| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $6.00e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 5 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates contextually rich dialogue and character-driven narratives through fine-tuning on roleplay datasets and narrative corpora. The model uses a merged architecture combining Llama 2 13B base weights with specialized adapters trained on creative writing and character interaction patterns, enabling coherent multi-turn conversations with consistent persona maintenance and descriptive narrative flourishes without explicit prompt engineering.
Unique: Specialized merge of Llama 2 13B with roleplay-specific fine-tuning that prioritizes narrative richness and character consistency over general-purpose instruction-following, using curated creative writing datasets rather than generic instruction tuning
vs alternatives: Outperforms base Llama 2 and generic chat models on creative roleplay tasks due to specialized training, while remaining smaller and faster than 70B+ models, making it cost-effective for indie developers
Maintains coherent conversation state across multiple exchanges by processing full dialogue history within the context window, using transformer attention mechanisms to weight recent messages and character context more heavily. The model tracks implicit conversational state (character mood, relationship dynamics, narrative threads) without explicit state variables, relying on learned patterns from roleplay training data to infer and maintain consistency across turns.
Unique: Roleplay-specific fine-tuning enables implicit tracking of character relationships and emotional arcs across conversation turns without explicit state machines, learned from narrative datasets where character consistency is critical
vs alternatives: Better at maintaining character consistency across long conversations than base Llama 2 due to creative writing training, though less sophisticated than explicit memory systems like RAG or conversation summarization pipelines
Generates detailed, evocative descriptions and narrative prose by leveraging fine-tuning on creative writing corpora that emphasize sensory details, metaphor, and literary style. The model produces longer, more elaborate responses with environmental descriptions and action narration compared to instruction-tuned models, using learned patterns from fantasy, science fiction, and interactive fiction training data to construct multi-sentence narrative blocks.
Unique: Fine-tuned specifically on creative writing and roleplay datasets that prioritize rich, descriptive prose over concise instruction-following, producing naturally elaborate narratives without requiring verbose prompts
vs alternatives: Produces more literary and descriptive output than base Llama 2 or generic chat models, though less controllable than models with explicit style parameters or dedicated creative writing fine-tunes
Provides model inference through OpenRouter's HTTP API with support for streaming token-by-token responses, enabling real-time output display in client applications. Requests are routed through OpenRouter's infrastructure which handles model loading, batching, and response streaming via Server-Sent Events (SSE), allowing developers to display model output progressively without waiting for full completion.
Unique: Accessed exclusively through OpenRouter's managed API with streaming support, rather than direct model weights or local inference, providing abstraction over infrastructure while enabling real-time response delivery
vs alternatives: Simpler to integrate than self-hosted inference (no GPU required, no model management), and streaming capability provides better UX than batch API calls, though with higher latency and ongoing API costs
Executes user instructions with a bias toward creative, narrative-rich responses due to fine-tuning on roleplay and creative writing datasets. The model balances instruction adherence with creative elaboration, using learned patterns to expand simple requests into richer outputs while still following explicit directives. This differs from pure instruction-tuned models which prioritize conciseness and direct compliance.
Unique: Balances instruction adherence with creative elaboration through roleplay-specific fine-tuning, producing naturally richer responses than base models without requiring verbose prompts, while maintaining instruction compliance
vs alternatives: Better at creative instruction-following than base Llama 2, though less suitable for technical tasks than general-purpose instruction-tuned models like Mistral or Hermes
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 MythoMax 13B 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
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