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| Pricing | Paid | Paid |
| Starting Price | $4.35e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 5 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
DeepSeek V4 Pro utilizes a Mixture-of-Experts architecture that activates a subset of its 1.6 trillion parameters based on the input context, allowing it to efficiently handle a context window of up to 1 million tokens. This design enables the model to perform complex reasoning tasks by dynamically selecting the most relevant experts for the given input, optimizing both performance and resource usage. The architecture is distinct in its ability to scale reasoning capabilities without a linear increase in computational cost.
Unique: The Mixture-of-Experts architecture allows for selective activation of parameters, making it uniquely efficient in processing extensive contexts without overwhelming resource demands.
vs alternatives: More efficient than traditional dense models like GPT-4 in handling long contexts due to its expert selection mechanism.
DeepSeek V4 Pro is capable of generating code snippets based on extensive contextual understanding, leveraging its 1 million token context window to maintain coherence across multiple code blocks. It applies advanced natural language processing techniques to interpret user intent and generate relevant code, while the Mixture-of-Experts model ensures that only the most pertinent parameters are activated for coding tasks, enhancing accuracy and relevance.
Unique: The model's ability to maintain context across extensive code generation tasks sets it apart, allowing for more coherent and contextually relevant outputs.
vs alternatives: Generates more contextually aware code than traditional models like Copilot due to its extensive token handling.
DeepSeek V4 Pro supports multi-turn conversations by maintaining state across interactions, enabled by its large context window. This allows the model to remember previous exchanges and respond in a way that feels natural and coherent. The architecture is designed to dynamically adjust its responses based on the evolving context of the conversation, making it suitable for applications requiring ongoing dialogue.
Unique: The ability to maintain context over long conversations without losing coherence is a key differentiator, enabled by the model's architecture.
vs alternatives: Offers better context retention than many chatbots, which typically struggle with multi-turn dialogue.
DeepSeek V4 Pro can adapt its output style and content based on user-defined parameters, such as tone, formality, or specific jargon. This is achieved through a combination of prompt engineering and the model's inherent understanding of language nuances, allowing it to tailor responses to fit various contexts and audiences. The architecture supports this flexibility by utilizing its extensive parameter set to adjust outputs dynamically.
Unique: The model's ability to dynamically adjust its output style based on user-defined parameters is a significant advantage over static models.
vs alternatives: More adaptable than traditional models, which often produce generic outputs without customization.
DeepSeek V4 Pro excels at summarizing large bodies of text by leveraging its extensive context window to capture key points and themes. It employs advanced NLP techniques to identify and distill the most relevant information, ensuring that summaries are both concise and informative. The Mixture-of-Experts architecture allows it to efficiently process and summarize lengthy documents without losing critical context.
Unique: The model's ability to maintain context over long texts for summarization is a key differentiator, enabling more accurate and relevant summaries.
vs alternatives: Produces more coherent summaries than many competing models, which often lose context in longer texts.
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 DeepSeek: DeepSeek V4 Pro at 21/100.
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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
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