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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $2.00e-6 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates coherent text up to 256K tokens using a hybrid State Space Model (SSM) and Transformer architecture that balances computational efficiency with long-range dependency modeling. The SSM components handle sequential processing with linear complexity, while Transformer layers provide attention-based refinement, enabling efficient processing of extended contexts without quadratic memory scaling typical of pure Transformer models.
Unique: Hybrid SSM-Transformer architecture achieves linear complexity in sequence length through State Space Models while maintaining Transformer attention for critical dependencies, reducing memory overhead from O(n²) to O(n) compared to pure Transformer implementations at 256K context
vs alternatives: More efficient than Claude 3.5 Sonnet (200K context) or GPT-4 Turbo (128K context) for long-context tasks due to linear SSM scaling, while maintaining competitive instruction-following quality
Executes multi-step instructions with improved grounding through fine-tuning on instruction-following datasets and factual consistency benchmarks. The model uses attention mechanisms to anchor outputs to provided context, reducing hallucinations when given explicit constraints, references, or factual anchors within the prompt.
Unique: Fine-tuned specifically for grounding outputs to provided context through instruction-following datasets, using attention mechanisms to anchor generation to source material rather than relying solely on general knowledge
vs alternatives: Improved grounding over base Jamba models and competitive with Claude 3.5 for instruction adherence, with better efficiency due to SSM architecture
Generates and understands text across multiple languages using a unified tokenizer and embedding space trained on multilingual corpora. The model applies the same SSM-Transformer architecture across language pairs without language-specific routing, enabling code-switching and cross-lingual reasoning within single responses.
Unique: Unified multilingual architecture without language-specific routing or switching overhead, enabling seamless code-switching and cross-lingual reasoning within single generation passes
vs alternatives: More efficient than language-specific model selection approaches used by some competitors, with comparable multilingual quality to GPT-4 but with better inference efficiency
Achieves lower inference latency and reduced computational overhead through the SSM-Transformer hybrid architecture, which replaces quadratic attention complexity with linear SSM processing for most sequence positions. This enables faster token generation and lower memory consumption during inference compared to pure Transformer models of similar capability.
Unique: Linear-complexity SSM components reduce per-token latency from O(n) to O(1) amortized cost for most sequence positions, while Transformer layers provide O(n) attention only where needed, resulting in 20-40% latency reduction vs pure Transformer models
vs alternatives: Faster inference than GPT-4 Turbo and Claude 3.5 Sonnet due to linear SSM scaling, with comparable quality and better cost-efficiency per token
Generates structured outputs (JSON, XML, code) that conform to provided schemas through constrained decoding and fine-tuning on structured generation tasks. The model uses attention mechanisms to track schema constraints during generation, ensuring outputs match specified formats without post-processing validation.
Unique: Fine-tuned for structured generation with implicit schema tracking through attention mechanisms, enabling reliable JSON/XML output without explicit schema parameters or post-processing
vs alternatives: Comparable to Claude 3.5's structured output capability but with better latency due to SSM architecture; less formal than OpenAI's JSON mode but more flexible for custom schemas
Understands and generates code across multiple programming languages using a tokenizer optimized for code syntax and a training corpus including public code repositories. The model applies the same SSM-Transformer architecture to code as natural language, enabling code completion, refactoring, and explanation without language-specific routing.
Unique: Code-optimized tokenizer and training corpus enable efficient code understanding without language-specific routing, with SSM architecture providing linear-complexity processing for long code files
vs alternatives: Comparable code quality to GitHub Copilot and Claude 3.5 for generation, with better latency for long files due to SSM architecture; less specialized than Codex but more efficient
Maintains coherent multi-turn conversations by leveraging the 256K context window to preserve full conversation history without summarization or truncation. The SSM-Transformer architecture efficiently processes extended conversation history, enabling the model to reference earlier turns and maintain consistent personality and context across hundreds of exchanges.
Unique: 256K context window enables full conversation history preservation without summarization, with SSM architecture providing linear-complexity processing of extended history
vs alternatives: Better context preservation than models with smaller context windows (GPT-4 Turbo at 128K), with more efficient processing than pure Transformer models due to SSM architecture
Performs semantic reasoning and understanding tasks through transformer attention layers that model long-range semantic dependencies, combined with SSM components for efficient sequential processing. The model applies multi-head attention to capture multiple semantic relationships simultaneously, enabling complex reasoning about meaning, intent, and logical relationships.
Unique: Hybrid SSM-Transformer architecture enables efficient semantic reasoning by using Transformer attention for semantic dependencies while SSM components handle sequential context, reducing computational overhead vs pure Transformer models
vs alternatives: Comparable semantic reasoning to GPT-4 and Claude 3.5, with better efficiency and lower latency due to SSM architecture
+1 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 AI21: Jamba Large 1.7 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