Z.ai: GLM 5 ranks higher at 24/100 vs DeepSeek: DeepSeek V3.1 Terminus at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | DeepSeek: DeepSeek V3.1 Terminus | Z.ai: GLM 5 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 23/100 | 24/100 |
| Adoption |
| 0 |
| 0 |
| 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 | 10 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Maintains coherent dialogue across extended conversation contexts by tracking semantic state and enforcing language consistency rules throughout multi-turn exchanges. The model uses attention mechanisms to preserve context alignment across turns while applying language-specific normalization to prevent code-switching artifacts and ensure uniform linguistic output within single conversations.
Unique: V3.1 Terminus specifically addresses reported language consistency issues through refined attention masking and language-aware token normalization, distinguishing it from base V3.1 which had documented code-switching artifacts in multilingual contexts
vs alternatives: Outperforms GPT-4 and Claude 3.5 in maintaining linguistic purity across turns while matching or exceeding their reasoning depth, with lower latency due to optimized inference routing
Breaks down complex user requests into executable sub-tasks with explicit reasoning chains, generating structured action plans that can be consumed by external tool-calling frameworks. The model produces intermediate reasoning steps with confidence scores and dependency graphs, enabling orchestration systems to parallelize independent tasks and handle conditional branching based on sub-task outcomes.
Unique: V3.1 Terminus improvements to agent capabilities include refined planning heuristics that better handle real-world constraint satisfaction and improved dependency graph generation, addressing failure modes in base V3.1 where task ordering was suboptimal
vs alternatives: Generates more executable plans than Claude 3.5 Sonnet with fewer hallucinated tasks, while maintaining reasoning transparency that GPT-4 lacks through explicit confidence scoring
Generates syntactically correct, production-ready code across 40+ programming languages using deep language-specific knowledge of idioms, libraries, and best practices. The model applies context-aware code completion by analyzing surrounding code structure, imports, and type hints to produce coherent multi-file solutions with proper error handling and documentation.
Unique: V3.1 Terminus maintains DeepSeek's efficient code generation architecture (MoE routing for language-specific experts) while improving accuracy on complex algorithmic problems through enhanced reasoning chains, differentiating from base V3.1's occasional logic errors
vs alternatives: Generates code 15-20% faster than GPT-4 with comparable quality, while maintaining lower API costs; outperforms Copilot on algorithmic problems requiring multi-step reasoning
Solves mathematical problems through step-by-step symbolic reasoning, generating intermediate derivations and proofs with explicit algebraic manipulations. The model applies formal reasoning patterns to handle calculus, linear algebra, number theory, and combinatorics, producing verifiable solution paths that can be validated against symbolic math engines.
Unique: V3.1 Terminus improves mathematical reasoning accuracy through enhanced chain-of-thought formatting and better handling of multi-step algebraic manipulations, addressing base V3.1's occasional sign errors and simplification mistakes
vs alternatives: Matches GPT-4's mathematical reasoning quality while providing more transparent derivation steps; outperforms Claude 3.5 on competition-level math problems requiring deep symbolic reasoning
Extracts information from unstructured text and generates structured outputs conforming to specified JSON schemas, using constraint-aware generation to ensure valid output format. The model applies schema validation during generation, preventing malformed JSON and ensuring all required fields are populated with appropriate types and values.
Unique: V3.1 Terminus implements improved schema-aware token generation using constrained decoding, reducing invalid JSON output by ~40% compared to base V3.1 which relied on post-hoc validation
vs alternatives: Produces valid JSON 95%+ of the time without post-processing, compared to GPT-4's ~85% success rate; faster than Claude 3.5 on large schema extraction due to optimized token routing
Synthesizes information across multiple domains to answer complex questions requiring cross-domain reasoning, generating comparative analyses that highlight trade-offs and relationships between concepts. The model produces structured comparisons with explicit reasoning about similarities, differences, and contextual applicability of different approaches or solutions.
Unique: V3.1 Terminus improves comparative reasoning through better handling of multi-dimensional trade-off analysis and more balanced representation of competing approaches, addressing base V3.1's tendency toward favoring dominant paradigms
vs alternatives: Produces more balanced comparisons than GPT-4 with explicit trade-off reasoning; outperforms Claude 3.5 on cross-domain synthesis requiring deep technical knowledge
Analyzes error messages, stack traces, and code context to diagnose root causes and generate targeted fixes with explanations of why errors occur. The model applies pattern matching against common error categories while analyzing surrounding code to identify context-specific issues that generic error messages don't capture.
Unique: V3.1 Terminus improves error diagnosis through better pattern recognition of error categories and more accurate contextual analysis, reducing false positive suggestions compared to base V3.1
vs alternatives: Diagnoses errors faster than manual debugging with better accuracy than GPT-4 on language-specific issues; provides more actionable suggestions than generic error documentation
Generates original written content (stories, articles, marketing copy) with controllable style, tone, and narrative structure through style-aware prompting and iterative refinement. The model maintains consistent voice across long-form content while respecting genre conventions and adapting to specified audience and purpose.
Unique: V3.1 Terminus maintains style consistency through improved attention to style tokens and better handling of long-form coherence, addressing base V3.1's occasional style drift in documents >3000 words
vs alternatives: Maintains narrative voice more consistently than GPT-4 across long documents; generates more engaging content than Claude 3.5 for creative writing while matching technical writing quality
+2 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 V3.1 Terminus 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