Z.ai: GLM 4.6

Generates coherent multi-turn conversations and long-form text outputs within a 200K token context window, enabling processing of documents, codebases, and conversation histories that would exceed typical model limits. The architecture maintains semantic coherence across extended sequences through optimized attention mechanisms and positional encoding schemes designed to handle the expanded token budget without degradation in reasoning quality or response relevance.

Maintains coherent dialogue state across multiple conversation turns by tracking message history, user intent evolution, and contextual references within the 200K token budget. The model uses transformer-based attention mechanisms to weight recent messages more heavily while preserving long-range dependencies, enabling natural conversation flow without explicit state management overhead on the client side.

Analyzes and generates code with awareness of entire file structures, imports, and cross-file dependencies by processing complete codebases within the 200K token context. The model uses transformer attention to identify structural patterns, dependency relationships, and semantic meaning across multiple files simultaneously, enabling context-aware code completion, refactoring suggestions, and bug detection without requiring external AST parsing or symbol table construction.

Processes long-form documents (research papers, technical specifications, legal contracts, reports) and extracts structured information, summaries, and insights by maintaining full document context within the 200K token window. The model applies reading comprehension patterns learned during training to identify key sections, extract entities, relationships, and actionable insights, then formats output as JSON, tables, or natural language summaries based on user specification.

Performs complex multi-step reasoning, problem decomposition, and planning tasks by leveraging the 200K token context to maintain detailed intermediate reasoning steps, hypotheses, and decision trees. The model generates explicit chain-of-thought outputs that trace logical progression from problem statement through analysis to conclusion, enabling transparency in reasoning and the ability to backtrack or explore alternative approaches within a single generation.

Provides OpenAI-compatible Chat Completions API interface accessible through OpenRouter, enabling drop-in integration with existing LLM applications without code changes. The model is exposed via standard HTTP endpoints supporting streaming responses, function calling, temperature/top-p sampling controls, and batch processing, with OpenRouter handling authentication, rate limiting, load balancing, and provider failover.

Generates and understands text across multiple languages with maintained semantic coherence and cultural appropriateness, leveraging training data spanning diverse language families. The model applies language-agnostic transformer patterns to handle morphological complexity, script differences, and idiomatic expressions, enabling code-switching, translation-adjacent tasks, and multilingual reasoning within single prompts.

Enables the model to request execution of external functions or tools by returning structured function call specifications that client applications parse and execute. The model learns to identify when a task requires external computation (API calls, database queries, code execution) and generates properly-formatted function call requests with parameters, which the client application executes and returns results for the model to incorporate into final responses.

Generates responses token-by-token and streams them to the client in real-time via Server-Sent Events (SSE), enabling progressive display of output as it's generated rather than waiting for complete response. The streaming architecture reduces perceived latency and enables responsive user interfaces that display model output incrementally, with support for cancellation and early termination.