| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.30e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
GLM-4.5-Air processes multi-turn conversations with native support for structured function calling via schema-based tool definitions. The model uses a Mixture-of-Experts (MoE) architecture where only a subset of expert parameters activate per token, reducing inference latency while maintaining reasoning quality. It routes conversation context through sparse expert layers, enabling efficient handling of tool invocations, parameter extraction, and agent decision-making without full model activation.
Unique: Implements MoE-based function calling where expert routing decisions are made per-token, allowing the model to dynamically allocate computation only to relevant experts for tool-calling tasks. This differs from dense models that activate all parameters regardless of task complexity, and from other MoE implementations that use static routing patterns.
vs alternatives: Achieves agent-level reasoning with 40-60% fewer active parameters than dense alternatives like GPT-4, reducing inference cost and latency while maintaining tool-calling accuracy through sparse expert specialization.
GLM-4.5-Air handles extended conversation histories through optimized token management and sparse attention patterns enabled by its MoE architecture. The model compresses context representation by routing only relevant context through active experts, reducing the computational cost of maintaining long conversation state. This allows multi-turn dialogues with hundreds of messages without proportional latency degradation.
Unique: Uses MoE sparse routing to compress context representation — only relevant experts process historical context, avoiding the quadratic attention cost of dense models on long sequences. This enables efficient context reuse without explicit summarization or context pruning strategies.
vs alternatives: Handles 2-3x longer conversation histories than similarly-sized dense models with comparable latency, because sparse expert routing reduces attention computation from O(n²) to approximately O(n·k) where k is the number of active experts.
GLM-4.5-Air can generate responses conforming to strict JSON schemas or structured formats through constrained decoding and schema-aware token routing. The model uses its MoE architecture to specialize certain experts for structured output generation, ensuring responses match predefined schemas without post-processing validation. This enables reliable extraction of entities, relationships, and structured information from unstructured text inputs.
Unique: Leverages MoE expert specialization to route schema-conformance checking through dedicated experts, enabling token-level constraint enforcement without external grammar-based decoding. This differs from regex or grammar-based constrained decoding which operates post-hoc on token sequences.
vs alternatives: Produces schema-compliant JSON with higher first-pass accuracy than post-processing approaches, and with lower latency overhead than grammar-based constrained decoding because schema validation is integrated into expert routing rather than applied as a separate decoding constraint.
GLM-4.5-Air supports server-sent events (SSE) streaming where tokens are emitted as they are generated, enabling real-time response display and token-level monitoring. The model streams through its MoE layers, allowing clients to observe token generation in real-time and implement early-stopping logic based on partial outputs. This architecture enables interactive applications where users see responses appearing incrementally rather than waiting for full generation.
Unique: Implements token-level streaming through MoE expert outputs, where each expert's contribution is streamed independently before being combined. This enables granular token-level observability and early-stopping at the expert routing level rather than post-generation.
vs alternatives: Provides lower latency to first token than batched generation approaches, and enables more granular early-stopping control than models that only support full-response streaming.
GLM-4.5-Air maintains multilingual reasoning capabilities through language-specific expert routing in its MoE architecture. The model activates different expert subsets depending on input language, enabling code generation, mathematical reasoning, and logical inference across programming languages, natural languages, and formal notations. This approach avoids the parameter bloat of dense multilingual models by specializing experts per language family.
Unique: Uses language-family-aware expert routing where different language groups (e.g., Germanic languages, Sino-Tibetan, programming languages) activate specialized expert subsets. This avoids the parameter explosion of dense multilingual models while maintaining language-specific reasoning quality.
vs alternatives: Achieves comparable multilingual code generation quality to larger dense models (GPT-4) with 40-60% fewer parameters by routing computation to language-specific experts rather than activating all parameters for every language.
GLM-4.5-Air's MoE architecture dynamically activates only a subset of expert parameters per token, reducing computational cost compared to dense models. The model routes each token through a gating network that selects 2-4 active experts from a larger pool (typically 64-128 experts), achieving inference cost reduction while maintaining output quality. This sparse activation pattern is transparent to users but directly impacts per-token pricing and latency.
Unique: Implements dynamic expert gating where a learned router network selects active experts per token, enabling sub-linear scaling of inference cost with model size. Unlike static MoE designs, the gating network adapts expert selection based on input tokens, optimizing for both quality and efficiency.
vs alternatives: Achieves 30-50% lower inference cost than dense models of comparable quality (e.g., GPT-3.5-turbo) due to sparse expert activation, while maintaining reasoning quality through selective expert routing rather than parameter reduction.
