| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $3.00e-8 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 10 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes forward passes using a Mixture-of-Experts (MoE) architecture where only 3.6B of 21B parameters are active per token, routing each token to specialized expert sub-networks via learned gating functions. This sparse activation pattern reduces computational cost and memory bandwidth compared to dense models while maintaining parameter capacity for diverse reasoning tasks.
Unique: Uses a 21B parameter MoE architecture with only 3.6B active parameters per forward pass, achieving dense-model capability with sparse-model efficiency through learned expert routing — distinct from dense models like Llama 2 70B and from other MoE implementations like Mixtral that use different expert counts and gating strategies
vs alternatives: Offers better inference efficiency than dense 20B models (lower latency, memory) while maintaining OpenAI training quality, and provides open-weight licensing (Apache 2.0) unlike proprietary GPT-4 variants
Maintains coherent multi-turn dialogue by processing conversation history within a fixed context window, using attention mechanisms to weight recent and relevant prior messages while discarding or summarizing older context when token limits are approached. The model learns to extract key information from conversation history to maintain semantic continuity across turns.
Unique: Leverages MoE architecture to maintain coherent multi-turn reasoning with selective expert activation — experts specializing in dialogue coherence and context tracking are preferentially routed for conversation continuation, versus dense models that apply uniform attention across all parameters
vs alternatives: Maintains conversation quality comparable to larger dense models while using 3.6B active parameters, reducing inference cost per turn versus GPT-3.5 or Llama 2 70B for long-running conversations
Generates syntactically valid code across multiple programming languages by learning patterns from training data that includes code repositories, technical documentation, and problem-solution pairs. The model applies language-specific reasoning to produce working implementations, debug explanations, and architectural suggestions for technical problems.
Unique: MoE routing allows specialized experts to activate for different programming languages and problem types — language-specific experts handle syntax and idioms while reasoning experts handle algorithm design, versus dense models applying uniform computation across all code domains
vs alternatives: Provides code generation capability comparable to Copilot or Claude at lower inference cost due to sparse activation, with open-weight licensing enabling local fine-tuning for domain-specific code patterns
Answers factual and conceptual questions by retrieving and synthesizing relevant knowledge from training data, applying reasoning to connect concepts across domains. The model generates coherent explanations that cite reasoning steps and provide context-appropriate detail levels based on question complexity.
Unique: MoE architecture routes different question types to specialized experts — domain-specific experts (science, history, technology) activate selectively based on question content, allowing efficient knowledge synthesis without computing all parameters for every query
vs alternatives: Achieves knowledge synthesis quality comparable to larger models while using 3.6B active parameters, reducing latency and cost versus GPT-3.5 for knowledge-heavy applications
Interprets complex, multi-step instructions and decomposes them into executable sub-tasks, then generates outputs following specified constraints (format, length, tone, structure). The model learns to parse instruction syntax, identify priorities, and handle edge cases like conflicting constraints or ambiguous requirements.
Unique: MoE routing enables instruction-parsing experts to activate first, decomposing complex requirements before routing to task-specific experts for execution — versus dense models that process instructions and execution in a single forward pass
vs alternatives: Handles multi-step instruction following with comparable quality to GPT-4 while using sparse activation, reducing per-token cost for instruction-heavy workflows
Generates original creative content (stories, poetry, marketing copy, dialogue) by learning stylistic patterns, narrative structures, and genre conventions from training data. The model applies learned constraints (rhyme schemes, character consistency, tone) to produce coherent creative outputs that match specified requirements.
Unique: MoE architecture allows style-specific experts (poetry, narrative, dialogue, marketing) to activate based on content type, enabling more consistent stylistic adherence than dense models that apply uniform parameters across all creative domains
vs alternatives: Produces creative content quality comparable to larger models while using sparse activation, reducing inference cost for high-volume content generation workflows
Condenses long-form text into concise summaries by identifying key information, removing redundancy, and preserving essential meaning. The model learns to extract structured information (entities, relationships, facts) from unstructured text and present it in specified formats (bullet points, JSON, tables).
Unique: MoE routing activates summarization experts for compression and extraction experts for structured data generation, allowing efficient handling of different extraction tasks without computing all parameters
vs alternatives: Provides summarization and extraction quality comparable to larger models while using sparse activation, reducing latency and cost for high-volume document processing
Translates text between languages and generates content in non-English languages by learning multilingual patterns from training data. The model preserves meaning, tone, and context-appropriate phrasing across language pairs, and can switch between languages within a single response.
Unique: MoE architecture includes language-specific experts for major language pairs, allowing efficient routing to appropriate experts based on source and target languages rather than computing translation parameters for all language combinations
vs alternatives: Provides translation quality comparable to specialized translation models while maintaining general-purpose reasoning capability, with sparse activation reducing per-token cost versus dense multilingual models
+2 more capabilities
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-20b at 23/100.
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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
+5 more capabilities