Z.ai: GLM 5.1 ranks higher at 24/100 vs DeepSeek: R1 Distill Qwen 32B at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | DeepSeek: R1 Distill Qwen 32B | Z.ai: GLM 5.1 |
|---|---|---|
| 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 | $2.90e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements DeepSeek R1's chain-of-thought reasoning capability distilled into a 32B parameter model, enabling step-by-step problem decomposition and multi-step logical inference without the computational overhead of the full R1 model. Uses knowledge distillation from R1's reasoning outputs to train Qwen 2.5 32B, allowing the model to produce explicit reasoning traces before final answers while maintaining inference efficiency suitable for production deployments.
Unique: Uses knowledge distillation to compress DeepSeek R1's reasoning capability into a 32B model, enabling explicit chain-of-thought reasoning at 1/3 the parameter count of full R1 while maintaining reasoning quality through supervised fine-tuning on R1 outputs
vs alternatives: Outperforms o1-mini on benchmarks while being 3-4x smaller and more cost-effective, with transparent reasoning traces unlike closed-source reasoning models
Leverages Qwen 2.5 32B's broad training corpus combined with R1 distillation to synthesize knowledge across mathematics, coding, science, and humanities domains. The model applies reasoning patterns learned from R1 to diverse problem types, using attention mechanisms trained on multi-domain reasoning examples to identify relevant knowledge and apply appropriate solution strategies.
Unique: Combines Qwen 2.5's broad multi-domain pretraining with R1's reasoning distillation, creating a model that applies consistent reasoning patterns across mathematics, code, science, and humanities without domain-specific adaptation
vs alternatives: Broader domain coverage than specialized reasoning models while maintaining reasoning quality comparable to o1-mini, making it more versatile for general-purpose applications
Generates and analyzes code by applying chain-of-thought reasoning to understand requirements, decompose problems into functions, and verify correctness. The model produces intermediate reasoning steps explaining algorithm choice, edge cases, and implementation strategy before generating final code, enabling developers to understand the reasoning behind generated solutions.
Unique: Applies explicit chain-of-thought reasoning to code generation, producing intermediate steps that explain algorithm selection, complexity analysis, and edge case handling before generating final code
vs alternatives: More transparent than Copilot for understanding code generation decisions, with reasoning traces that help developers learn why specific solutions were chosen
Solves mathematical problems by generating explicit step-by-step derivations, using the distilled reasoning capability to break down complex calculations into intermediate steps. The model applies symbolic reasoning patterns learned from R1 to handle algebra, calculus, probability, and discrete mathematics, with each step justified and verifiable.
Unique: Distills R1's mathematical reasoning capability to generate complete step-by-step derivations with intermediate justifications, making mathematical problem-solving transparent and verifiable
vs alternatives: Provides more detailed reasoning than standard LLMs and more cost-effective reasoning than o1-mini while maintaining educational value through explicit derivation steps
Processes documents up to 128K tokens while maintaining reasoning capability, enabling analysis of entire codebases, research papers, or legal documents with chain-of-thought reasoning applied to the full context. The model uses efficient attention mechanisms to handle long sequences without losing reasoning quality, allowing comprehensive analysis without context truncation.
Unique: Maintains chain-of-thought reasoning quality across 128K token context window using efficient attention patterns, enabling reasoning over entire documents without context truncation or quality degradation
vs alternatives: Larger context window than most reasoning models while preserving reasoning capability, making it suitable for comprehensive document analysis that would require chunking with other models
Maintains reasoning capability across multi-turn conversations by preserving context and applying chain-of-thought reasoning to each turn while building on previous reasoning steps. The model tracks conversation state and applies reasoning patterns consistently across turns, enabling iterative problem-solving and refinement.
Unique: Applies consistent chain-of-thought reasoning across multi-turn conversations while preserving context, enabling iterative problem-solving where each turn builds on previous reasoning
vs alternatives: Maintains reasoning quality across conversation turns better than standard LLMs, though with higher token cost than non-reasoning models
Achieves performance parity or superiority to OpenAI's o1-mini on standardized benchmarks (AIME, MATH, coding competitions) through knowledge distillation from R1, while operating at 32B parameters instead of o1-mini's larger size. The model is optimized for benchmark tasks through supervised fine-tuning on R1 outputs, enabling strong performance on structured reasoning problems.
Unique: Distilled to achieve o1-mini-competitive benchmark performance at 32B parameters through supervised fine-tuning on R1 outputs, enabling cost-effective reasoning without full R1 model size
vs alternatives: Matches o1-mini benchmark performance while being significantly smaller and more cost-effective, making it suitable for production deployments where o1-mini cost is prohibitive
Transfers reasoning capability from the larger DeepSeek R1 model to the 32B Qwen 2.5 base through knowledge distillation, where the model learns to mimic R1's reasoning patterns and outputs. This approach preserves R1's reasoning quality while reducing parameter count and inference cost, using supervised fine-tuning on R1-generated reasoning traces as training signal.
