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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.10e-6 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 5 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Implements OpenAI's chain-of-thought reasoning architecture with high reasoning_effort setting, allocating extended computational budget to internal reasoning steps before generating responses. The model performs multi-step logical decomposition for STEM problems, explicitly working through intermediate reasoning states rather than direct answer generation. This is achieved through a configurable reasoning effort parameter that controls the depth and duration of the internal reasoning process.
Unique: Implements configurable reasoning effort levels (low/medium/high) that directly control internal computation budget allocation, allowing developers to trade latency and cost for reasoning depth — a design pattern distinct from fixed-capacity reasoning models. The high setting specifically optimizes for STEM domains through domain-specific reasoning token allocation.
vs alternatives: Outperforms GPT-4o and Claude 3.5 Sonnet on STEM benchmarks while maintaining lower cost than o3-full, making it the optimal choice for cost-sensitive STEM applications requiring extended reasoning.
Provides REST API access to the o3-mini-high model through OpenAI's standard chat completion endpoint, supporting both streaming and non-streaming response modes. Requests are authenticated via API key and transmitted over HTTPS, with responses formatted as JSON containing token usage metadata, finish reasons, and generated text. The streaming variant uses server-sent events (SSE) to deliver tokens incrementally, enabling real-time response rendering in client applications.
Unique: Integrates reasoning_effort parameter directly into standard OpenAI chat completion API without requiring separate endpoints or model variants, allowing developers to dynamically adjust reasoning depth per-request while maintaining API compatibility with existing OpenAI integrations.
vs alternatives: Maintains full backward compatibility with existing OpenAI API code while adding reasoning capabilities, eliminating migration friction compared to switching to entirely different model providers or architectures.
Balances computational cost and reasoning capability through the o3-mini architecture, which uses fewer parameters and optimized inference than o3-full while maintaining extended reasoning for STEM tasks. The high reasoning_effort setting allocates extended computation specifically to STEM reasoning patterns rather than general language understanding, reducing wasted computation on non-STEM queries. Cost is further optimized through selective reasoning — developers can use lower reasoning_effort settings for simpler queries and reserve high effort for complex problems.
Unique: Implements domain-specific parameter optimization where reasoning_effort is tuned for STEM tasks specifically, reducing computational overhead compared to general-purpose reasoning models that allocate equal reasoning budget across all domains. The o3-mini architecture itself is smaller than o3-full, enabling lower base inference costs.
vs alternatives: Provides 60-70% cost reduction vs o3-full for STEM tasks while maintaining comparable reasoning quality, making it the most cost-efficient extended-reasoning model for educational and scientific applications.
Supports multi-turn conversation history where each turn can leverage extended reasoning, maintaining conversation context across multiple exchanges. The model processes the full message history (system prompt + all previous user/assistant messages) before applying reasoning_effort to generate the next response. This enables interactive problem-solving sessions where users can ask follow-up questions, request clarifications, or build on previous reasoning steps without losing context.
Unique: Applies reasoning_effort parameter to the full conversation context rather than isolated queries, enabling reasoning to leverage previous problem-solving steps and user clarifications. This differs from stateless reasoning models that treat each request independently.
vs alternatives: Enables more natural interactive problem-solving compared to single-turn reasoning models, as users can iteratively refine solutions without losing reasoning context, though at the cost of higher per-turn token consumption.
Supports JSON mode and schema-based output constraints through OpenAI's structured output API, allowing developers to specify a JSON schema that the model must adhere to when generating responses. The model generates valid JSON that conforms to the provided schema, with built-in validation ensuring the output matches the specified structure, types, and constraints. This is particularly useful for STEM applications where structured data extraction (equations, solutions, step-by-step breakdowns) is required.
Unique: Integrates JSON schema validation directly into the reasoning loop, ensuring that extended reasoning outputs conform to specified structures without post-processing or validation layers. This differs from models that generate free-form text requiring external parsing.
vs alternatives: Eliminates the need for post-generation parsing and validation, reducing latency and error rates compared to extracting structured data from unstructured reasoning outputs.
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 OpenAI: o3 Mini High at 20/100.
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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