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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.00e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 11 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates text by selectively activating only 11B of 196B parameters per token using a sparse Mixture of Experts (MoE) architecture. The model routes each token through a gating network that determines which expert modules to activate, reducing computational overhead while maintaining capability. This sparse activation pattern enables efficient inference without full model evaluation, trading off some latency for dramatically reduced memory and compute requirements compared to dense models of equivalent parameter count.
Unique: Uses a 196B parameter sparse MoE architecture that activates only 11B parameters per token through learned gating, achieving dense-model capability with sparse-model efficiency. This differs from dense models (which activate all parameters) and from other MoE implementations by optimizing the expert routing mechanism specifically for language understanding and generation tasks.
vs alternatives: Delivers comparable reasoning quality to dense 70B+ models while requiring 60-70% less compute per inference token than dense alternatives, making it faster and cheaper than GPT-4 or Llama 2 70B for equivalent capability levels.
Maintains and processes multi-turn conversation history by accepting role-based message sequences (system, user, assistant) and maintaining coherent context across exchanges. The model processes the entire conversation history as a single input sequence, with special tokens demarcating role boundaries, allowing it to track conversation state, maintain consistency in persona and knowledge, and reference previous exchanges. This enables stateless conversation handling where each request includes full history, avoiding server-side session management complexity.
Unique: Implements conversation context through stateless message arrays rather than server-side session storage, allowing clients to manage full conversation history and reducing backend complexity. The sparse MoE architecture processes this history efficiently by routing tokens through relevant experts based on conversation content.
vs alternatives: Simpler to deploy and scale than models requiring session management, while maintaining conversation coherence comparable to stateful chatbot systems like ChatGPT, at lower infrastructure cost.
Summarizes long documents or conversations into concise overviews while preserving key information. The model can generate summaries at different detail levels (brief bullet points, paragraph summaries, executive summaries) and can focus on specific aspects of the source material. This is implemented through instruction-following that specifies summary length, style, and focus areas.
Unique: Implements summarization through sparse expert routing that activates compression and key-information-extraction specialists based on document type and summary requirements. This allows efficient summarization without the parameter overhead of dense models.
vs alternatives: Provides summarization quality comparable to GPT-4 while being 40-50% cheaper, making it cost-effective for high-volume document processing and knowledge management workflows.
Generates and completes code across multiple programming languages by understanding syntax, semantics, and common patterns. The model was trained on diverse code repositories and can generate syntactically valid code, complete partial implementations, suggest refactorings, and explain code logic. It handles context from surrounding code to make completion suggestions that fit the existing codebase style and architecture, though it operates without access to the actual codebase structure or type information.
Unique: Leverages sparse MoE routing to efficiently handle code generation across 40+ languages by activating language-specific expert modules based on detected syntax and patterns. This allows a single model to maintain high-quality code generation across diverse languages without the parameter overhead of dense models.
vs alternatives: Faster and cheaper than Copilot or Claude for code generation due to sparse activation, while maintaining multi-language support comparable to GPT-4, making it suitable for cost-sensitive development tool integrations.
Performs multi-step reasoning by generating intermediate thinking steps that break down complex problems into manageable sub-tasks. The model can articulate its reasoning process, identify dependencies between steps, and build solutions incrementally. This capability enables solving problems that require planning, logical deduction, or mathematical reasoning by having the model explicitly work through each step rather than jumping directly to answers.
Unique: Implements reasoning through sparse expert routing that activates reasoning-specialized modules for complex tasks while maintaining efficiency. The MoE architecture allows the model to allocate more parameters to reasoning steps when needed without the overhead of a dense model.
vs alternatives: Provides reasoning transparency comparable to GPT-4 or Claude while consuming 40-50% fewer tokens due to sparse activation, making it cost-effective for reasoning-heavy applications.
Follows detailed instructions and adapts behavior based on system prompts that define role, constraints, output format, and task-specific rules. The model interprets natural language instructions and applies them consistently across multiple turns, allowing fine-grained control over response style, tone, and content restrictions. This is implemented through the system message role in multi-turn conversations, which establishes context that influences all subsequent responses.
Unique: Implements instruction-following through the sparse MoE architecture by routing tokens through instruction-interpretation experts that specialize in understanding and applying constraints. This allows efficient instruction-following without the parameter overhead of dense models.
vs alternatives: Provides instruction-following quality comparable to GPT-4 or Claude while being 40-50% cheaper to run, making it suitable for cost-sensitive applications requiring customizable AI behavior.
Answers questions and synthesizes information by processing provided context (documents, code, data) and extracting relevant information to formulate responses. The model reads through provided context, identifies relevant passages or concepts, and generates answers grounded in that context. This enables question-answering over custom documents without requiring external retrieval systems, though it's limited by context window size and doesn't perform semantic search across large document collections.
Unique: Implements context-aware question-answering through sparse expert routing that activates retrieval and synthesis experts based on question type and context content. This allows efficient processing of context without the parameter overhead of dense models.
vs alternatives: Simpler to implement than full RAG systems while providing comparable accuracy for small-to-medium documents, at lower cost than dense models. Suitable for applications where context fits in a single prompt.
Generates creative content (stories, poetry, marketing copy, dialogue) with controllable style and tone through natural language instructions. The model can adapt its writing style to match specified tones (formal, casual, humorous, etc.), genres, and audience levels. This is implemented through instruction-following capabilities combined with the model's training on diverse creative content, allowing fine-grained control over output characteristics without requiring fine-tuning.
Unique: Leverages sparse MoE routing to activate creative-writing specialists based on detected genre and style cues, allowing efficient generation of diverse creative content without the parameter overhead of dense models trained on all writing styles.
vs alternatives: Provides creative quality comparable to GPT-4 or Claude while being 40-50% cheaper, making it cost-effective for high-volume creative content generation in marketing and content creation workflows.
+3 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
StepFun: Step 3.5 Flash scores higher at 24/100 vs Z.ai: GLM 4.5 at 24/100.
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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