| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $3.90e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 11 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-4.5 uses a Mixture-of-Experts (MoE) architecture to dynamically route tokens through specialized expert networks based on input characteristics, enabling efficient processing of 128k-token contexts without proportional latency increases. The MoE design allows selective expert activation per token, reducing computational overhead while maintaining reasoning depth across extended conversations and multi-document analysis tasks typical of agent-based workflows.
Unique: Mixture-of-Experts routing specifically tuned for agent workloads rather than generic dense models; expert activation patterns are optimized for tool-use sequences and multi-step reasoning rather than general language tasks
vs alternatives: Outperforms dense models like GPT-4 Turbo on agent tasks within 128k context by routing computational budget to relevant experts, reducing latency and cost vs. models that process all tokens through identical layers
GLM-4.5 implements native function calling through a schema-based registry where tools are defined as JSON schemas with parameter constraints, type validation, and description metadata. The model learns to emit structured tool invocations that map directly to function signatures, enabling deterministic tool orchestration without post-processing or regex parsing. Integration with OpenRouter's API exposes this via standard function-calling parameters compatible with OpenAI's format.
Unique: Schema-based function calling is trained directly into the model weights rather than implemented as post-hoc decoding constraints, allowing the model to learn semantic relationships between tool purposes and input context during training
vs alternatives: More reliable than constraint-based function calling (e.g., Guidance, LMQL) because tool selection is learned rather than enforced, reducing parsing failures and enabling the model to reason about tool applicability
Z.ai: GLM 4.5 scores higher at 24/100 vs OpenAI: gpt-oss-120b at 23/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
GLM-4.5 can be used for batch inference through OpenRouter's API, enabling cost-optimized processing of large numbers of requests. Batch processing typically offers reduced pricing compared to real-time API calls and is suitable for non-urgent inference tasks. The model can process batches of prompts efficiently, with results returned after processing completes. This is valuable for agents running scheduled tasks or processing large datasets.
Unique: Batch processing is offered through OpenRouter's unified API rather than a separate batch service, enabling seamless switching between real-time and batch modes with the same client code
vs alternatives: More cost-effective than real-time API for high-volume inference; simpler than managing separate batch infrastructure because OpenRouter handles queuing and result delivery
GLM-4.5 maintains coherent conversation state across turns by encoding prior messages into a compressed representation that persists within the 128k context window. The model uses attention mechanisms to selectively retrieve relevant prior context, enabling agents to reference earlier decisions, tool results, and user preferences without explicit memory management. This is particularly effective for agent workflows where state accumulation (e.g., task progress, discovered facts) must inform subsequent actions.
Unique: Implicit memory management through attention-based context selection rather than explicit memory modules; the model learns which prior turns are relevant without separate retrieval or summarization steps
vs alternatives: More efficient than explicit memory systems (e.g., LangChain's ConversationBufferMemory) because attention is computed once during inference rather than requiring separate retrieval and summarization passes
GLM-4.5 generates code across 40+ programming languages by leveraging training data that includes diverse codebases and syntax patterns. The model understands language-specific idioms, library conventions, and structural patterns (e.g., async/await in JavaScript, type hints in Python, generics in Java) without explicit language-specific modules. Generation is context-aware, respecting indentation, existing code style, and project conventions when completing or extending code snippets.
Unique: Language-agnostic code generation trained on diverse codebases rather than language-specific fine-tuning; the model generalizes syntax patterns across languages, enabling reasonable code generation even for less common languages
vs alternatives: Broader language coverage than specialized models like Codex (which emphasizes Python/JavaScript) but lower quality on niche languages compared to language-specific models; better for polyglot teams than single-language specialists
GLM-4.5 is trained on extensive technical documentation, API references, and code examples, enabling it to understand and reason about complex technical concepts, library APIs, and system architectures. The model can parse API schemas (OpenAPI, GraphQL, Protocol Buffers), understand parameter constraints and type systems, and generate code that correctly uses APIs based on documentation. This is particularly valuable for agent workflows that must interact with external systems.
Unique: Semantic understanding of API schemas and documentation is learned from training data rather than implemented as a separate schema parser; the model reasons about API semantics holistically
vs alternatives: More flexible than code-generation-only models because it understands API semantics and can reason about correctness; better than generic LLMs for technical tasks because training includes extensive API documentation
GLM-4.5 can generate responses that explicitly show reasoning steps, enabling transparency into how conclusions were reached. When prompted with chain-of-thought patterns, the model generates intermediate reasoning steps before final answers, making it suitable for applications requiring explainability or verification. This is implemented through training on reasoning-annotated data and prompt patterns that encourage step-by-step decomposition.
Unique: Chain-of-thought reasoning is trained directly into the model rather than implemented as a decoding strategy; the model learns to generate reasoning steps as part of its core training objective
vs alternatives: More natural and coherent reasoning steps than prompt-injection approaches (e.g., appending 'think step by step') because reasoning is learned as a first-class capability
GLM-4.5 supports multiple languages (Chinese, English, and others) with training that enables cross-lingual reasoning — understanding concepts expressed in one language and reasoning about them in another. The model can translate, summarize, and reason across languages without language-specific degradation. This is particularly valuable for global applications and agents that must operate in multilingual environments.
Unique: Cross-lingual reasoning is learned from multilingual training data rather than implemented as separate language-specific models; the model develops a shared representation across languages
vs alternatives: More efficient than maintaining separate models per language because a single model handles all languages; better for cross-lingual reasoning than language-specific models because the shared representation enables concept transfer
+3 more capabilities