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| Pricing | Paid | Paid |
| Starting Price | $5.10e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 10 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates coherent, contextually-aware responses in multi-turn conversations using instruction-tuned transformer architecture optimized for dialogue. The model maintains conversation history through standard transformer context windows (8K tokens) and applies instruction-following fine-tuning to prioritize user intent over raw next-token prediction, enabling it to follow explicit directives, refuse harmful requests, and maintain consistent persona across exchanges.
Unique: 70B parameter scale with instruction-tuning specifically optimized for dialogue (vs. base models or smaller instruct variants) provides superior instruction-following and nuance in conversational contexts while remaining computationally efficient compared to 405B models. Uses standard transformer architecture with rotary position embeddings and grouped query attention for efficient context handling.
vs alternatives: Outperforms GPT-3.5 on instruction-following benchmarks while being 3-5x cheaper than GPT-4, and offers better dialogue quality than smaller open models (7B-13B) due to parameter scale and instruction-tuning depth.
Analyzes and explains code snippets, generates code walkthroughs, and reasons about algorithmic correctness by leveraging instruction-tuning that emphasizes logical decomposition and step-by-step explanation. The model can parse code syntax, identify patterns, and generate detailed explanations of what code does and why, though it does not perform actual code execution or static analysis.
Unique: Instruction-tuning emphasizes step-by-step reasoning and explanation (similar to chain-of-thought training) applied to code analysis, enabling more detailed walkthroughs than base models. 70B scale provides sufficient capacity to reason about complex algorithms without hallucinating syntax.
vs alternatives: Provides better code explanations than GPT-3.5 and comparable quality to GPT-4 at significantly lower cost, though lacks the specialized code-understanding of models fine-tuned specifically on programming tasks like Codestral or specialized code LLMs.
Extracts structured information (entities, relationships, key-value pairs) from natural language text by leveraging instruction-tuning to follow explicit extraction schemas and output formats. The model can parse instructions like 'extract all email addresses and associated names' or 'convert this paragraph into JSON with fields X, Y, Z' and generate structured outputs, though without formal schema validation or type enforcement.
Unique: Instruction-tuning enables the model to follow arbitrary output format specifications without fine-tuning, using natural language instructions to define extraction schemas. 70B scale provides sufficient reasoning capacity to handle complex multi-field extraction and conditional logic.
vs alternatives: More flexible than regex-based extraction (handles ambiguous cases) and cheaper than specialized NER models or commercial extraction APIs, though less accurate than fine-tuned extractors or formal parsing approaches for highly structured domains.
Generates original written content (articles, emails, documentation, creative fiction) while adapting to specified tone, style, and audience through instruction-tuning that emphasizes stylistic control and user intent alignment. The model can generate content ranging from formal technical documentation to casual marketing copy by following explicit style instructions and examples, maintaining coherence across multi-paragraph outputs.
Unique: Instruction-tuning optimizes for following explicit style and tone instructions, enabling fine-grained control over voice and register without fine-tuning. 70B scale provides sufficient capacity for coherent long-form writing with consistent style across multiple paragraphs.
vs alternatives: Offers better style control and coherence than smaller models (7B-13B) and comparable quality to GPT-4 at lower cost, though less specialized than domain-specific writing models or human writers for high-stakes content requiring deep domain expertise.
Answers questions and synthesizes information from provided context (documents, code, specifications) by reading and reasoning over the supplied text without external knowledge retrieval. The model processes context windows up to ~8K tokens and generates answers grounded in that context, useful for Q&A over documents, FAQs, and knowledge base queries without requiring vector databases or RAG systems.
Unique: Instruction-tuning emphasizes grounding answers in provided context and explicitly acknowledging when information is not available, reducing hallucination compared to base models. 70B scale enables complex reasoning over multi-document context without external retrieval systems.
vs alternatives: Simpler to implement than RAG systems (no vector database required) and faster for small contexts, but less scalable than retrieval-augmented approaches for large knowledge bases. Comparable to GPT-4 for context-grounded Q&A at lower cost.
Solves complex problems by breaking them into steps, reasoning through each component, and synthesizing solutions. The instruction-tuning emphasizes chain-of-thought reasoning patterns, enabling the model to articulate intermediate steps, identify assumptions, and correct errors mid-reasoning. Useful for math problems, logic puzzles, debugging, and decision-making scenarios where explicit reasoning is valuable.
Unique: Instruction-tuning explicitly optimizes for chain-of-thought reasoning patterns, enabling the model to articulate intermediate steps and self-correct. 70B scale provides sufficient capacity for multi-step reasoning without losing coherence.
vs alternatives: Better reasoning transparency than smaller models and comparable to GPT-4 on many reasoning tasks at lower cost, though specialized reasoning models or symbolic solvers may outperform on highly constrained domains like formal mathematics.
Condenses long documents, articles, or conversations into summaries of varying lengths and detail levels by following explicit summarization instructions. The model can generate executive summaries, bullet-point summaries, or detailed abstracts while preserving key information and maintaining factual accuracy relative to source material. Supports both extractive (selecting key sentences) and abstractive (rephrasing) summarization patterns.
Unique: Instruction-tuning enables flexible summarization with configurable detail levels and output formats without fine-tuning. 70B scale provides sufficient capacity to understand document structure and identify key information across diverse domains.
vs alternatives: More flexible than extractive summarization tools (handles abstractive summarization) and cheaper than specialized summarization APIs, though less accurate than fine-tuned summarization models for domain-specific documents.
Translates text between languages and adapts content for different linguistic and cultural contexts. The model supports translation from English to many languages and vice versa, with instruction-tuning enabling control over formality level, terminology, and cultural adaptation. Translations maintain semantic meaning while adapting for target language idioms and conventions.
Unique: Instruction-tuning enables control over formality level and cultural adaptation without fine-tuning. 70B scale provides sufficient multilingual capacity for accurate translation across diverse language pairs and domains.
vs alternatives: Cheaper and more flexible than professional translation services, comparable to Google Translate for quality on common language pairs, but less specialized than domain-specific translation models or professional human translators for critical content.
+2 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 Meta: Llama 3 70B Instruct 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