| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.00e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Maintains conversation history across multiple exchanges, building context through a sliding window of prior messages. The model processes the full conversation thread to generate contextually-aware responses, enabling coherent multi-step dialogues without explicit state management. This is implemented via transformer attention mechanisms that weight recent and relevant prior turns more heavily than distant ones.
Unique: GLM 4 32B uses a hybrid attention mechanism optimized for cost-efficiency at 32B parameters, balancing context retention with inference speed — smaller than 70B models but with enhanced tool-use awareness built into the base architecture
vs alternatives: More cost-effective than GPT-4 or Claude 3 Opus for conversational tasks while maintaining competitive reasoning quality through specialized training on tool-use and code tasks
Generates syntactically correct code across 40+ programming languages by learning language-specific idioms, libraries, and patterns from training data. The model understands context from partial code, docstrings, and type hints to predict the most likely next tokens, supporting both completion-in-place and full-function generation. Implementation leverages transformer architecture with language-aware tokenization and embedding spaces.
Unique: GLM 4 32B includes specialized training on code-related tasks with enhanced support for tool-use patterns, making it particularly effective at generating code that calls APIs or external functions — not just standalone code
vs alternatives: More cost-effective than Copilot Pro or Claude for code generation while maintaining competitive accuracy on tool-use and API integration patterns due to specialized training
Understands complex, multi-step instructions and breaks them into executable subtasks, maintaining state across steps. The model learns to follow detailed specifications, handle edge cases, and adapt to variations in input. Implementation uses instruction-tuning on task datasets with explicit step-by-step reasoning, enabling the model to plan, execute, and verify each step of a workflow.
Unique: GLM 4 32B is trained on instruction-following datasets with explicit reasoning traces, enabling it to show its planning process and decompose tasks transparently — this makes it easier to debug and verify complex workflows
vs alternatives: More reliable at instruction-following than smaller models while being more cost-effective than GPT-4, with better transparency about reasoning process than black-box systems
Accepts structured tool definitions (function signatures, parameter schemas, descriptions) and generates function calls with correctly-typed arguments when the model determines a tool is needed. The model learns to route requests to appropriate tools by matching user intent against tool descriptions, then formats output as structured JSON or code that can be directly executed. This is implemented via instruction-tuning on tool-use datasets and constrained decoding to ensure valid schema compliance.
Unique: GLM 4 32B has significantly enhanced tool-use capabilities built into the base model (not via fine-tuning), enabling reliable function calling without additional instruction-tuning — this is a core architectural feature rather than a bolt-on capability
vs alternatives: More reliable tool-use than smaller open models while being more cost-effective than GPT-4 Turbo, with native support for complex multi-step tool chains
Can query the internet to retrieve current information when the model determines that real-time data is needed to answer a user query. The model learns to recognize when its training data is insufficient (e.g., current events, recent product releases, live prices) and generates search queries, then synthesizes results into coherent answers. Implementation involves decision logic to determine search necessity, query generation, and result ranking/synthesis.
Unique: GLM 4 32B integrates online search as a native capability (not via external RAG systems), with the model learning when to search and how to synthesize results — reducing the need for separate search infrastructure
vs alternatives: More integrated than Perplexity's approach (which is search-first) while being more cost-effective than GPT-4 with Bing search, with native decision logic about when search is necessary
Extracts structured information from unstructured text by mapping content to predefined schemas (JSON, tables, key-value pairs). The model understands semantic relationships and can normalize data, handle missing fields, and infer types based on context. Implementation uses instruction-tuning on extraction tasks combined with constrained decoding to ensure output conforms to specified schema, preventing hallucinated fields or type mismatches.
Unique: GLM 4 32B uses constrained decoding to guarantee schema compliance, preventing invalid JSON or missing required fields — this is more reliable than post-hoc validation of unconstrained generation
vs alternatives: More cost-effective than GPT-4 for extraction tasks while maintaining competitive accuracy through specialized training, with guaranteed schema compliance reducing post-processing overhead
Analyzes code snippets or error messages to identify bugs, suggest fixes, and explain root causes. The model understands common error patterns, language-specific pitfalls, and debugging strategies. It generates corrected code, explains why the error occurred, and suggests preventive measures. Implementation leverages training on code repositories with bug fixes and error logs, enabling pattern recognition across languages and frameworks.
Unique: GLM 4 32B combines code understanding with reasoning about error patterns, enabling it to suggest not just fixes but explanations of why errors occur — this requires both language modeling and logical reasoning
vs alternatives: More cost-effective than GitHub Copilot for debugging while providing better explanations than simple error-matching tools, with reasoning about root causes rather than just pattern matching
Translates text between 50+ language pairs while preserving semantic meaning, tone, and context. The model understands idioms, cultural references, and technical terminology, adapting translations to target audience and domain. Implementation uses multilingual transformer embeddings trained on parallel corpora, with special handling for code, proper nouns, and domain-specific terms to maintain accuracy across languages.
