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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $6.20e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 8 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Processes multi-turn conversations using a transformer-based architecture trained on instruction-following datasets, maintaining context across dialogue turns through attention mechanisms over the full conversation history. Implements chain-of-thought reasoning patterns to decompose complex queries into intermediate reasoning steps before generating final responses, enabling coherent multi-step problem solving within a single conversation thread.
Unique: Trained on Microsoft's Wizard instruction-following datasets which emphasize complex reasoning and multi-step problem decomposition; uses mixture-of-experts (8x22B) architecture to route different reasoning types through specialized expert pathways, enabling more nuanced handling of diverse task types compared to dense models
vs alternatives: Outperforms open-source alternatives on instruction-following benchmarks while maintaining competitive performance with proprietary models like GPT-4, with the advantage of being accessible via standard API without vendor lock-in
Generates syntactically correct code across multiple programming languages by leveraging training on large code corpora and instruction-tuning for code-specific tasks. Produces not just code but accompanying explanations of logic, architectural patterns, and implementation choices. Uses attention mechanisms to understand code context and generate contextually appropriate completions that follow language idioms and best practices.
Unique: Instruction-tuned specifically for code tasks through Wizard training methodology, enabling it to generate not just functional code but well-documented, idiomatic implementations with explicit reasoning about design choices; mixture-of-experts routing allows specialized handling of different programming paradigms
vs alternatives: Produces more readable and documented code than base models while maintaining competitive quality with specialized code models like Codex, with the advantage of being openly available and not restricted to specific languages or frameworks
Answers factual and analytical questions by synthesizing information from its training data and applying multi-step reasoning to arrive at well-justified answers. Implements reasoning-before-response patterns where the model explicitly works through the logic of a question before stating conclusions. Supports both factual recall and analytical reasoning tasks, with the ability to acknowledge uncertainty and explain the basis for answers.
Unique: Trained with instruction-following on reasoning-heavy datasets that emphasize explicit working-through of complex questions; mixture-of-experts architecture allows different expert pathways for factual vs. analytical reasoning, improving accuracy across diverse question types
vs alternatives: Demonstrates stronger reasoning transparency and multi-step problem solving than many open models while maintaining competitive accuracy with proprietary models, with explicit training for acknowledging uncertainty rather than confident hallucination
Generates diverse written content from creative fiction to technical documentation by leveraging instruction-tuning on varied writing styles and domains. Adapts tone, formality, and structure based on implicit or explicit instructions about the target audience and purpose. Uses attention over writing conventions and stylistic patterns to maintain consistency within generated documents and match specified writing styles.
Unique: Instruction-tuned across diverse writing domains through Wizard training, enabling style adaptation and tone control that goes beyond simple template filling; mixture-of-experts routing allows specialized handling of technical vs. creative writing tasks
vs alternatives: Produces more stylistically consistent and domain-appropriate content than general-purpose models while being more flexible than specialized writing models, with the advantage of handling both technical and creative tasks in a single model
Solves logical puzzles, mathematical problems, and constraint satisfaction tasks by applying structured reasoning patterns and symbolic manipulation. Implements step-by-step logical deduction where the model explicitly works through logical implications and constraints before arriving at conclusions. Handles problems requiring tracking multiple constraints and reasoning about their interactions.
Unique: Trained with explicit instruction-following on reasoning-heavy datasets that emphasize logical step-by-step working; mixture-of-experts architecture routes logical reasoning tasks through specialized expert pathways optimized for symbolic manipulation and constraint tracking
vs alternatives: Demonstrates stronger explicit reasoning transparency and multi-step logical deduction than general models while maintaining competitive performance with specialized reasoning models, with the advantage of handling diverse reasoning types in a single model
Supports structured function calling and API integration by understanding function schemas and generating appropriately formatted function calls. Parses function definitions, understands parameter requirements and types, and generates valid function call syntax that can be executed by external systems. Enables chaining multiple function calls to accomplish complex tasks that require interaction with external tools or APIs.
Unique: Instruction-tuned for function calling through Wizard training on tool-use datasets; mixture-of-experts routing allows specialized handling of function schema understanding and parameter generation, improving accuracy of generated function calls
vs alternatives: Provides reliable function calling without requiring proprietary function-calling APIs, enabling integration with any external system via standard function definitions, while maintaining competitive accuracy with specialized function-calling models
Processes and generates text in multiple languages with understanding of language-specific grammar, idioms, and cultural context. Implements cross-lingual transfer learning where knowledge from high-resource languages improves performance on lower-resource languages. Supports code-switching and maintains language consistency within generated text while respecting language-specific conventions.
Unique: Trained on diverse multilingual instruction-following datasets through Wizard methodology, enabling language-aware generation that respects language-specific conventions; mixture-of-experts architecture may route language-specific processing through specialized experts
vs alternatives: Handles multilingual tasks in a single model without requiring separate language-specific models, with instruction-following enabling better control over language choice and translation style compared to base multilingual models
Generates responses while respecting safety guidelines and refusing to engage with harmful requests. Implements safety filtering through training on instruction-following datasets that include examples of appropriate refusals and boundary-setting. Distinguishes between legitimate requests for sensitive information (e.g., educational content about security) and genuinely harmful requests, enabling nuanced safety without over-censoring.
Unique: Instruction-tuned for nuanced safety through Wizard training on datasets that distinguish between harmful and legitimate sensitive requests; enables context-aware refusals that explain reasoning rather than silent blocking
vs alternatives: Provides more nuanced safety decisions than rule-based filtering while maintaining better transparency than black-box safety mechanisms, with explicit training for explaining refusals rather than just blocking requests
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 WizardLM-2 8x22B at 23/100.
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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