| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $3.00e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 7 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates text using a 671B-parameter mixture-of-experts architecture assembled from three DeepSeek checkpoints (R1-0528, R1, V3-0324) via Assembly-of-Experts merge technique. Routes input tokens through sparse expert networks where only a subset of parameters activate per token, reducing computational cost while maintaining model capacity. The merge combines reasoning-optimized (R1) and instruction-following (V3) checkpoints to balance chain-of-thought depth with practical task performance.
Unique: Assembly-of-Experts merge combining R1 reasoning checkpoints with V3 instruction-tuning across 671B parameters, creating a hybrid that preserves chain-of-thought capability while maintaining practical task performance — distinct from single-checkpoint models or simple ensemble averaging
vs alternatives: Offers reasoning-grade model performance with MoE efficiency gains (sparse activation) at lower per-token cost than dense 671B models, while merged checkpoints provide better instruction-following than pure R1 reasoning models
Generates intermediate reasoning steps and explicit thinking traces before producing final answers, leveraging the R1 checkpoint components in the merged model. The model learns to decompose complex problems into substeps, showing work for mathematical reasoning, logical deduction, and multi-stage problem solving. This capability is inherited from DeepSeek-R1's training on reasoning-focused datasets and is preserved through the Assembly-of-Experts merge.
Unique: Preserves R1 checkpoint's chain-of-thought training through Assembly-of-Experts merge, maintaining reasoning trace generation capability while adding V3's instruction-following — unlike pure R1 models that may be less responsive to task-specific instructions, or V3-only models that lack explicit reasoning traces
vs alternatives: Provides transparent reasoning traces comparable to OpenAI o1 but with lower per-token cost via MoE efficiency, while maintaining better instruction-following than pure reasoning models
Generates, completes, and analyzes code across multiple programming languages by leveraging training on diverse code repositories and instruction-tuning from the V3 checkpoint. The model understands code structure, syntax, and semantics for languages including Python, JavaScript, Java, C++, Go, Rust, and others. Supports code generation from natural language descriptions, code completion, refactoring suggestions, and bug analysis through token-level understanding of programming constructs.
Unique: Combines R1's reasoning capability for complex algorithmic problems with V3's instruction-tuned code generation, enabling both step-by-step algorithm explanation and practical code output — unlike pure reasoning models that may struggle with syntax, or code-only models that lack algorithmic reasoning
vs alternatives: Offers reasoning-aware code generation (explaining algorithm choices) with MoE efficiency, providing better algorithmic depth than GitHub Copilot while maintaining practical instruction-following
Follows complex, multi-part instructions and adapts behavior to task-specific requirements through training on the V3-0324 checkpoint, which emphasizes instruction-tuning and alignment. The model interprets nuanced directives about output format, tone, style, and constraints, and maintains consistency across multi-turn conversations. This capability enables the model to function as a specialized assistant for domain-specific tasks without requiring fine-tuning.
Unique: V3 checkpoint's instruction-tuning combined with R1's reasoning creates models that both follow complex directives precisely AND explain their reasoning for task-specific decisions — unlike instruction-only models that may lack reasoning depth, or reasoning-only models that may ignore formatting requirements
vs alternatives: Provides instruction-following quality comparable to GPT-4 with added reasoning transparency, while MoE architecture reduces per-token cost compared to dense instruction-tuned models of equivalent capability
Maintains conversation history and context across multiple turns within a single API session, enabling coherent multi-turn dialogue where the model references previous messages and builds on prior context. The model tracks conversation state, understands pronouns and references to earlier statements, and adapts responses based on accumulated context. This is implemented through standard transformer attention mechanisms that process the full conversation history as input tokens.
Unique: Merged checkpoint approach preserves both R1's reasoning consistency across turns and V3's instruction-following, enabling conversations that maintain logical coherence while adapting to user-specified conversation styles or constraints
vs alternatives: Provides multi-turn conversation capability with reasoning transparency (showing why model made contextual decisions), while MoE efficiency reduces per-turn cost compared to dense models for long conversations
Solves mathematical problems including algebra, calculus, statistics, and symbolic reasoning through training on mathematical datasets and R1 checkpoint's reasoning capability. The model can work through multi-step mathematical proofs, show intermediate calculations, and explain mathematical concepts. It understands mathematical notation, can parse equations, and applies appropriate mathematical techniques to problem categories.
Unique: R1 checkpoint's training on mathematical reasoning datasets combined with V3's instruction clarity enables both deep mathematical reasoning AND clear explanation of solutions — unlike pure reasoning models that may show work but lack pedagogical clarity, or instruction models that may lack mathematical depth
vs alternatives: Provides reasoning-grade mathematical problem solving with explicit step-by-step explanations, offering better transparency than black-box calculators while maintaining practical instruction-following for educational contexts
Provides text generation through OpenRouter's REST API with support for streaming responses (server-sent events) and batch processing. Requests are routed through OpenRouter's infrastructure, which handles load balancing, rate limiting, and provider selection. Streaming enables real-time token delivery for interactive applications, while batch processing allows asynchronous processing of multiple requests with optimized throughput. The API accepts standard OpenAI-compatible request formats.
Unique: OpenRouter's unified API abstracts away provider-specific implementation details while maintaining OpenAI API compatibility, enabling applications to switch between DeepSeek and other models without code changes — unlike direct provider APIs that require model-specific client libraries
vs alternatives: Provides managed inference with automatic load balancing and provider failover, reducing operational overhead compared to self-hosted deployment while maintaining lower per-token cost than direct OpenAI API access
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 TNG: DeepSeek R1T2 Chimera at 22/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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