HumanEval ranks higher at 63/100 vs CodeLlama (7B, 13B, 34B, 70B) at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | CodeLlama (7B, 13B, 34B, 70B) | HumanEval |
|---|---|---|
| Type | Model | Benchmark |
| UnfragileRank | 23/100 | 63/100 |
| Adoption | 0 | 1 |
| Quality | 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Generates code from natural language prompts using Transformer-based architecture with four parameter variants (7B, 13B, 34B, 70B) allowing trade-offs between inference speed and code quality. Each variant is independently optimized for different hardware constraints and latency requirements, with the 7B model targeting edge devices and 70B targeting maximum code understanding. Inference is performed via Ollama's local execution engine or cloud API, with streaming token output for real-time code generation.
Unique: Offers four independently-optimized parameter sizes (7B-70B) built on Llama 2 architecture with code-specific pretraining, allowing developers to select optimal inference speed/quality tradeoff for their hardware; distributed via Ollama's quantized GGUF format enabling local execution without cloud dependency
vs alternatives: Faster local inference than cloud-only models (Copilot, GPT-4) with no API latency or rate limits, but lower code quality than larger proprietary models due to smaller parameter count and older training data
Implements bidirectional code infill using a special prompt format (<PRE>{prefix}<SUF>{suffix}<MID>) that allows the model to generate code between two existing code blocks. This capability leverages the model's ability to understand both preceding and following context simultaneously, enabling inline code completion within existing functions or methods. The FIM format is natively supported across all CodeLlama variants and works through standard API endpoints.
Unique: Implements bidirectional context awareness through explicit <PRE>/<SUF>/<MID> prompt format rather than relying on left-to-right generation, enabling the model to condition on both preceding and following code simultaneously — a design choice that requires careful prompt engineering but enables more contextually-aware completions
vs alternatives: Supports true bidirectional infill unlike some code models that only generate left-to-right, but requires manual prompt formatting and lacks IDE integration abstractions that Copilot provides natively
Builds on Llama 2's general-purpose Transformer architecture and applies code-specific pretraining to specialize the model for code understanding and generation. The exact composition of code-specific training data is undocumented, but the model learns code syntax, semantics, and common patterns from large-scale code repositories. The code-specialized weights are then fine-tuned into separate variants (base, instruct, python) for different use cases.
Unique: Applies code-specific pretraining on top of Llama 2's general-purpose foundation, creating a specialized model without architectural modifications — leverages Llama 2's proven Transformer design while adding code domain knowledge
vs alternatives: Code-specialized weights provide better code understanding than base Llama 2, but without published benchmarks, actual improvement vs general-purpose models is unknown; less specialized than models trained from scratch on code-only data
Provides a specialized `-instruct` variant fine-tuned on instruction-following data to enable natural language discussion about code, answering programming questions, and explaining code behavior. This variant is optimized for chat-style interactions rather than raw code generation, using instruction-tuning techniques to align model outputs with helpful, safe responses. Accessed via the `/api/chat` endpoint with multi-turn conversation support.
Unique: Separate `-instruct` variant explicitly fine-tuned for instruction-following and safe responses, rather than using a single base model with prompt engineering — allows specialized optimization for dialogue vs code generation tasks
vs alternatives: Dedicated instruction-tuned variant provides better conversation quality than applying generic prompts to base CodeLlama, but lacks the safety training and RLHF refinement of Claude or GPT-4
Provides a `codellama:python` variant fine-tuned on 100 billion tokens of Python-specific code, enabling superior Python code generation compared to the base model. This domain-adapted variant uses continued pretraining on Python code repositories to specialize the model's weights for Python syntax, idioms, and common patterns. The specialization improves both code quality and inference efficiency for Python-only use cases.