GLM-5.1 executes multi-step coding tasks over extended timeframes without requiring human intervention between steps, using an internal planning mechanism that decomposes complex objectives into sub-tasks and maintains execution state across sequential operations. Unlike minute-level interaction models that require prompting after each step, this capability enables the model to autonomously navigate decision trees, handle errors, and adapt strategy based on intermediate results without context resets.
Unique: Designed specifically for minute+ autonomous execution windows rather than single-turn interactions; maintains internal execution state and decision-making across extended task horizons without requiring external orchestration or re-prompting between steps
vs alternatives: Outperforms GPT-4 and Claude for long-horizon coding tasks because it's architected for continuous autonomous operation rather than stateless request-response cycles
GLM-5.1 generates and refactors code with awareness of the full codebase structure, dependencies, and patterns, using semantic understanding of how changes in one file propagate to others. The model analyzes import graphs, function signatures, and usage patterns across files to ensure generated code maintains consistency and doesn't introduce breaking changes, rather than treating each file in isolation.
Unique: Maintains semantic awareness of codebase structure and cross-file dependencies during generation, enabling it to make coordinated changes across multiple files rather than treating each file independently
vs alternatives: Produces more consistent multi-file refactorings than Copilot or Claude because it reasons about the entire codebase context simultaneously rather than file-by-file
Z.ai: GLM 5.1 scores higher at 24/100 vs Z.ai: GLM 4.5 Air at 22/100.
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GLM-5.1 diagnoses errors and bugs by analyzing error messages, stack traces, and code context to identify root causes and suggest fixes. The model correlates error symptoms with likely causes, explains why errors occur, and provides specific debugging steps or code fixes.
Unique: Diagnoses errors by correlating symptoms with root causes using semantic understanding of code and error patterns, providing explanations and fixes rather than just pattern matching
vs alternatives: More effective at diagnosing subtle bugs than search-based solutions because it reasons about code semantics and error causality
GLM-5.1 identifies performance bottlenecks in code and suggests optimizations with specific implementation guidance, analyzing algorithms, data structures, and resource usage to recommend improvements. The model understands performance implications of different approaches and can suggest algorithmic or architectural changes to improve efficiency.
Unique: Suggests optimizations based on algorithmic and architectural analysis rather than just code-level tweaks, understanding performance implications of different approaches
vs alternatives: Provides more meaningful performance guidance than generic LLMs because it understands algorithm complexity and can suggest structural improvements
GLM-5.1 analyzes code for security vulnerabilities including injection attacks, authentication/authorization issues, cryptographic weaknesses, and data exposure risks, providing specific remediation guidance. The model understands common vulnerability patterns and security best practices to identify risks and suggest secure implementations.
Unique: Identifies security vulnerabilities through semantic analysis of code patterns and provides remediation guidance based on security best practices, not just pattern matching against known CVEs
vs alternatives: More effective at finding context-specific security issues than SAST tools because it understands code intent and can suggest secure implementations
GLM-5.1 performs step-by-step reasoning about code behavior by internally simulating or tracing execution paths, allowing it to predict runtime behavior, identify bugs, and explain code logic without requiring actual execution. This capability uses chain-of-thought-like reasoning applied specifically to code semantics, tracking variable state, control flow, and function call sequences to reason about correctness.
Unique: Applies extended reasoning specifically to code semantics and execution paths, enabling it to predict runtime behavior and identify subtle bugs through symbolic execution simulation rather than pattern matching
vs alternatives: More effective at finding subtle logic bugs than GPT-4 because it explicitly traces execution state rather than relying on pattern recognition
GLM-5.1 maintains rich context across multiple conversation turns when working on code, remembering previous edits, design decisions, and constraints without requiring users to re-specify them. The model builds an internal model of the codebase state and user intent that persists across turns, enabling iterative refinement where each turn builds on previous work rather than starting fresh.
Unique: Maintains stateful context across turns specifically optimized for code collaboration, remembering design decisions and codebase state without explicit memory structures
vs alternatives: Provides better iterative code refinement than stateless models because it retains context about previous edits and design intent across turns
GLM-5.1 translates natural language specifications into code that preserves semantic intent, handling ambiguous or underspecified requirements by inferring reasonable implementations based on context and common patterns. The model uses semantic understanding of both natural language and code to bridge the gap between high-level intent and low-level implementation details.
Unique: Translates natural language to code with explicit semantic fidelity checking, inferring reasonable implementations for underspecified requirements rather than producing literal or incomplete code
vs alternatives: Handles ambiguous requirements better than Copilot because it uses semantic reasoning to infer intent rather than pattern matching against training data
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