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| Pricing | Paid | Paid |
| Starting Price | $2.70e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 5 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates coherent, contextually-aware text responses to natural language instructions using a 1B-parameter transformer architecture fine-tuned on instruction-following datasets. The model processes input tokens through multi-head attention layers and produces output via autoregressive decoding, optimized for dialogue and conversational tasks through instruction-tuning rather than raw next-token prediction.
Unique: 1B-parameter scale with instruction-tuning specifically optimized for dialogue and conversational tasks, enabling sub-100ms latency inference on commodity hardware while maintaining coherent multi-turn conversation — trades reasoning depth for deployment efficiency
vs alternatives: Smaller and faster than Llama 3.1 8B or Mistral 7B for dialogue workloads, but with lower accuracy on reasoning tasks; more efficient than GPT-4 for cost-sensitive applications, but less capable on complex instructions
Processes and generates text across multiple languages using a shared transformer vocabulary trained on multilingual instruction-following data. The model applies language-agnostic attention mechanisms to understand semantic relationships across languages, enabling summarization, translation, and analysis tasks in non-English languages without language-specific fine-tuning.
Unique: Unified multilingual instruction-tuned model avoiding separate language-specific deployments — uses shared transformer vocabulary with attention mechanisms trained on parallel multilingual instruction data, enabling cost-efficient cross-lingual inference
vs alternatives: More cost-effective than deploying separate language-specific models or using larger multilingual models like mT5, but with lower accuracy on low-resource languages compared to specialized translation models
Condenses long-form text into concise summaries by processing full input through transformer attention layers and generating abstractive summaries via instruction-following prompts. The model learns to identify salient information and rewrite it in compressed form, rather than extracting sentences, enabling flexible summary styles (bullet points, paragraphs, key takeaways) based on instruction phrasing.
Unique: Instruction-guided abstractive summarization allowing flexible summary styles (bullet points, paragraphs, key takeaways) via prompt engineering rather than fixed summarization templates — leverages instruction-tuning to interpret summary format directives
vs alternatives: More flexible than extractive summarization tools, but less reliable than larger models (7B+) for factual accuracy; faster and cheaper than GPT-4 for high-volume summarization, but with higher hallucination risk
Adapts to new tasks without retraining by interpreting task descriptions and examples embedded in prompts, using instruction-tuning to generalize from natural language task specifications. The model processes few-shot examples (2-5 demonstrations) or zero-shot instructions through standard transformer attention, enabling rapid task switching without model fine-tuning or separate endpoints.
Unique: Instruction-tuned architecture enabling zero-shot and few-shot task adaptation through natural language prompts without fine-tuning — leverages instruction-following training to interpret task specifications and generalize from minimal examples
vs alternatives: Faster iteration than fine-tuning-based approaches, but with lower accuracy on complex tasks compared to task-specific fine-tuned models; more flexible than fixed-task models, but less capable than larger instruction-tuned models (7B+) at learning from few examples
Exposes model inference through OpenRouter's HTTP API, supporting both streaming (token-by-token responses) and batch processing modes. Requests are routed through OpenRouter's infrastructure, which handles load balancing, rate limiting, and provider selection, returning responses via standard REST endpoints with configurable temperature, top-p, and max-token parameters.
Unique: OpenRouter-hosted inference providing OpenAI-compatible API surface with transparent provider routing and per-token pricing — abstracts underlying infrastructure while maintaining standard LLM API contracts
vs alternatives: More cost-effective than OpenAI API for this model size, with faster inference than self-hosted on CPU; less control than self-hosted deployment, but eliminates infrastructure management overhead
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 Meta: Llama 3.2 1B Instruct at 21/100.
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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