Z.ai: GLM 5.1 ranks higher at 24/100 vs AlfredPros: CodeLLaMa 7B Instruct Solidity at 21/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | AlfredPros: CodeLLaMa 7B Instruct Solidity | Z.ai: GLM 5.1 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 21/100 | 24/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $8.00e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates Solidity smart contract code from natural language descriptions and prompts using a 7B parameter Code LLaMA model fine-tuned specifically for Solidity syntax and patterns. The model was trained via 4-bit QLoRA (Quantized Low-Rank Adaptation) using the PEFT library, enabling efficient parameter updates on a subset of weights while maintaining full model capability. This approach reduces memory footprint during inference while preserving the model's ability to understand Solidity-specific idioms, security patterns, and contract structures learned during fine-tuning.
Unique: Fine-tuned specifically on Solidity code using 4-bit QLoRA via PEFT library, enabling a lightweight 7B model to generate Solidity-idiomatic code with domain-specific pattern recognition that general-purpose Code LLaMA lacks. The quantization approach reduces inference latency and memory requirements compared to full-precision models while maintaining Solidity-specific knowledge.
vs alternatives: Smaller and faster than GPT-4 or Claude for Solidity generation while maintaining Solidity-specific accuracy; more specialized than general Code LLaMA but more cost-effective and privacy-preserving than cloud-based alternatives for teams with on-premise or edge deployment needs.
Completes partial Solidity code snippets by predicting the next tokens based on context, leveraging the instruction-tuned variant of Code LLaMA to understand Solidity syntax, function signatures, and common contract patterns. The model uses causal language modeling (next-token prediction) with attention mechanisms trained on Solidity code to generate contextually appropriate continuations, including function bodies, state variable declarations, and contract logic.
Unique: Instruction-tuned variant of Code LLaMA specifically adapted for Solidity, enabling it to understand and complete Solidity-specific patterns (modifiers, events, storage layouts) that general code completion models treat as generic syntax.
vs alternatives: More Solidity-aware than generic Code LLaMA completion; lighter-weight and faster than GPT-4 Turbo for real-time IDE integration while maintaining domain-specific accuracy.
Analyzes existing Solidity code and generates natural language explanations, documentation, and inline comments. The instruction-tuned model reads Solidity code as input and produces human-readable descriptions of contract logic, function behavior, state transitions, and security considerations. This leverages the model's training on code-to-text pairs and instruction-following capability to produce contextually appropriate explanations at multiple levels of detail.
Unique: Instruction-tuned specifically on Solidity code-documentation pairs, enabling it to generate Solidity-idiomatic explanations that reference contract-specific concepts (state variables, modifiers, events) rather than generic programming constructs.
vs alternatives: More Solidity-aware than general-purpose documentation generators; faster and more cost-effective than hiring human auditors for initial documentation, though not a replacement for security review.
Analyzes Solidity code and suggests refactoring improvements, gas optimizations, and code quality enhancements. The model uses its training on Solidity patterns and best practices to identify opportunities for simplification, gas reduction, and adherence to Solidity conventions. This is implemented via prompt-based instruction following, where the model receives code and a refactoring directive and generates improved versions with explanations of changes.
Unique: Fine-tuned on Solidity-specific optimization patterns including gas-efficient storage layouts, function selector optimization, and EVM-aware code patterns that general refactoring models do not understand.
vs alternatives: More Solidity-specific than generic code refactoring tools; faster and cheaper than manual auditor review while providing immediate suggestions, though requires validation against actual gas benchmarks.
Identifies potential security issues and suggests secure coding patterns in Solidity code by analyzing contract logic against known vulnerability patterns and best practices. The model uses its training on secure Solidity patterns to flag common issues like reentrancy risks, unchecked external calls, and improper access control, then suggests remediation patterns. This is implemented via instruction-following prompts that ask the model to analyze code for security concerns.
Unique: Trained on Solidity-specific security patterns and known vulnerabilities (reentrancy, overflow, access control), enabling it to recognize EVM-specific attack vectors that general security analysis tools miss.
vs alternatives: More Solidity-aware than generic static analysis tools; faster and cheaper than manual security review but not a replacement for professional audits; complements automated tools like Slither by providing pattern-based reasoning.
GLM-5.1 executes multi-step coding tasks over extended timeframes without requiring human intervention between steps, using an internal planning mechanism that decomposes complex objectives into sub-tasks and maintains execution state across sequential operations. Unlike minute-level interaction models that require prompting after each step, this capability enables the model to autonomously navigate decision trees, handle errors, and adapt strategy based on intermediate results without context resets.
