Z.ai: GLM 5.1 ranks higher at 24/100 vs OpenAI: GPT-4 Turbo (older v1106) at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | OpenAI: GPT-4 Turbo (older v1106) | 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 | $1.00e-5 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Processes both text and image inputs simultaneously within a single inference pass, using a unified transformer architecture that encodes visual tokens alongside text embeddings. The model applies attention mechanisms across both modalities, enabling it to reason about image content, answer questions about visual elements, and generate text responses grounded in visual context. Vision inputs are converted to image tokens through a learned visual encoder before being fed into the main language model backbone.
Unique: Unified transformer architecture that treats image tokens and text tokens with equal priority in attention computation, rather than using separate vision encoders with late fusion. This enables deeper cross-modal reasoning where visual and textual information influence each other throughout all transformer layers.
vs alternatives: Outperforms Claude 3 Opus and Gemini Pro Vision on complex visual reasoning tasks requiring multi-step inference, particularly for technical diagrams and document analysis, due to larger model scale (1.3T parameters) and longer training on vision-language data.
Constrains model output to valid JSON matching a developer-provided schema, using a decoding-time constraint mechanism that prevents invalid JSON generation at the token level. The model's output is validated against the schema before being returned, ensuring type correctness, required field presence, and enum constraints. This works by modifying the sampling distribution at each token position to only allow tokens that keep the output valid JSON.
Unique: Implements constraint-based decoding at inference time using a modified sampling algorithm that prunes invalid tokens before probability distribution, rather than post-hoc validation. This guarantees valid JSON output on first generation without retry loops, and works across all model sizes.
vs alternatives: More reliable than Anthropic's structured output (which uses prompt engineering) and faster than Claude's approach because constraints are enforced at the token level rather than through post-generation validation or probabilistic guidance.
Accepts a list of tool/function definitions with parameters, and the model learns to emit structured function calls in response to user queries. The model outputs function names and arguments as JSON, which the developer's application then executes and feeds back to the model for continued reasoning. This enables agentic workflows where the model decides which tools to invoke, in what order, and how to interpret results. The model is trained to understand function signatures, parameter types, and return values.
Unique: Supports parallel function calling (multiple tools invoked in a single model output) and vision-compatible function calling (can call tools based on image analysis), unlike earlier GPT-4 versions. Uses a unified token vocabulary for both text generation and function call syntax, enabling seamless switching between modes.
vs alternatives: More flexible than Claude's tool use because it supports arbitrary JSON parameter types and parallel invocation, and more reliable than Gemini's function calling due to larger training dataset on tool-use patterns and better parameter type understanding.
Processes input sequences up to 128,000 tokens (approximately 96,000 words or 400+ pages of text) in a single request, enabling the model to maintain coherent reasoning across very long documents, codebases, or conversation histories. The model uses a modified attention mechanism (likely sparse or hierarchical attention) to handle the extended context efficiently without quadratic memory scaling. This allows developers to pass entire books, code repositories, or long conversation threads without truncation.
Unique: Achieves 128K context window using a combination of grouped-query attention (reducing KV cache size) and optimized position embeddings that extrapolate beyond training length. This is 4x larger than Claude 3 Opus (200K) but with better latency characteristics due to architectural efficiency.
vs alternatives: Faster inference on 128K contexts than Claude 3 Opus due to grouped-query attention reducing memory bandwidth, though Claude's 200K window is larger; better for real-time applications requiring long context, worse for absolute maximum context capacity.
Interprets natural language instructions and system prompts to adapt behavior without fine-tuning, using in-context learning to understand task specifications from examples (few-shot) or descriptions (zero-shot). The model's training includes extensive instruction-following data, enabling it to understand complex, multi-step tasks described in plain English and execute them consistently. This works through the model's learned ability to parse instructions, extract intent, and apply that intent to new inputs.
Unique: Trained on a diverse set of instruction-following tasks using RLHF (reinforcement learning from human feedback), enabling it to understand implicit instructions and adapt to novel task descriptions. The model learns to parse instructions compositionally, combining multiple constraints (tone, format, length) in a single response.
vs alternatives: More reliable instruction-following than GPT-3.5 due to larger scale and RLHF training; comparable to Claude 3 Opus but with better performance on technical instructions and code-related tasks due to larger training dataset on programming content.
Generates syntactically correct code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) based on natural language descriptions, comments, or partial code. The model understands language-specific idioms, standard libraries, and best practices for each language. Code generation works through transformer-based sequence-to-sequence prediction, where the model learns patterns from billions of tokens of code in its training data and predicts the most likely next tokens that form valid code.
Unique: Trained on a curated, high-quality subset of public code repositories with deduplication and filtering for correctness, rather than all available code. This results in better adherence to best practices and fewer security anti-patterns compared to models trained on raw GitHub data.
vs alternatives: Outperforms GitHub Copilot on code generation from natural language descriptions due to larger model size and instruction-following training; comparable to Claude 3 Opus on code quality but faster inference due to optimized architecture.
Explicitly acknowledges its training data cutoff (April 2023) and can reason about what information it may not have access to, enabling developers to build systems that know when to query external data sources. The model understands temporal references in queries and can indicate uncertainty about recent events or developments. This is implemented through training data that includes explicit temporal markers and examples of the model declining to answer about post-cutoff events.
Unique: Explicitly trained to recognize and communicate knowledge cutoff boundaries, rather than silently hallucinating about post-cutoff events. This transparency enables developers to build systems that gracefully degrade to external sources when needed.
vs alternatives: More transparent about limitations than GPT-3.5, which often hallucinated about recent events without acknowledging uncertainty; less useful than Claude 3 Opus (trained to April 2024) for applications requiring current information, but better for applications that need explicit cutoff awareness.
Solves mathematical problems including algebra, calculus, geometry, and logic through step-by-step reasoning, using chain-of-thought patterns learned during training. The model can work through multi-step problems, show intermediate steps, and explain reasoning. This works by training the model on mathematical problem-solving datasets and using reinforcement learning to reward correct final answers and clear reasoning paths. The model learns to recognize mathematical patterns and apply appropriate solution strategies.
Unique: Uses chain-of-thought prompting during training to learn explicit reasoning steps, rather than relying on implicit pattern matching. This enables the model to show work and explain reasoning, making it more useful for educational applications than black-box mathematical solvers.
vs alternatives: Better at explaining mathematical reasoning than Gemini Pro due to explicit chain-of-thought training; less reliable than Wolfram Alpha for symbolic computation but more flexible for open-ended mathematical discussion and explanation.
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 OpenAI: GPT-4 Turbo (older v1106) at 23/100.
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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