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| Pricing | Paid | Paid |
| Starting Price | $1.00e-5 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Processes multi-turn conversations with improved instruction adherence through transformer-based attention mechanisms trained on instruction-tuning datasets. Supports up to 128K tokens of context (approximately 96K input + 32K output), enabling analysis of entire documents, codebases, or conversation histories in a single request without context truncation or sliding-window approximations.
Unique: 128K context window with improved instruction-following through reinforcement learning from human feedback (RLHF) training, enabling coherent reasoning across entire documents without context loss — achieved through sparse attention patterns and hierarchical token processing rather than full quadratic attention
vs alternatives: Larger context window than GPT-3.5 Turbo (4K) and comparable to Claude 2 (100K), but with faster inference latency and lower per-token cost for instruction-following tasks
Constrains model output to valid JSON format through post-processing validation and beam search constraints during token generation. When enabled, the model generates only syntactically valid JSON that matches a provided schema, eliminating the need for regex parsing or output repair logic in downstream applications.
Unique: Implements constraint-based token generation that prunes invalid JSON tokens during beam search, ensuring 100% valid JSON output without post-processing — uses a finite-state automaton to track valid JSON syntax states and only allows tokens that maintain validity
vs alternatives: More reliable than prompt-based JSON requests (which fail 5-15% of the time) and faster than Claude's native JSON mode because it uses tighter constraint checking during decoding rather than post-hoc validation
Enables the model to invoke multiple functions simultaneously in a single response through a structured function-calling protocol. The model generates a list of function calls with arguments, which are executed in parallel by the client, and results are fed back to the model for synthesis — supporting complex workflows that require coordinating multiple APIs or tools.
Unique: Supports parallel function invocation in a single turn through a structured function-call list format, allowing clients to execute multiple tools concurrently and aggregate results — uses a token-efficient schema representation that minimizes context overhead compared to sequential function calling
vs alternatives: Faster than sequential function calling (which requires multiple round-trips) and more flexible than hardcoded tool chains because the model dynamically decides which tools to invoke based on the prompt
Provides deterministic model outputs through a seed parameter that controls the random number generator used during token sampling. When the same seed is provided with identical inputs, the model generates identical outputs, enabling reproducible results for testing, debugging, and consistent behavior in production systems.
Unique: Implements seed-based determinism by controlling the random number generator state during sampling, ensuring byte-for-byte identical outputs for identical inputs — uses a fixed random seed to initialize the softmax temperature sampling and top-k/top-p filtering
vs alternatives: More reliable than temperature=0 for reproducibility because it guarantees identical token selection across runs, whereas temperature=0 may still produce different outputs due to floating-point rounding in different environments
Processes images alongside text prompts to answer questions about visual content, perform OCR, analyze diagrams, and describe scenes. The model encodes images into visual tokens using a vision transformer backbone, then fuses them with text embeddings in the transformer for joint reasoning about image and text content.
Unique: Integrates a vision transformer encoder that converts images to visual tokens, which are then processed alongside text tokens in the same transformer architecture — enables joint reasoning about image and text without separate modality-specific branches
vs alternatives: More capable than GPT-4V for complex visual reasoning tasks and faster than Claude 3 Vision for OCR due to optimized image tokenization, but less accurate than specialized OCR tools like Tesseract for document extraction
Generates syntactically correct code in 40+ programming languages based on natural language descriptions, code comments, or partial code. Uses transformer-based code understanding trained on public repositories to predict the next tokens in a code sequence, supporting both completion (filling in missing code) and generation (writing code from scratch).
Unique: Trained on diverse public code repositories with instruction-tuning for code generation tasks, enabling context-aware completion that understands programming patterns and idioms — uses byte-pair encoding (BPE) tokenization optimized for code syntax
vs alternatives: More capable than GitHub Copilot for generating code from natural language descriptions and faster than Claude for multi-file refactoring due to optimized code tokenization, but less specialized than Codex for domain-specific code generation
Decomposes complex problems into step-by-step reasoning chains through prompting techniques that encourage the model to 'think aloud' before providing answers. The model generates intermediate reasoning steps, which improve accuracy on multi-step problems by allowing the transformer to allocate more computation to reasoning rather than direct answer prediction.
Unique: Implements chain-of-thought through prompting that encourages intermediate reasoning generation, leveraging the transformer's ability to allocate computation across tokens — the model learns to generate reasoning tokens that improve downstream answer accuracy through RLHF training on reasoning-heavy tasks
vs alternatives: More reliable than direct answer generation for complex problems (10-30% accuracy improvement on math and logic tasks) and more transparent than black-box reasoning, but slower and more expensive than single-step inference
The model has training data only up to December 2023, meaning it lacks knowledge of events, product releases, API changes, and research published after that date. Requests about current events or recent developments will produce outdated or hallucinated information, as the model cannot distinguish between pre-cutoff knowledge and post-cutoff speculation.
Unique: Training data cutoff at December 2023 creates a hard boundary in the model's knowledge — the model cannot distinguish between pre-cutoff facts and post-cutoff speculation, leading to confident hallucinations about recent events
vs alternatives: Similar knowledge cutoff to GPT-4 (April 2023 for base model) but more recent than earlier GPT-3.5 versions; requires RAG augmentation for current information, unlike search-augmented models like Perplexity or Bing Chat
+1 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 5 scores higher at 24/100 vs OpenAI: GPT-4 Turbo Preview 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