Unique: Implements domain-specific adaptation through continued pretraining on 100B tokens of Python code rather than generic instruction-tuning, creating a specialized variant optimized for Python syntax and idioms while maintaining the base model's architecture
vs alternatives: Python-specific fine-tuning provides better Python code quality than base CodeLlama, but lacks the multi-language flexibility of GPT-4 or the extensive Python-specific training of GitHub Copilot
Executes CodeLlama models entirely on user hardware via Ollama's quantized GGUF format, eliminating cloud API calls and enabling offline code generation. The Ollama runtime handles model loading, quantization (format unspecified but typically 4-bit or 8-bit), memory management, and inference optimization. Models are downloaded once and cached locally, with inference latency determined by local hardware rather than network round-trips or cloud queue times.
Unique: Distributes models in Ollama's quantized GGUF format enabling local execution without cloud dependency, with Ollama runtime handling memory-efficient inference and model caching — a design choice prioritizing privacy and cost over cloud-optimized latency
vs alternatives: Complete data privacy and offline capability vs cloud models (Copilot, GPT-4), but with unpredictable latency and no performance guarantees compared to cloud services with dedicated GPU infrastructure
Exposes CodeLlama inference through standardized REST API endpoints (`/api/generate` for text generation, `/api/chat` for conversation) and official SDKs (Python `ollama` library, JavaScript/TypeScript `ollama` library) with streaming token support. The API abstracts away model loading and quantization details, allowing developers to integrate code generation without understanding Ollama internals. Streaming responses enable real-time token-by-token output for UI responsiveness.
Unique: Provides both low-level REST API and high-level SDKs (Python, JavaScript) with streaming support, allowing developers to choose between direct HTTP control and language-specific abstractions — Ollama abstracts model loading/quantization complexity while maintaining API simplicity
vs alternatives: Simpler REST API than OpenAI's (no authentication, no rate limits) and local-first by default, but lacks the production-grade features of cloud APIs (monitoring, logging, SLA guarantees, automatic scaling)
Generates code across multiple programming languages (Python, C++, Java, PHP, TypeScript/JavaScript, C#, Bash, and others) using a single unified Transformer model trained on polyglot code data. The model learns language-agnostic code patterns and syntax rules during pretraining, enabling it to switch between languages based on prompt context without separate language-specific models (except the Python variant). Language selection is implicit in the prompt — developers specify the target language in natural language instructions.
Unique: Single unified Transformer model trained on polyglot code data enables language switching via prompt context rather than requiring separate language-specific models — trades language-specific optimization for architectural simplicity and unified inference
vs alternatives: Supports multiple languages in one model unlike language-specific models (Codex for Python), but with potentially lower per-language quality than specialized models; more flexible than single-language models but less optimized than GPT-4's multi-language approach
+3 more capabilities
Provides a curated collection of 164 Python programming problems designed to test code generation capabilities, each with a unique task ID, natural language prompt, function signature, canonical reference implementation, and comprehensive test cases. Problems are stored in JSONL.gz format and loaded via the read_problems() function in data.py, enabling reproducible evaluation across different code generation models.
Unique: Hand-crafted by OpenAI with deliberate problem diversity covering algorithms, data structures, and edge cases; each problem includes a canonical solution and comprehensive test suite designed to catch subtle correctness issues rather than surface-level syntax errors
vs alternatives: More rigorous and widely-adopted than crowdsourced alternatives because problems were vetted by domain experts and test cases are designed to catch functional bugs, not just runtime errors
Executes untrusted Python code in an isolated environment via the unsafe_execute() function in execution.py, with built-in protections including configurable timeout (default 10 seconds), memory limits, and exception handling. The execution engine runs generated code against problem test cases and captures pass/fail results without exposing the host system to malicious or runaway code.
Unique: Uses signal-based timeout mechanism (SIGALRM on Unix) combined with exception wrapping to safely execute untrusted code without requiring containerization, making it lightweight for research workflows while still preventing infinite loops and resource exhaustion
vs alternatives: Simpler and faster than container-based approaches (Docker) for research benchmarking because it avoids container startup overhead, while still providing adequate isolation for non-adversarial code generation evaluation
HumanEval scores higher at 63/100 vs CodeLlama (7B, 13B, 34B, 70B) at 23/100. CodeLlama (7B, 13B, 34B, 70B) leads on ecosystem, while HumanEval is stronger on adoption and quality.
