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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $3.90e-8 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements a 117B-parameter Mixture-of-Experts architecture that activates only 5.1B parameters per forward pass, routing input tokens to specialized expert subnetworks based on learned gating functions. This sparse activation pattern reduces computational cost while maintaining model capacity for complex reasoning tasks, using a load-balancing mechanism to distribute tokens across experts and prevent collapse to a single dominant expert.
Unique: OpenAI's proprietary MoE gating and load-balancing mechanism optimized for agentic reasoning, activating 5.1B of 117B parameters per forward pass with specialized expert routing designed specifically for multi-step decision-making rather than general-purpose dense inference
vs alternatives: Achieves 4.4x parameter efficiency vs. dense 120B models (5.1B active vs. 120B) while maintaining reasoning capability superior to smaller dense models, with OpenAI's production-grade expert balancing preventing the expert collapse and load imbalance issues common in open-source MoE implementations
Supports structured reasoning chains where the model can decompose complex tasks into intermediate steps, make decisions about which tools or functions to invoke, and iteratively refine outputs based on tool results. The model is trained to generate reasoning tokens that explicitly show its decision-making process, enabling transparent multi-turn agent loops where each step's output feeds into the next step's input, with native support for function calling schemas and structured output formatting.
Unique: Trained specifically for agentic reasoning with explicit reasoning token generation and native function-calling integration, using OpenAI's proprietary training approach to balance reasoning depth with tool invocation accuracy, enabling transparent multi-step agent loops without requiring external chain-of-thought frameworks
vs alternatives: Outperforms GPT-4 on complex multi-step reasoning tasks while being 3-4x cheaper per token, with better tool-calling accuracy than open-source models due to OpenAI's supervised fine-tuning on agent trajectories
Processes up to 128,000 tokens in a single context window, enabling the model to maintain coherent understanding across entire documents, codebases, or multi-turn conversations without losing semantic relationships between distant parts of the input. Uses efficient attention mechanisms (likely sparse or linear attention variants optimized for MoE) to handle long sequences while maintaining the reasoning capability needed for complex analysis across the full context.
Unique: 128K token context window combined with MoE sparse activation allows efficient processing of long sequences without proportional latency increase, using expert routing to focus computation on relevant context regions rather than applying uniform attention across entire sequence
vs alternatives: Maintains semantic coherence across 128K tokens with lower latency than dense models using full attention, while being cheaper per token than GPT-4 Turbo's 128K context due to sparse activation reducing per-token compute cost
Generates syntactically correct and semantically sound code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.), with understanding of language-specific idioms, frameworks, and best practices. The model is trained on diverse code repositories and can generate complete functions, classes, or multi-file solutions, with support for generating code that integrates with popular libraries and frameworks. Includes capability to understand existing code context and generate compatible additions or refactorings.
Unique: Trained on diverse code repositories with understanding of language-specific idioms and framework patterns, using MoE routing to specialize different experts on different language families (e.g., one expert for dynamic languages, another for systems languages), enabling consistent code quality across 40+ languages
vs alternatives: Generates code across more languages than Copilot with better framework integration due to broader training data, while being cheaper per token than GPT-4 and faster than Claude due to sparse activation reducing per-token latency
Reliably follows complex, multi-part instructions and generates output in specified structured formats (JSON, XML, YAML, CSV, Markdown tables) with high consistency. The model is trained to parse instruction hierarchies, handle conditional logic (if-then patterns), and generate output that strictly adheres to specified schemas or templates. Supports both explicit format requests (e.g., 'output as JSON') and implicit format inference from examples provided in the prompt.
Unique: Trained with instruction-following fine-tuning that emphasizes schema adherence and format consistency, using MoE expert specialization where certain experts are optimized for structured output generation vs. free-form text, enabling reliable structured output without requiring external schema validation frameworks
vs alternatives: More reliable structured output than GPT-3.5 with lower cost than GPT-4, while being faster than Claude due to sparse activation and more consistent than open-source models due to OpenAI's supervised fine-tuning on instruction-following tasks
Provides inference through OpenAI's REST API with support for both streaming (real-time token-by-token output) and batch processing (asynchronous processing of multiple requests). Streaming mode returns tokens as they are generated, enabling real-time user feedback and progressive rendering in applications. Batch mode accepts multiple requests in a single API call, optimizing throughput for non-latency-sensitive workloads and reducing per-request overhead through request consolidation.
Unique: OpenAI's managed API infrastructure with optimized streaming protocol for real-time token delivery and batch processing system designed for efficient throughput, using request consolidation and dynamic batching to amortize MoE routing overhead across multiple requests
vs alternatives: Simpler integration than self-hosted models (no infrastructure management), with better streaming latency than competitors due to OpenAI's optimized API infrastructure, while batch processing offers 50-70% cost savings vs. real-time API calls for non-latency-sensitive workloads
Understands and generates text in 50+ languages with reasonable fluency, including major languages (Spanish, French, German, Mandarin, Japanese, Arabic) and many lower-resource languages. The model maintains semantic understanding across language boundaries and can perform tasks like translation, cross-lingual information retrieval, and multilingual summarization. Uses language-agnostic tokenization and embedding spaces to handle diverse character sets and linguistic structures.
Unique: Trained on diverse multilingual corpora with language-agnostic embedding spaces, using MoE expert specialization where different experts handle different language families (e.g., one expert for Romance languages, another for Sino-Tibetan languages), enabling consistent quality across 50+ languages
vs alternatives: Supports more languages than GPT-3.5 with better quality than open-source multilingual models, while being cheaper than GPT-4 and faster due to sparse activation reducing per-token compute for multilingual inference
Maintains coherent conversation state across multiple turns, where each response is informed by the full conversation history and previous context. The model tracks entities, relationships, and discussion topics across turns, enabling natural follow-up questions and references to earlier statements without explicit re-specification. Uses attention mechanisms to weight recent context more heavily while still maintaining awareness of earlier conversation points, with support for explicit context management through system prompts and conversation summaries.
Unique: Trained with multi-turn conversation data using OpenAI's proprietary RLHF approach, with MoE expert routing that specializes in conversation context tracking and entity resolution, enabling natural multi-turn conversations without explicit context management frameworks
vs alternatives: Better multi-turn coherence than GPT-3.5 with lower cost than GPT-4, while being faster than Claude due to sparse activation and more consistent context tracking than open-source models due to supervised fine-tuning on conversation data
+1 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-120b 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