Z.ai: GLM 4.5 ranks higher at 24/100 vs Tencent: Hunyuan A13B Instruct at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Tencent: Hunyuan A13B Instruct | Z.ai: GLM 4.5 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 22/100 | 24/100 |
| Adoption |
| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.40e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 6 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Hunyuan-A13B uses a sparse Mixture-of-Experts (MoE) architecture with 13B active parameters selected from an 80B parameter pool, enabling efficient instruction-following through dynamic expert routing. The model supports explicit chain-of-thought reasoning patterns, allowing it to decompose complex tasks into intermediate reasoning steps before generating final responses. This architecture reduces computational overhead during inference while maintaining reasoning capability through selective expert activation based on input tokens.
Unique: Uses sparse MoE with 13B active parameters from 80B total pool, enabling chain-of-thought reasoning at lower inference cost than dense 70B+ models; Tencent's proprietary expert routing mechanism selects relevant experts per token rather than activating full parameter set
vs alternatives: More parameter-efficient than Llama 2 70B or Mistral 7B for reasoning tasks due to sparse activation, while maintaining instruction-following quality through MoE specialization; trades inference latency variance for lower per-token compute cost
Hunyuan-A13B is instruction-tuned to follow multi-turn conversational patterns, maintaining coherence across sequential user requests within a single session. The model processes each turn as context-aware input, allowing it to reference previous exchanges and adapt responses based on conversation history. This capability enables natural dialogue flows where the model understands implicit references, maintains consistent persona, and refines answers based on user feedback across turns.
Unique: Instruction-tuned specifically for multi-turn dialogue with MoE routing that may specialize certain experts for conversational coherence; Tencent's tuning approach emphasizes maintaining context across turns within the sparse expert framework
vs alternatives: Comparable to GPT-3.5 Turbo for multi-turn dialogue but with lower inference cost due to MoE sparsity; less capable than GPT-4 on complex multi-turn reasoning but more efficient than dense alternatives of similar parameter count
Hunyuan-A13B can generate code snippets and provide technical explanations by leveraging its instruction-tuning and chain-of-thought capability. When prompted with code-related tasks, the model can produce syntactically valid code in multiple languages, explain implementation logic, and reason through algorithmic problems. The MoE architecture may route to specialized experts for code understanding, though this is implementation-dependent and not explicitly documented.
Unique: Combines MoE sparse activation with instruction-tuning for code tasks; may route code-understanding experts selectively, reducing overhead vs dense models while maintaining code quality through specialized expert paths
vs alternatives: More efficient than Codex or GPT-3.5 Turbo for code generation due to sparse activation, but likely less capable than specialized code models like Codestral or GitHub Copilot on complex multi-file refactoring
Hunyuan-A13B is designed to achieve competitive performance on standard instruction-following benchmarks (MMLU, HellaSwag, TruthfulQA, etc.) through instruction-tuning and MoE specialization. The model's architecture allows different experts to specialize in different task domains, enabling strong cross-domain performance without proportional parameter scaling. This capability reflects the model's training on diverse instruction datasets and evaluation against established baselines.
Unique: Achieves competitive benchmark performance through MoE specialization rather than parameter scaling, allowing different experts to optimize for different task types; Tencent's instruction-tuning approach balances performance across diverse benchmarks within the sparse architecture
vs alternatives: Competitive with Llama 2 13B and Mistral 7B on benchmarks while using MoE for efficiency; likely underperforms dense 70B+ models on complex reasoning benchmarks but offers better cost-performance ratio
Hunyuan-A13B is accessible via OpenRouter's API, providing a managed inference endpoint without requiring local deployment or infrastructure management. The integration handles model loading, batching, and scaling transparently, exposing a standard REST API interface for text generation. Developers interact with the model through HTTP requests, specifying parameters like temperature, max tokens, and top-p sampling, with responses streamed or returned in full depending on configuration.
Unique: Accessed exclusively through OpenRouter's managed API rather than direct Tencent endpoints; OpenRouter handles MoE routing and expert selection server-side, abstracting infrastructure complexity from the caller
vs alternatives: Simpler integration than self-hosted Ollama or vLLM but with higher latency and per-token costs; comparable to using OpenAI API but with lower cost-per-token due to MoE efficiency
Hunyuan-A13B supports streaming generation through OpenRouter's API, allowing responses to be consumed token-by-token as they are generated rather than waiting for full completion. This capability enables real-time user feedback, progressive rendering in UIs, and early stopping based on application logic. The model exposes sampling parameters (temperature, top-p, top-k) for fine-grained control over generation behavior, allowing tuning of output diversity and determinism.
Unique: Streaming is implemented at the OpenRouter layer, not model-specific; MoE routing happens server-side, and tokens are streamed to the client as experts generate them, enabling low-latency progressive output
vs alternatives: Streaming capability is standard across modern LLM APIs; Hunyuan's advantage is lower per-token cost due to MoE efficiency, making streaming more economical for high-volume applications
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 Tencent: Hunyuan A13B Instruct at 22/100.
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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