":"Both API endpoints (codestral.mistral.ai and api.mistral.ai) support streaming responses where generated code is returned as a stream of tokens rather than waiting for full completion. Enables real-time display of generated code in IDEs and web interfaces as tokens are produced, improving perceived latency and user experience. Streaming tokens can be displayed incrementally without waiting for full response.","intents":["Display code generation results in real-time as they are produced rather than waiting for full completion","Build responsive IDE plugins that show code suggestions immediately without blocking","Create web-based code generation interfaces with streaming output display","Reduce perceived latency by showing partial results while generation continues"],"best_for":["IDE plugin developers building real-time code completion features","web application developers building code generation interfaces","teams prioritizing user experience and perceived responsiveness"],"limitations":["Streaming latency and time-to-first-token not benchmarked — actual responsiveness unknown","Requires client-side streaming implementation — not all HTTP clients support streaming by default","Token-by-token display may show incomplete or syntactically invalid code during streaming — requires client-side buffering or validation","Streaming adds complexity to error handling — errors may occur mid-stream after partial output displayed"],"requires":["HTTP client with streaming support (Server-Sent Events, chunked transfer encoding, etc.)","Client-side code to parse and display streaming tokens","UI framework capable of incremental content updates"],"input_types":["text (code generation prompts)"],"output_types":["streaming tokens (code generated incrementally)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codestral__headline","uri":"capability://code.generation.editing.ai.powered.code.generation.model","name":"ai-powered code generation model","description":"Codestral is a dedicated AI model for code generation, optimized for 80+ programming languages with a 32K context window, ideal for developers seeking advanced code completion and generation capabilities.","intents":["best AI code generation model","AI model for code completion","top code generation tools for Python","AI code assistant for JavaScript","best API for IDE code integration"],"best_for":["developers needing code assistance","teams looking for IDE integration"],"limitations":["not open source","commercial use requires licensing"],"requires":["API access"],"input_types":["code snippets","programming instructions"],"output_types":["generated code","code completions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["API key for Mistral (codestral.mistral.ai endpoint for dedicated access or api.mistral.ai for standard access)","HTTP client capable of making REST API calls","Understanding of prompt engineering for instruction-following models","API key for codestral.mistral.ai dedicated endpoint (currently free for 8 weeks, then pricing TBD)","IDE plugin framework (VS Code API, JetBrains Plugin SDK, etc.)","HTTP client with support for streaming responses (for real-time completion display)","Understanding of FIM prompt formatting (undocumented — requires API exploration)","Understanding of Mistral AI Non-Production License terms","For commercial use: contact with Mistral AI sales team to negotiate commercial license","API key for Mistral"],"failure_modes":["32K token context window is hard limit — cannot process codebases or requirements larger than ~24K tokens of actual content","Benchmark scores on standard tasks (HumanEval, MBPP) not disclosed in source material, only comparative claims provided","No multi-modal support — cannot generate code from images, diagrams, or mixed media inputs","Instruction-following quality varies significantly across the 80+ supported languages with no per-language performance breakdown available","FIM prompt format specifications not disclosed — requires reverse-engineering from API behavior or Mistral documentation","Latency benchmarks not provided despite 'performance/latency space' claims — actual IDE responsiveness unknown","Requires dedicated codestral.mistral.ai endpoint which is in beta with 8-week free period; post-beta pricing unknown","FIM quality and accuracy across 80+ languages not benchmarked or disclosed","Non-Production License prohibits commercial use — any revenue-generating use requires commercial license","Commercial license pricing and terms unknown — requires contacting Mistral AI team","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.547Z","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=codestral","compare_url":"https://unfragile.ai/compare?artifact=codestral"}},"signature":"17FKjBYdY6MAkVZ531j8tyy0PP0R3dtdM6irTg7NZp4DJzOiYmMMxftkmNCyyEoCoCG6VQ2k9EPiFVwDJH9JAw==","signedAt":"2026-06-19T18:09:49.823Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codestral","artifact":"https://unfragile.ai/codestral","verify":"https://unfragile.ai/api/v1/verify?slug=codestral","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"}}