Unique: GLM 4 32B uses multilingual embeddings trained on diverse parallel corpora, enabling it to handle low-resource language pairs better than models trained primarily on English — this is a training data advantage rather than architectural
vs alternatives: More cost-effective than specialized translation APIs while maintaining competitive quality through multilingual training, with better handling of technical and code-related content than generic translation services
+3 more capabilities
GLM-5 processes extended code contexts (supporting multi-file projects and large codebases) while maintaining semantic understanding of system architecture through attention mechanisms optimized for code structure. The model uses specialized tokenization for programming languages and maintains coherence across thousands of tokens of code context, enabling generation of complex features that respect existing patterns and dependencies.
Unique: Engineered specifically for complex systems design with attention mechanisms tuned for code structure and architectural patterns, rather than generic language modeling — enables understanding of system-wide dependencies and design constraints across extended contexts
vs alternatives: Outperforms general-purpose models on large-scale programming tasks because it's optimized for architectural coherence and long-horizon code generation rather than treating code as generic text
GLM-5 supports extended reasoning chains for agentic workflows through structured prompt patterns that enable step-by-step decomposition of complex tasks. The model can maintain state across multiple turns, reason about tool outputs, and make decisions about next actions — designed for long-horizon agent loops where the model must plan, execute, observe, and adapt across dozens of steps.
Unique: Explicitly engineered for long-horizon agent workflows with architectural patterns optimized for extended reasoning chains, rather than single-turn tool calling — maintains coherence and decision quality across dozens of reasoning steps
vs alternatives: Better suited for multi-step agentic tasks than general-purpose models because reasoning and tool-use patterns are baked into the training, not bolted on via prompt engineering
Z.ai: GLM 4 32B scores higher at 24/100 vs Z.ai: GLM 5 at 24/100.
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GLM-5 analyzes code for performance bottlenecks and suggests optimization strategies through understanding of algorithmic complexity, memory management, and system-level performance patterns. The model can identify inefficient algorithms, suggest data structure improvements, and recommend caching or parallelization strategies — enabling targeted performance improvements with understanding of trade-offs.
Unique: Understands algorithmic complexity and system-level performance patterns, enabling identification of fundamental bottlenecks and suggestion of targeted optimizations rather than micro-optimizations
vs alternatives: Identifies more fundamental performance issues than profiling tools because it understands algorithmic complexity and can suggest architectural improvements, not just code-level optimizations
GLM-5 generates comprehensive API specifications, including endpoint definitions, request/response schemas, error handling, and usage examples through understanding of API design best practices and REST/GraphQL patterns. The model can produce OpenAPI/Swagger specifications, generate API documentation, and suggest design improvements — enabling rapid API specification and documentation.
Unique: Generates comprehensive API specifications that follow REST/GraphQL best practices and include error handling, authentication, and usage examples — not just endpoint definitions
vs alternatives: Produces more complete and best-practice-aligned API specifications than simple code-to-spec tools because it understands API design patterns and includes comprehensive documentation
GLM-5 generates high-quality technical documentation, design documents, and architectural specifications through training on expert-level technical writing patterns. The model understands domain-specific terminology, maintains consistency across long documents, and can generate structured documentation (API specs, RFC-style documents, architecture decision records) with appropriate technical depth and precision.
Unique: Trained on expert-level technical documentation patterns and domain-specific terminology, enabling generation of publication-ready documentation with appropriate technical depth rather than generic summaries
vs alternatives: Produces more technically precise and domain-aware documentation than general-purpose models because it understands architectural patterns, trade-offs, and expert writing conventions specific to software engineering
GLM-5 breaks down complex, ambiguous problems into structured task hierarchies and implementation plans through chain-of-thought reasoning patterns. The model can identify dependencies, suggest phased approaches, and generate detailed step-by-step plans for tackling large engineering challenges — useful for translating high-level requirements into actionable development roadmaps.
Unique: Optimized for expert-level problem decomposition through training on complex system design patterns and architectural reasoning, enabling generation of sophisticated multi-phase plans rather than simple task lists
vs alternatives: Produces more sophisticated and architecturally-aware plans than general-purpose models because it understands system design patterns, dependency relationships, and phased implementation strategies
GLM-5 analyzes code for quality issues, architectural violations, and design improvements through patterns learned from expert code review practices. The model can identify performance bottlenecks, suggest refactoring opportunities, flag architectural inconsistencies, and provide detailed feedback on code quality — going beyond simple linting to understand design intent and system-wide implications.
Unique: Trained on expert code review patterns and architectural reasoning, enabling detection of design issues and architectural violations rather than just syntax and style problems
vs alternatives: Provides more sophisticated architectural and design feedback than linting tools because it understands system-wide implications and expert design patterns, not just local code quality
GLM-5 translates code between programming languages while preserving semantic meaning and adapting to language-specific idioms. The model understands language-specific patterns, libraries, and best practices, enabling translation that produces idiomatic code rather than mechanical line-by-line conversions — useful for migrating systems across language ecosystems or supporting polyglot architectures.
Unique: Produces idiomatic, language-specific code rather than mechanical translations because it understands language-specific patterns, libraries, and best practices learned from diverse codebases
vs alternatives: Generates more idiomatic and maintainable translations than simple pattern-matching tools because it understands semantic equivalence and language-specific idioms
+4 more capabilities