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| Pricing | Free | Free |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: 22B parameter model specifically optimized for code with 32K context window trained on 80+ languages, enabling longer-range code understanding than smaller models while remaining deployable on consumer hardware via HuggingFace. Instruction-following capability built into base training rather than requiring separate fine-tuning stages.
vs alternatives: Larger context window (32K) than Codex/GPT-3.5 (8K) and comparable to GPT-4 while being smaller and faster to run locally, with explicit multi-language training across 80+ languages vs Copilot's narrower focus on Python/JavaScript/TypeScript
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.
Unique: Dedicated FIM endpoint (codestral.mistral.ai) optimized for IDE latency with streaming response support, separate from general-purpose API endpoint. Allows IDE plugins to send only prefix/suffix context rather than full files, reducing payload size and privacy exposure while maintaining code understanding through bidirectional context.
vs alternatives: Dedicated low-latency endpoint for IDE use cases vs Copilot's cloud-only architecture, with explicit FIM support vs GitHub Copilot's proprietary completion mechanism, and open-weight model availability for self-hosting vs Copilot's closed API-only access
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.
Unique: Dual-licensing model with free Non-Production License for research and evaluation vs commercial licensing for production use. Enables free evaluation and research while maintaining commercial control vs fully open-source models with permissive licenses.
vs alternatives: Free evaluation license for research vs competitors requiring paid licenses for any use; commercial licensing option vs fully open-source models without commercial support; case-by-case commercial licensing vs fixed commercial pricing
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.
Unique: SQL generation evaluated on Spider benchmark as part of 80+ language support vs competitors with separate SQL-specific models. Unified model for SQL and other languages vs specialized SQL generation tools.
vs alternatives: Unified model for SQL and code generation vs separate SQL-specific tools; multi-database support vs database-specific generators
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.
Unique: FIM evaluation demonstrates competitive performance with 22B parameters vs DeepSeek Coder 33B, highlighting parameter efficiency advantage while maintaining comparable FIM quality for IDE integration
vs alternatives: Smaller parameter count (22B vs 33B) with comparable FIM performance enables faster inference and lower computational requirements compared to DeepSeek Coder
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.
Unique: Single 22B model trained on 80+ languages with unified transformer architecture vs competitors' language-specific models or narrower language coverage. Explicit training on less common languages (Fortran, Swift, Bash) alongside mainstream languages, enabling niche language support without separate model deployments.
vs alternatives: Broader language coverage (80+ vs Copilot's ~15 primary languages) with single model vs Codeium's language-specific optimization, though with unknown per-language quality tradeoffs
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.
Unique: Evaluated on MBPP benchmark specifically for test generation capability, indicating explicit training signal for synthesizing test cases rather than incidental capability. Generates tests from code context and instructions rather than requiring separate test specification format.
vs alternatives: Dedicated evaluation on test generation benchmarks vs general-purpose code models that treat testing as secondary capability; multi-language test generation vs language-specific test generation tools
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.
Unique: 32K context window specifically optimized for repository-level understanding vs smaller context windows in competing models. Evaluated on RepoBench benchmark for cross-file code completion, indicating explicit training for repository-aware code generation rather than single-file focus.
vs alternatives: 4x larger context window than GPT-3.5 (8K) enabling multi-file repository understanding in single request vs Copilot's file-by-file approach; outperforms on RepoBench according to source material vs general-purpose code models
+5 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs Codestral at 59/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities