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| Pricing | Paid | Paid |
| Starting Price | $9.00e-8 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 7 decomposed | 12 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-5 processes extended code contexts (supporting multi-file projects and large codebases) while maintaining semantic understanding of system architecture through attention mechanisms optimized for code structure. The model uses specialized tokenization for programming languages and maintains coherence across thousands of tokens of code context, enabling generation of complex features that respect existing patterns and dependencies.
Unique: Engineered specifically for complex systems design with attention mechanisms tuned for code structure and architectural patterns, rather than generic language modeling — enables understanding of system-wide dependencies and design constraints across extended contexts
vs alternatives: Outperforms general-purpose models on large-scale programming tasks because it's optimized for architectural coherence and long-horizon code generation rather than treating code as generic text
GLM-5 supports extended reasoning chains for agentic workflows through structured prompt patterns that enable step-by-step decomposition of complex tasks. The model can maintain state across multiple turns, reason about tool outputs, and make decisions about next actions — designed for long-horizon agent loops where the model must plan, execute, observe, and adapt across dozens of steps.
Unique: Explicitly engineered for long-horizon agent workflows with architectural patterns optimized for extended reasoning chains, rather than single-turn tool calling — maintains coherence and decision quality across dozens of reasoning steps
vs alternatives: Better suited for multi-step agentic tasks than general-purpose models because reasoning and tool-use patterns are baked into the training, not bolted on via prompt engineering
Z.ai: GLM 5 scores higher at 24/100 vs NVIDIA: Nemotron 3 Super at 22/100.
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GLM-5 analyzes code for performance bottlenecks and suggests optimization strategies through understanding of algorithmic complexity, memory management, and system-level performance patterns. The model can identify inefficient algorithms, suggest data structure improvements, and recommend caching or parallelization strategies — enabling targeted performance improvements with understanding of trade-offs.
Unique: Understands algorithmic complexity and system-level performance patterns, enabling identification of fundamental bottlenecks and suggestion of targeted optimizations rather than micro-optimizations
vs alternatives: Identifies more fundamental performance issues than profiling tools because it understands algorithmic complexity and can suggest architectural improvements, not just code-level optimizations
GLM-5 generates comprehensive API specifications, including endpoint definitions, request/response schemas, error handling, and usage examples through understanding of API design best practices and REST/GraphQL patterns. The model can produce OpenAPI/Swagger specifications, generate API documentation, and suggest design improvements — enabling rapid API specification and documentation.
Unique: Generates comprehensive API specifications that follow REST/GraphQL best practices and include error handling, authentication, and usage examples — not just endpoint definitions
vs alternatives: Produces more complete and best-practice-aligned API specifications than simple code-to-spec tools because it understands API design patterns and includes comprehensive documentation
GLM-5 generates high-quality technical documentation, design documents, and architectural specifications through training on expert-level technical writing patterns. The model understands domain-specific terminology, maintains consistency across long documents, and can generate structured documentation (API specs, RFC-style documents, architecture decision records) with appropriate technical depth and precision.
Unique: Trained on expert-level technical documentation patterns and domain-specific terminology, enabling generation of publication-ready documentation with appropriate technical depth rather than generic summaries
vs alternatives: Produces more technically precise and domain-aware documentation than general-purpose models because it understands architectural patterns, trade-offs, and expert writing conventions specific to software engineering
GLM-5 breaks down complex, ambiguous problems into structured task hierarchies and implementation plans through chain-of-thought reasoning patterns. The model can identify dependencies, suggest phased approaches, and generate detailed step-by-step plans for tackling large engineering challenges — useful for translating high-level requirements into actionable development roadmaps.
Unique: Optimized for expert-level problem decomposition through training on complex system design patterns and architectural reasoning, enabling generation of sophisticated multi-phase plans rather than simple task lists
vs alternatives: Produces more sophisticated and architecturally-aware plans than general-purpose models because it understands system design patterns, dependency relationships, and phased implementation strategies
GLM-5 analyzes code for quality issues, architectural violations, and design improvements through patterns learned from expert code review practices. The model can identify performance bottlenecks, suggest refactoring opportunities, flag architectural inconsistencies, and provide detailed feedback on code quality — going beyond simple linting to understand design intent and system-wide implications.
Unique: Trained on expert code review patterns and architectural reasoning, enabling detection of design issues and architectural violations rather than just syntax and style problems
vs alternatives: Provides more sophisticated architectural and design feedback than linting tools because it understands system-wide implications and expert design patterns, not just local code quality
GLM-5 translates code between programming languages while preserving semantic meaning and adapting to language-specific idioms. The model understands language-specific patterns, libraries, and best practices, enabling translation that produces idiomatic code rather than mechanical line-by-line conversions — useful for migrating systems across language ecosystems or supporting polyglot architectures.
Unique: Produces idiomatic, language-specific code rather than mechanical translations because it understands language-specific patterns, libraries, and best practices learned from diverse codebases
vs alternatives: Generates more idiomatic and maintainable translations than simple pattern-matching tools because it understands semantic equivalence and language-specific idioms
+4 more capabilities