| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $6.00e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 8 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Generates code with multi-step task decomposition and long-horizon planning capabilities, enabling the model to break down complex coding tasks into sequential subtasks and maintain coherent context across extended reasoning chains. The 30B parameter architecture is optimized for agentic workflows where the model must plan tool use, manage state across multiple function calls, and adapt based on intermediate results.
Unique: 30B-class model specifically optimized for agentic coding workflows with explicit long-horizon task planning capabilities, rather than general-purpose code completion — uses architectural patterns tuned for maintaining coherence across extended reasoning chains in coding contexts
vs alternatives: Smaller and faster than 70B+ models while maintaining agentic planning capabilities, making it cost-effective for autonomous coding agents that don't require maximum reasoning depth
Delivers text generation via streaming API endpoints that emit tokens incrementally, enabling real-time response rendering and token-level control over generation parameters. Integrates with OpenRouter's infrastructure to provide consistent streaming behavior across multiple model providers, with support for temperature, top-p, and max-tokens constraints applied at generation time.
Unique: Exposes token-level generation control through OpenRouter's unified streaming API, allowing per-request parameter tuning without model-specific SDK integration — abstracts provider differences (OpenAI, Anthropic, etc.) behind consistent streaming interface
vs alternatives: More flexible than direct model APIs because it allows switching between providers and models without code changes, and provides unified streaming semantics across heterogeneous backends
Maintains multi-turn conversations using role-based message formatting (system, user, assistant) with full context preservation across turns. The model processes the entire conversation history to generate contextually coherent responses, with support for system prompts that define behavior and constraints. Architecture relies on stateless API calls where the client manages conversation state and sends full history with each request.
Unique: Implements stateless multi-turn conversation where the client owns conversation state, enabling flexible persistence strategies (database, file, in-memory) without model-level state management — contrasts with stateful conversation APIs that manage history server-side
vs alternatives: More flexible than stateful conversation APIs because clients can implement custom history management, pruning, or summarization strategies; however, requires more client-side complexity than fully managed conversation services
Enables the model to request execution of external functions by generating structured function calls with parameters, using JSON schema definitions to specify available tools. The model learns to invoke functions based on task requirements and can chain multiple function calls in sequence. Implementation relies on providing tool definitions in the system prompt or via dedicated function-calling parameters, with the model outputting structured JSON that clients parse and execute.
Unique: Supports function calling through OpenRouter's unified interface, allowing clients to define tools once and use them across multiple underlying models (OpenAI, Anthropic, etc.) without model-specific function-calling syntax — abstracts provider API differences
vs alternatives: More portable than direct model APIs because tool definitions are provider-agnostic; however, requires client-side function execution and result feeding, adding complexity vs. fully managed agent platforms
Analyzes code snippets and full codebases with awareness of language-specific syntax, semantics, and architectural patterns. The model can identify bugs, suggest refactorings, explain code behavior, and understand dependencies between functions and modules. Implementation leverages the 30B parameter scale and code-specific training to maintain coherence across multi-file contexts and recognize common patterns (design patterns, anti-patterns, security issues).
Unique: 30B-class model optimized for code understanding with explicit training for agentic coding tasks, providing better code analysis than smaller models while maintaining efficiency — balances depth of analysis with inference speed
vs alternatives: More efficient than 70B+ models for code analysis while maintaining quality comparable to larger models; faster than static analysis tools for semantic understanding but less precise than specialized linters for syntax-level issues
Generates step-by-step reasoning traces that decompose complex problems into intermediate reasoning steps before arriving at final answers. The model can be prompted to 'think aloud' using chain-of-thought patterns, enabling transparency into decision-making and improving accuracy on multi-step reasoning tasks. Implementation relies on prompting techniques (e.g., 'Let's think step by step') that activate the model's reasoning capabilities without requiring special model modifications.
Unique: 30B-class model with explicit optimization for long-horizon reasoning tasks, enabling effective chain-of-thought reasoning without the token overhead of much larger models — balances reasoning depth with efficiency
vs alternatives: More efficient than 70B+ models for chain-of-thought tasks while maintaining reasoning quality; more transparent than smaller models that may skip reasoning steps
Provides access to the GLM-4.7-Flash model through OpenRouter's unified API, abstracting away provider-specific implementation details and offering consistent request/response formats across multiple underlying models. Clients make HTTP requests to OpenRouter endpoints with standard JSON payloads, and OpenRouter handles routing, rate limiting, and provider-specific protocol translation. This enables easy model switching and multi-model fallback strategies without code changes.
Unique: OpenRouter's unified API abstraction layer allows GLM-4.7-Flash to be accessed alongside 100+ other models with identical request/response formats, enabling seamless model switching and multi-model fallback without SDK changes — contrasts with direct provider APIs that require model-specific code
vs alternatives: More flexible than direct provider APIs for multi-model applications; adds latency and cost overhead but eliminates vendor lock-in and simplifies model evaluation
Processes text inputs with awareness of context window constraints, maintaining coherence within the model's maximum token capacity. The model can handle inputs up to its context window limit (typically 128K tokens for GLM-4.7-Flash) and generates outputs that fit within remaining token budget. Implementation relies on client-side token counting and context management to avoid exceeding limits, with graceful degradation when inputs approach window boundaries.
Unique: 30B-class model with extended context window (likely 128K tokens) optimized for long-context tasks, enabling processing of full documents and multi-file codebases without chunking — larger window than many smaller models but smaller than 200K+ context models
vs alternatives: Larger context window than GPT-3.5 or smaller open models, enabling longer documents without chunking; smaller than Claude 200K or GPT-4 Turbo, reducing cost for shorter documents but requiring chunking for very long inputs
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 5 scores higher at 24/100 vs Z.ai: GLM 4.7 Flash at 23/100.
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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