| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $4.50e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 5 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Engages in extended dialogue by leveraging a SLERP (Spherical Linear Interpolation) merge of multiple base models, combining their learned representations in weight space to balance reasoning depth, instruction-following, and creative generation. The model maintains conversation context across turns and adapts responses based on dialogue history, using the merged weight distribution to optimize for both factual accuracy and nuanced reasoning.
Unique: Uses SLERP (Spherical Linear Interpolation) weight merging to combine multiple base models' learned representations in a single 13B parameter model, rather than using a single base model or ensemble approach. This approach preserves the geometric structure of weight space while blending complementary capabilities from source models.
vs alternatives: Offers better cost-to-capability ratio than 70B+ models and more balanced reasoning than single-purpose 13B models, but with emergent behavior that may be less predictable than non-merged alternatives.
Processes structured and unstructured prompts by applying learned instruction-following patterns from merged component models, dynamically balancing adherence to explicit user directives with creative generation when appropriate. The SLERP merge weights multiple instruction-tuned models to optimize for both strict compliance and contextual flexibility, allowing the model to interpret ambiguous instructions and generate novel solutions.
Unique: The SLERP merge combines instruction-tuned models with varying creativity-compliance trade-offs, creating a single model that adapts to both rigid and open-ended tasks through learned weight interpolation rather than explicit control parameters.
vs alternatives: Avoids the latency and complexity of ensemble methods or model switching, providing a single inference endpoint that handles both instruction-following and creative tasks better than non-merged 13B baselines.
Delivers model outputs via OpenRouter's streaming API, allowing real-time token-by-token response generation with minimal latency. The integration handles authentication, rate limiting, and response formatting transparently, enabling developers to build responsive conversational interfaces without managing model infrastructure directly.
Unique: Leverages OpenRouter's managed API infrastructure to abstract away model deployment, scaling, and infrastructure management while providing streaming responses that enable real-time user interactions.
vs alternatives: Eliminates infrastructure overhead compared to self-hosted models, and provides more responsive streaming than batch API endpoints, though with added latency and cost compared to local inference.
Maintains and processes multi-turn conversation context by encoding prior dialogue into the model's input, allowing responses to reference previous messages, maintain consistent personas, and build on earlier reasoning. The model uses attention mechanisms to weight relevant context from conversation history, enabling coherent long-form discussions without explicit memory structures.
Unique: Relies on attention-based context encoding rather than explicit memory structures, allowing the merged model to dynamically weight relevant prior exchanges based on learned patterns from training data.
vs alternatives: Simpler to implement than external memory systems (RAG, vector stores) for short-to-medium conversations, but requires careful context management for longer dialogues compared to models with explicit memory mechanisms.
Generates executable code and technical explanations by leveraging the merged model's instruction-following and reasoning capabilities, producing code snippets with inline comments and step-by-step explanations. The model can handle multiple programming languages and explain its reasoning for code structure, making it suitable for both code generation and educational contexts.
Unique: The SLERP merge balances code generation quality with reasoning depth, allowing the model to both generate code and explain its decisions without requiring separate specialized models.
vs alternatives: More cost-effective than larger code-specialized models (like CodeLlama-34B) while maintaining reasonable code quality, though with lower accuracy on complex algorithmic problems compared to larger baselines.
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 ReMM SLERP 13B at 18/100.
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