Z.ai: GLM 5.1 ranks higher at 24/100 vs OpenAI: gpt-oss-safeguard-20b at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | OpenAI: gpt-oss-safeguard-20b | Z.ai: GLM 5.1 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 22/100 | 24/100 |
| Adoption |
| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $7.50e-8 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Classifies text content across multiple safety dimensions (toxicity, hate speech, sexual content, violence, etc.) using a 21B-parameter MoE architecture trained specifically for safety reasoning. The model performs multi-label classification with confidence scores, enabling downstream filtering decisions. Unlike generic classifiers, it reasons about context and intent rather than pattern-matching keywords, reducing false positives on sarcasm, reclaimed language, and domain-specific terminology.
Unique: Uses a specialized 21B MoE architecture trained exclusively for safety reasoning rather than general-purpose language understanding, with sparse activation patterns that route safety-critical tokens through expert subnetworks optimized for adversarial detection and context-aware classification
vs alternatives: Faster and more context-aware than generic LLM-based classifiers (Claude, GPT-4) because it's purpose-built for safety with MoE sparsity, while more accurate than rule-based or shallow ML classifiers because it performs semantic reasoning about intent and context
Detects and flags adversarial prompts, jailbreak attempts, and prompt injection attacks by analyzing linguistic patterns, instruction-following cues, and known attack vectors. The model identifies attempts to override system instructions, bypass safety guidelines, or manipulate the LLM into unsafe behavior. It operates as a gating layer that can reject or flag suspicious inputs before they reach downstream LLMs, reducing attack surface.
Unique: Trained on a curated dataset of real-world jailbreak attempts and adversarial prompts collected from production LLM systems, enabling detection of attack patterns that generic safety models miss. MoE routing directs suspicious tokens to adversarial-detection experts rather than general classifiers.
vs alternatives: More effective than regex-based or rule-based jailbreak filters because it understands semantic intent and paraphrasing, and faster than running full LLM reasoning (GPT-4 as a judge) because it uses sparse MoE activation to focus compute on suspicious patterns
Validates and filters text generated by downstream LLMs before it reaches users, detecting unsafe, harmful, or policy-violating outputs. The model analyzes generated text for toxicity, misinformation, privacy violations, and other safety concerns, enabling post-hoc filtering of LLM outputs. It can be integrated as a guardrail layer in inference pipelines to prevent unsafe content from being served.
Unique: Specialized for evaluating LLM-generated text rather than user input, with training data that includes common failure modes of large language models (hallucinations, unsafe reasoning chains, policy violations). MoE experts are tuned for detecting subtle safety issues in fluent, coherent text.
vs alternatives: More efficient than running a second LLM as a judge (e.g., GPT-4 safety evaluation) because it uses sparse MoE activation, and more accurate than simple keyword/regex filtering because it understands semantic meaning and context in generated text
Performs simultaneous classification across multiple safety dimensions (toxicity, hate speech, sexual content, violence, illegal activity, misinformation, privacy violations, etc.) with independent confidence scores for each label. The model outputs a structured safety profile rather than a single binary decision, enabling fine-grained policy enforcement. Each label is scored independently, allowing downstream systems to apply different thresholds per category.
Unique: Trained with multi-task learning across safety dimensions, with MoE experts specialized for different harm categories (toxicity experts, hate speech experts, misinformation experts, etc.). Each expert produces independent confidence scores rather than a single aggregated decision.
vs alternatives: More flexible than binary safe/unsafe classifiers because it provides per-category scores, enabling policy-specific thresholds. More interpretable than black-box LLM judges because each label has explicit confidence, supporting audit and appeals workflows
Achieves sub-200ms latency for safety classification by using Mixture-of-Experts (MoE) architecture with sparse activation. Rather than running all 21B parameters, the model routes each input through a gating network that selects only the relevant expert subnetworks (typically 2-4 experts out of many), reducing compute by 80-90%. This enables real-time safety filtering in high-throughput systems without dedicated GPU infrastructure.
Unique: Uses learned gating networks to route inputs to specialized safety experts, with dynamic sparsity that adapts per-input. Unlike dense models that run all parameters, MoE activation is conditional — suspicious inputs trigger more experts, while benign inputs use fewer. This is fundamentally different from pruning or quantization approaches.
vs alternatives: 10-20x faster than running GPT-4 as a safety judge, and 2-3x faster than dense 20B models because sparse activation reduces compute. Maintains better accuracy than lightweight classifiers (BERT-based) because it has access to 21B parameters when needed, but only activates them selectively
Evaluates safety by understanding semantic context, intent, and nuance rather than pattern-matching keywords. The model reasons about whether content is harmful in context (e.g., distinguishing between reclaimed language, educational discussion of harmful topics, and actual harm). It uses transformer-based attention mechanisms to weigh different parts of the input, understanding that the same phrase can be safe or unsafe depending on context.
Unique: Trained on safety examples with rich contextual annotations, enabling the model to learn that identical phrases have different safety implications depending on context. Uses attention mechanisms to identify which parts of the input are most relevant to safety decisions, rather than treating all tokens equally.
vs alternatives: More accurate than keyword-based systems on edge cases (satire, reclaimed language, educational content), and more interpretable than black-box neural classifiers because attention patterns can be visualized to show which context influenced the decision
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 OpenAI: gpt-oss-safeguard-20b at 22/100.
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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
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