| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $9.00e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 7 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Nemotron 3 Super uses a hybrid Mamba-Transformer architecture with sparse Mixture of Experts (MoE) routing that activates only 12B of 120B parameters per forward pass. The model employs learned gating mechanisms to route tokens to specialized expert sub-networks, reducing computational cost while maintaining model capacity. This sparse activation pattern is computed dynamically based on input tokens, enabling efficient inference on consumer-grade hardware without quantization.
Unique: Hybrid Mamba-Transformer architecture with sparse MoE routing activates only 10% of parameters (12B/120B) per token, combining Mamba's linear-time sequence modeling with Transformer's attention capabilities for efficient multi-agent reasoning without quantization
vs alternatives: More parameter-efficient than dense 70B models (Llama 2 70B, Mistral 7x8B) while maintaining 120B-equivalent capacity, and avoids quantization overhead that degrades reasoning in smaller quantized models
Nemotron 3 Super is optimized for multi-agent applications where multiple specialized agents coordinate to solve complex tasks. The model maintains coherent context across extended conversations, tracking agent roles, responsibilities, and shared state. The architecture supports deep reasoning chains where agents build on each other's outputs, with the sparse MoE design ensuring each agent's specialized reasoning path activates relevant experts without full model overhead.
Unique: Optimized specifically for multi-agent applications where sparse MoE routing allows different agents to activate specialized reasoning paths, reducing redundant computation compared to dense models that process all agent reasoning through identical parameter sets
vs alternatives: Better suited for multi-agent coordination than GPT-4 (closed-source, higher cost) or Llama 2 70B (dense, less efficient for specialized agent reasoning paths)
Nemotron 3 Super generates code across multiple programming languages and can understand multi-file codebases for refactoring tasks. The model uses its extended context window and reasoning capabilities to track dependencies between files, suggest structural improvements, and generate coherent changes across a codebase. The sparse MoE architecture allows code-specific experts to activate for syntax-aware generation while general reasoning experts handle architectural decisions.
Unique: Sparse MoE design allows language-specific experts to activate for syntax-aware generation while architectural reasoning experts handle cross-file dependencies, avoiding the overhead of processing all code through identical dense parameters
vs alternatives: More efficient than Copilot for multi-file refactoring due to sparse activation, and open-weight model allows fine-tuning for domain-specific code patterns unlike proprietary alternatives
Nemotron 3 Super excels at breaking down complex problems into reasoning steps, generating explicit intermediate reasoning before final answers. The model can produce detailed chain-of-thought traces for mathematical problems, logical reasoning, and multi-step planning tasks. The hybrid Mamba-Transformer architecture provides both efficient sequence modeling (Mamba) and attention-based reasoning (Transformer), enabling coherent multi-step reasoning without excessive parameter activation.
Unique: Hybrid Mamba-Transformer allows efficient generation of long reasoning chains without activating full 120B parameters; Mamba's linear-time complexity prevents reasoning traces from becoming prohibitively expensive compared to dense models
vs alternatives: More efficient reasoning than GPT-4 for chain-of-thought tasks due to sparse activation, and open-weight design allows inspection and fine-tuning of reasoning patterns unlike closed-source models
Nemotron 3 Super is accessed exclusively through OpenRouter's API, supporting both streaming (token-by-token) and batch inference modes. The API abstracts away the underlying sparse MoE complexity, presenting a standard LLM interface. Streaming enables real-time response generation for interactive applications, while batch processing allows cost-optimized throughput for non-latency-sensitive workloads. The sparse activation is handled transparently by the inference backend.
Unique: OpenRouter integration abstracts sparse MoE complexity behind standard LLM API, allowing developers to use Nemotron 3 Super without understanding MoE routing; supports both streaming and batch modes with transparent cost optimization
vs alternatives: More accessible than self-hosted sparse MoE models due to managed API, and cheaper per-token than GPT-4 while maintaining comparable reasoning quality for many tasks
Nemotron 3 Super can process and synthesize information from extended documents, generating summaries, extracting key points, and answering questions about document content. The model's extended context window and efficient sparse activation enable processing of longer documents than typical dense models without excessive latency. The reasoning capabilities allow nuanced synthesis rather than simple extractive summarization.
Unique: Sparse MoE activation allows efficient processing of longer documents than dense models; specialized reasoning experts activate for synthesis tasks while general language experts handle document understanding, reducing redundant computation
vs alternatives: More efficient than Llama 2 70B for document summarization due to sparse activation, and open-weight design allows fine-tuning for domain-specific summarization unlike GPT-4
Nemotron 3 Super is trained to follow detailed instructions and adapt behavior based on system prompts and task specifications. The model can adjust tone, style, output format, and reasoning approach based on explicit instructions. This capability enables single-model deployment across diverse applications without model switching. The sparse MoE design allows task-specific experts to activate based on instruction content, improving efficiency for specialized tasks.
Unique: Sparse MoE routing allows task-specific experts to activate based on instruction content, enabling efficient adaptation to diverse tasks without full model re-computation; instruction-following is optimized through training on diverse task distributions
vs alternatives: More instruction-following consistency than Llama 2 70B, and open-weight design allows fine-tuning for domain-specific instruction patterns unlike proprietary models
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 NVIDIA: Nemotron 3 Super 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