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Tests generated code against problem-specific test cases via the check_correctness() function in execution.py, which executes both the canonical solution and generated code against identical test suites to verify functional equivalence. Test cases are embedded in each problem definition and executed in the sandboxed environment, with detailed failure reporting including assertion errors and exception traces.
Unique: Executes test cases in the same sandboxed environment as generated code, ensuring identical execution context and preventing false positives from environment-dependent behavior; test cases are embedded in problem definitions rather than stored separately, ensuring tight coupling between problems and their validation logic
vs alternatives: More reliable than static analysis or type checking because it actually executes code and validates outputs, while being simpler than property-based testing frameworks because test cases are hand-written and problem-specific
Calculates the pass@k metric via estimate_pass_at_k() in evaluation.py, which estimates the probability that at least one of k code samples passes all test cases for a given problem. Uses an unbiased estimator that accounts for sampling without replacement, enabling fair comparison of code generation models that produce different numbers of samples per problem.
Unique: Implements unbiased pass@k estimator that corrects for sampling without replacement, preventing overestimation of model performance when fewer than k samples are available; formula accounts for the hypergeometric distribution rather than assuming independence
vs alternatives: More statistically rigorous than naive pass@k calculation (which assumes independence) because it uses the unbiased estimator formula, enabling fair comparison of models with different sample budgets
Provides stream_jsonl() and write_jsonl() functions in data.py for reading code completions from JSONL files and writing evaluation results back to JSONL format. Each completion record contains task_id, completion string, and optional metadata; results include pass/fail status, detailed error messages, and execution metrics. This format enables efficient processing of large batches of completions without loading entire datasets into memory.
Unique: Uses streaming JSONL parsing to avoid loading entire completion datasets into memory, enabling evaluation of millions of samples on resource-constrained systems; results are written incrementally as evaluations complete rather than buffered
vs alternatives: More memory-efficient than CSV or JSON alternatives because streaming parser processes one record at a time, while still maintaining structured format compatibility with standard data tools
Provides a CLI tool (evaluate_functional_correctness) that orchestrates the entire evaluation pipeline: reads completions from JSONL, executes code in sandbox, runs test cases, calculates pass@k metrics, and writes results to output file. Supports configurable k values via --k parameter and parallelizes evaluation across multiple problems using Python's multiprocessing module.
Unique: Single-command evaluation pipeline that chains data loading, code execution, testing, and metric calculation without requiring intermediate file handling; uses Python multiprocessing to parallelize problem evaluation across CPU cores automatically
vs alternatives: Simpler than writing custom evaluation scripts because it handles all pipeline stages in one command, while being more flexible than web-based benchmarking platforms because it runs locally without network dependencies
Executes test cases in isolated Python scopes via check_correctness() function, which creates a fresh namespace for each code sample and test execution to prevent state leakage between problems. Test code is executed after the generated function is defined, with explicit assertion statements that raise exceptions on failure, enabling precise error reporting without requiring external test frameworks.
Unique: Uses Python's exec() with isolated namespace dictionaries to ensure each problem's test execution does not affect others, combined with exception wrapping to capture and report assertion failures with full stack traces
vs alternatives: More reliable than pytest or unittest frameworks for this use case because it avoids framework overhead and provides direct control over execution context, while still capturing detailed failure information
Supports evaluating multiple code samples per problem via the evaluate_functional_correctness() function, which processes JSONL files containing multiple completions per task_id and aggregates results to calculate per-problem pass@k statistics. Handles variable numbers of samples per problem and produces both per-sample and aggregated metrics in output JSONL.
Unique: Processes variable-length sample lists per problem and calculates pass@k for each k value in a single pass, using the unbiased estimator to handle problems with fewer samples than k
vs alternatives: More efficient than running separate evaluations for each k value because it calculates all k values from a single set of pass/fail results, while supporting arbitrary numbers of samples per problem