Unique: Uses knowledge distillation to transfer R1's reasoning capability to a 32B model, enabling R1-quality reasoning at 1/3 parameter count through supervised fine-tuning on R1 outputs
vs alternatives: More efficient than full R1 while maintaining reasoning quality, and more transparent than black-box reasoning models like o1 through explicit reasoning traces
GLM-5.1 executes multi-step coding tasks over extended timeframes without requiring human intervention between steps, using an internal planning mechanism that decomposes complex objectives into sub-tasks and maintains execution state across sequential operations. Unlike minute-level interaction models that require prompting after each step, this capability enables the model to autonomously navigate decision trees, handle errors, and adapt strategy based on intermediate results without context resets.
Unique: Designed specifically for minute+ autonomous execution windows rather than single-turn interactions; maintains internal execution state and decision-making across extended task horizons without requiring external orchestration or re-prompting between steps
vs alternatives: Outperforms GPT-4 and Claude for long-horizon coding tasks because it's architected for continuous autonomous operation rather than stateless request-response cycles
GLM-5.1 generates and refactors code with awareness of the full codebase structure, dependencies, and patterns, using semantic understanding of how changes in one file propagate to others. The model analyzes import graphs, function signatures, and usage patterns across files to ensure generated code maintains consistency and doesn't introduce breaking changes, rather than treating each file in isolation.
Unique: Maintains semantic awareness of codebase structure and cross-file dependencies during generation, enabling it to make coordinated changes across multiple files rather than treating each file independently
vs alternatives: Produces more consistent multi-file refactorings than Copilot or Claude because it reasons about the entire codebase context simultaneously rather than file-by-file
Z.ai: GLM 5.1 scores higher at 24/100 vs DeepSeek: R1 Distill Qwen 32B at 23/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
GLM-5.1 diagnoses errors and bugs by analyzing error messages, stack traces, and code context to identify root causes and suggest fixes. The model correlates error symptoms with likely causes, explains why errors occur, and provides specific debugging steps or code fixes.
Unique: Diagnoses errors by correlating symptoms with root causes using semantic understanding of code and error patterns, providing explanations and fixes rather than just pattern matching
vs alternatives: More effective at diagnosing subtle bugs than search-based solutions because it reasons about code semantics and error causality
GLM-5.1 identifies performance bottlenecks in code and suggests optimizations with specific implementation guidance, analyzing algorithms, data structures, and resource usage to recommend improvements. The model understands performance implications of different approaches and can suggest algorithmic or architectural changes to improve efficiency.
Unique: Suggests optimizations based on algorithmic and architectural analysis rather than just code-level tweaks, understanding performance implications of different approaches
vs alternatives: Provides more meaningful performance guidance than generic LLMs because it understands algorithm complexity and can suggest structural improvements
GLM-5.1 analyzes code for security vulnerabilities including injection attacks, authentication/authorization issues, cryptographic weaknesses, and data exposure risks, providing specific remediation guidance. The model understands common vulnerability patterns and security best practices to identify risks and suggest secure implementations.
Unique: Identifies security vulnerabilities through semantic analysis of code patterns and provides remediation guidance based on security best practices, not just pattern matching against known CVEs
vs alternatives: More effective at finding context-specific security issues than SAST tools because it understands code intent and can suggest secure implementations
GLM-5.1 performs step-by-step reasoning about code behavior by internally simulating or tracing execution paths, allowing it to predict runtime behavior, identify bugs, and explain code logic without requiring actual execution. This capability uses chain-of-thought-like reasoning applied specifically to code semantics, tracking variable state, control flow, and function call sequences to reason about correctness.
Unique: Applies extended reasoning specifically to code semantics and execution paths, enabling it to predict runtime behavior and identify subtle bugs through symbolic execution simulation rather than pattern matching
vs alternatives: More effective at finding subtle logic bugs than GPT-4 because it explicitly traces execution state rather than relying on pattern recognition
GLM-5.1 maintains rich context across multiple conversation turns when working on code, remembering previous edits, design decisions, and constraints without requiring users to re-specify them. The model builds an internal model of the codebase state and user intent that persists across turns, enabling iterative refinement where each turn builds on previous work rather than starting fresh.
Unique: Maintains stateful context across turns specifically optimized for code collaboration, remembering design decisions and codebase state without explicit memory structures
vs alternatives: Provides better iterative code refinement than stateless models because it retains context about previous edits and design intent across turns
GLM-5.1 translates natural language specifications into code that preserves semantic intent, handling ambiguous or underspecified requirements by inferring reasonable implementations based on context and common patterns. The model uses semantic understanding of both natural language and code to bridge the gap between high-level intent and low-level implementation details.
Unique: Translates natural language to code with explicit semantic fidelity checking, inferring reasonable implementations for underspecified requirements rather than producing literal or incomplete code
vs alternatives: Handles ambiguous requirements better than Copilot because it uses semantic reasoning to infer intent rather than pattern matching against training data
+5 more capabilities