Unique: Designed specifically for minute+ autonomous execution windows rather than single-turn interactions; maintains internal execution state and decision-making across extended task horizons without requiring external orchestration or re-prompting between steps
vs alternatives: Outperforms GPT-4 and Claude for long-horizon coding tasks because it's architected for continuous autonomous operation rather than stateless request-response cycles
GLM-5.1 generates and refactors code with awareness of the full codebase structure, dependencies, and patterns, using semantic understanding of how changes in one file propagate to others. The model analyzes import graphs, function signatures, and usage patterns across files to ensure generated code maintains consistency and doesn't introduce breaking changes, rather than treating each file in isolation.
Unique: Maintains semantic awareness of codebase structure and cross-file dependencies during generation, enabling it to make coordinated changes across multiple files rather than treating each file independently
vs alternatives: Produces more consistent multi-file refactorings than Copilot or Claude because it reasons about the entire codebase context simultaneously rather than file-by-file
Z.ai: GLM 5.1 scores higher at 24/100 vs AlfredPros: CodeLLaMa 7B Instruct Solidity at 21/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
GLM-5.1 diagnoses errors and bugs by analyzing error messages, stack traces, and code context to identify root causes and suggest fixes. The model correlates error symptoms with likely causes, explains why errors occur, and provides specific debugging steps or code fixes.
Unique: Diagnoses errors by correlating symptoms with root causes using semantic understanding of code and error patterns, providing explanations and fixes rather than just pattern matching
vs alternatives: More effective at diagnosing subtle bugs than search-based solutions because it reasons about code semantics and error causality
GLM-5.1 identifies performance bottlenecks in code and suggests optimizations with specific implementation guidance, analyzing algorithms, data structures, and resource usage to recommend improvements. The model understands performance implications of different approaches and can suggest algorithmic or architectural changes to improve efficiency.
Unique: Suggests optimizations based on algorithmic and architectural analysis rather than just code-level tweaks, understanding performance implications of different approaches
vs alternatives: Provides more meaningful performance guidance than generic LLMs because it understands algorithm complexity and can suggest structural improvements
GLM-5.1 analyzes code for security vulnerabilities including injection attacks, authentication/authorization issues, cryptographic weaknesses, and data exposure risks, providing specific remediation guidance. The model understands common vulnerability patterns and security best practices to identify risks and suggest secure implementations.
Unique: Identifies security vulnerabilities through semantic analysis of code patterns and provides remediation guidance based on security best practices, not just pattern matching against known CVEs
vs alternatives: More effective at finding context-specific security issues than SAST tools because it understands code intent and can suggest secure implementations
GLM-5.1 performs step-by-step reasoning about code behavior by internally simulating or tracing execution paths, allowing it to predict runtime behavior, identify bugs, and explain code logic without requiring actual execution. This capability uses chain-of-thought-like reasoning applied specifically to code semantics, tracking variable state, control flow, and function call sequences to reason about correctness.
Unique: Applies extended reasoning specifically to code semantics and execution paths, enabling it to predict runtime behavior and identify subtle bugs through symbolic execution simulation rather than pattern matching
vs alternatives: More effective at finding subtle logic bugs than GPT-4 because it explicitly traces execution state rather than relying on pattern recognition
GLM-5.1 maintains rich context across multiple conversation turns when working on code, remembering previous edits, design decisions, and constraints without requiring users to re-specify them. The model builds an internal model of the codebase state and user intent that persists across turns, enabling iterative refinement where each turn builds on previous work rather than starting fresh.
Unique: Maintains stateful context across turns specifically optimized for code collaboration, remembering design decisions and codebase state without explicit memory structures
vs alternatives: Provides better iterative code refinement than stateless models because it retains context about previous edits and design intent across turns
GLM-5.1 translates natural language specifications into code that preserves semantic intent, handling ambiguous or underspecified requirements by inferring reasonable implementations based on context and common patterns. The model uses semantic understanding of both natural language and code to bridge the gap between high-level intent and low-level implementation details.
Unique: Translates natural language to code with explicit semantic fidelity checking, inferring reasonable implementations for underspecified requirements rather than producing literal or incomplete code
vs alternatives: Handles ambiguous requirements better than Copilot because it uses semantic reasoning to infer intent rather than pattern matching against training data
+5 more capabilities