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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $2.70e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Implements DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that selectively attends to relevant tokens rather than computing full quadratic attention across all positions. This reduces computational complexity from O(n²) to approximately O(n log n) while maintaining reasoning quality, enabling efficient processing of longer contexts without proportional memory overhead. The sparse pattern is learned during training and dynamically applied based on token importance scoring.
Unique: DeepSeek Sparse Attention (DSA) uses learned, fine-grained token importance scoring during training to create task-adaptive sparse patterns, rather than fixed sparsity strategies (e.g., local windows or strided patterns) used by competitors. This enables selective attention to semantically relevant tokens across the full sequence.
vs alternatives: Achieves longer effective context windows than Claude 3.5 Sonnet (200K) with lower inference latency due to sparse computation, while maintaining reasoning quality comparable to dense attention models at shorter contexts.
Maintains conversation state across multiple turns, tracking context, user intent, and reasoning chains within a single session. The model processes each turn by incorporating full conversation history, enabling coherent follow-up questions, clarifications, and iterative refinement of responses. State is managed client-side via message arrays passed to the API, with the model internally managing attention over the conversation history using the sparse attention mechanism.
Unique: Combines sparse attention over conversation history with full-sequence reasoning, allowing the model to selectively focus on relevant prior turns rather than equally weighting all history. This reduces noise from early conversation turns while maintaining coherence.
vs alternatives: Handles longer conversation histories (100+ turns) more efficiently than GPT-4 due to sparse attention, reducing per-turn latency and token costs while maintaining context awareness comparable to dense-attention models.
Generates syntactically correct, executable code across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) with reasoning about algorithmic correctness, performance characteristics, and edge cases. The model applies sparse attention to understand full codebase context when provided, enabling generation of code that integrates with existing patterns. Outputs include inline comments, type hints, and error handling appropriate to the target language.
Unique: Uses sparse attention to maintain awareness of full codebase context (imports, class definitions, function signatures) when generating code, enabling generation that respects existing architectural patterns rather than generating in isolation. Sparse patterns learned during training prioritize syntactically relevant tokens (keywords, brackets, indentation).
vs alternatives: Generates code with better architectural coherence than Copilot for large codebases (10K+ lines) due to sparse attention over full context, while maintaining latency comparable to GPT-4 Turbo due to reduced computational overhead.
Performs step-by-step mathematical reasoning including algebraic manipulation, calculus, linear algebra, and logical proofs. The model generates intermediate reasoning steps (chain-of-thought), showing work for complex calculations and deriving conclusions from mathematical premises. Sparse attention enables tracking of long derivations by selectively attending to relevant prior steps rather than all previous tokens.
Unique: Sparse attention over derivation steps allows the model to maintain coherence across long mathematical proofs by selectively attending to relevant prior equations and definitions, rather than treating all previous tokens equally. This enables more accurate multi-step reasoning than dense attention on very long derivations.
vs alternatives: Produces more detailed mathematical reasoning than GPT-4 for complex multi-step problems due to sparse attention enabling longer reasoning chains without context loss, though still lacks symbolic computation capabilities of specialized math engines.
Synthesizes information from long documents or multiple sources into coherent summaries, key insights, and structured knowledge representations. The model uses sparse attention to identify and extract relevant information from lengthy inputs without processing every token equally, enabling efficient summarization of documents up to 100K+ tokens. Outputs include abstractive summaries, bullet-point key findings, and structured data extraction (tables, JSON).
Unique: Sparse attention patterns learned during training prioritize sentences and sections with high information density, enabling the model to extract key insights from 100K+ token documents without proportional computational cost. Sparse patterns adapt to document structure (headings, sections) rather than treating all tokens equally.
vs alternatives: Summarizes documents 2-3x longer than Claude 3.5 Sonnet's practical context limit with lower latency due to sparse computation, while maintaining summary quality comparable to dense-attention models on shorter documents.
Follows complex, multi-step instructions and decomposes ambiguous tasks into concrete subtasks with clear execution plans. The model interprets user intent from natural language instructions, identifies missing information, and generates step-by-step action plans. Sparse attention enables tracking of long instruction sequences by selectively attending to relevant prior steps and constraints.
Unique: Sparse attention over instruction sequences allows the model to maintain awareness of constraints and dependencies across long task descriptions without equal weighting of all tokens. Sparse patterns prioritize constraint keywords and task boundaries identified during training.
vs alternatives: Decomposes complex tasks with longer instruction contexts (50K+ tokens) more accurately than GPT-4 due to sparse attention reducing noise from verbose context, while maintaining planning quality comparable to dense-attention models on typical task lengths.
Generates original creative content including stories, poetry, marketing copy, and dialogue with coherent narrative structure, character consistency, and stylistic variation. The model maintains narrative context across long passages using sparse attention, enabling generation of novel-length content without losing plot coherence. Outputs respect specified tone, genre, and structural constraints.
Unique: Sparse attention patterns learned on narrative data prioritize plot-relevant tokens (character names, key events, emotional beats) over filler text, enabling the model to maintain narrative coherence across longer passages than dense-attention models while using less computation.
vs alternatives: Generates longer coherent narratives (10K+ tokens) with better plot consistency than GPT-4 due to sparse attention reducing noise from verbose descriptions, while maintaining creative quality comparable to dense-attention models on typical story lengths.
Translates text between 50+ languages with context-aware semantic accuracy, preserving tone, idioms, and cultural nuances. The model performs cross-lingual reasoning by understanding concepts across languages and generating responses in target languages. Sparse attention enables efficient processing of long multilingual documents by selectively attending to language-relevant tokens rather than processing all tokens equally.
Unique: Sparse attention patterns adapt to language-specific token distributions, enabling efficient processing of morphologically rich languages (German, Finnish) and languages with different token boundaries (Chinese, Japanese) without proportional computational overhead.
vs alternatives: Translates longer documents (100K+ tokens) more efficiently than Google Translate API with comparable semantic accuracy, while maintaining context awareness across language boundaries better than phrase-based translation systems.
+1 more capabilities
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 DeepSeek: DeepSeek V3.2 Exp at 23/100.
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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