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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates code from natural language descriptions using a DeepSeekMoE sparse architecture that routes input tokens through a gating network to selectively activate only 21B of 236B total parameters during inference. The router network dynamically chooses which expert sub-networks process each token, enabling efficient computation while maintaining GPT-4-Turbo-level code generation quality. This sparse activation pattern reduces memory footprint and latency compared to dense models while preserving multi-language code generation across 338 programming languages.
Unique: Uses DeepSeekMoE framework with dynamic router-based expert selection to activate only 21B/236B parameters per token, achieving 90.2% HumanEval performance while reducing inference memory by ~60% compared to dense 236B models through sparse activation patterns
vs alternatives: Outperforms Llama-2-70B and Code-Llama-70B on HumanEval (90.2% vs 81.8% and 85.5%) while using 3.3x fewer active parameters, and matches GPT-4-Turbo performance with open-source weights and permissive licensing
Processes up to 128,000 tokens of context enabling analysis and generation across entire code repositories, multiple files, and extensive documentation. The extended context is implemented through rotary position embeddings (RoPE) and optimized attention mechanisms that scale efficiently with the longer sequence length. This allows the model to maintain coherence across large codebases, understand cross-file dependencies, and generate code that respects repository-wide patterns and conventions.
Unique: Extends context from 16K to 128K tokens using rotary position embeddings and optimized attention, enabling single-pass analysis of entire repositories without chunking or sliding-window approaches, while maintaining coherence across 8x longer sequences
vs alternatives: Provides 8x longer context than DeepSeek-Coder-V1 (16K) and matches Claude 3.5 Sonnet's 200K context for code tasks while remaining open-source and deployable locally
Maintains strong general language understanding capabilities despite specialization in code, enabling the model to handle natural language questions, summarization, translation, and reasoning tasks. This is achieved through training on 6 trillion tokens including both code and natural language data, preserving the base DeepSeek-V2 general capabilities while enhancing code-specific performance. The model can switch between code and natural language tasks without degradation.
Unique: Maintains strong general language understanding from base DeepSeek-V2 while specializing in code through continued pre-training on 6 trillion tokens, enabling single-model support for mixed code/natural language tasks
vs alternatives: Provides better general language understanding than code-only models (Code-Llama) while maintaining code performance comparable to GPT-4-Turbo, enabling unified code+language workflows
Supports multiple quantization formats (FP8, INT8, INT4) enabling deployment on hardware with limited VRAM through reduced precision representations. Quantization is implemented through frameworks like GPTQ and AWQ that compress model weights while maintaining reasonable performance. The 236B model can be reduced to 8-16GB VRAM requirements through aggressive quantization, enabling deployment on consumer GPUs and edge devices.
Unique: Supports multiple quantization formats (FP8, INT8, INT4) through GPTQ/AWQ, reducing 236B model from 40GB to 8-16GB VRAM while maintaining 85-95% of original performance through post-training quantization
vs alternatives: Enables deployment on consumer GPUs through quantization support, whereas many code models require enterprise-grade hardware; trade-off is 5-15% quality loss vs full precision
Performs refactoring across multiple files by understanding inter-file dependencies and maintaining consistency across the codebase. The 128K context window enables loading multiple related files simultaneously, and the model can track variable definitions, function calls, and imports across files to generate refactoring changes that respect dependencies. This is implemented through careful prompt engineering that includes dependency information and cross-file references.
Unique: Leverages 128K context window to load and refactor multiple files simultaneously while tracking inter-file dependencies, enabling single-pass refactoring of related code without chunking or iterative passes
vs alternatives: Provides cross-file refactoring capabilities comparable to IDE refactoring tools (VS Code, IntelliJ) while remaining language-agnostic and deployable locally, vs proprietary cloud-based refactoring services
Translates code from one programming language to another while preserving semantic meaning and functionality. The model understands language-specific idioms, standard libraries, and design patterns, enabling it to generate idiomatic code in the target language rather than literal translations. This works through providing source code in one language and requesting translation to another, with optional constraints (preserve performance characteristics, use specific libraries, etc.).
Unique: Translates code across 338 languages while preserving semantic meaning through language-specific expert routing in MoE architecture. Trained on parallel code implementations across language families, enabling idiomatic translation rather than literal syntax conversion.
vs alternatives: Supports translation across 338 languages (vs GPT-4's ~50) and generates idiomatic target code through specialized training on parallel implementations; outperforms simple regex-based translation tools through semantic understanding of language patterns.
Completes partially written code across 338 programming languages by predicting the most probable next tokens based on context. The model was trained on 1.5 trillion code tokens spanning diverse language ecosystems, enabling it to understand syntax, idioms, and conventions for mainstream languages (Python, JavaScript, Java, C++) and niche languages (Rust, Go, Kotlin, Haskell, etc.). Completion works through standard next-token prediction with language-specific tokenization and vocabulary handling.
Unique: Trained on 1.5 trillion code tokens across 338 languages (expanded from 86 in V1), enabling single-model support for mainstream and niche languages without separate language-specific models or fine-tuning
vs alternatives: Supports 4x more languages than GitHub Copilot (which focuses on ~20 mainstream languages) and provides open-source weights for all 338 languages vs proprietary completion engines
Identifies and fixes bugs in code by analyzing error patterns, exception messages, and logical inconsistencies learned during training on 6 trillion tokens including buggy code examples and fixes. The model uses its 128K context window to understand the full scope of buggy code, trace execution paths, and suggest corrections. Debugging works through prompt engineering (e.g., 'Fix the bug in this code') or instruction-tuned variants that explicitly handle debugging tasks.
Unique: Leverages 6 trillion token training corpus including buggy code examples and fixes, combined with 128K context to understand multi-file bug patterns and generate contextually appropriate repairs without external debugging tools
vs alternatives: Provides open-source debugging capabilities comparable to GitHub Copilot's bug-fixing features while supporting 338 languages and enabling local deployment without API calls
+6 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 DeepSeek Coder V2 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