Z.ai: GLM 5.1 ranks higher at 24/100 vs Qwen: Qwen3 235B A22B Thinking 2507 at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Qwen: Qwen3 235B A22B Thinking 2507 | 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 | $1.50e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 10 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements a Mixture-of-Experts architecture that activates only 22B of 235B parameters per forward pass using learned gating mechanisms to route tokens to specialized expert subnetworks. This sparse activation pattern reduces computational cost while maintaining model capacity through expert specialization, enabling complex multi-step reasoning without full model inference overhead. The routing mechanism learns to distribute different reasoning types (mathematical, logical, creative) across domain-specific experts during training.
Unique: Uses learned gating mechanisms to route tokens to 22B active experts from a 235B total pool, implementing true sparse MoE rather than dense-with-pruning approaches. The A22B designation indicates Alibaba's specific expert configuration and routing strategy, which differs from standard MoE implementations in how experts are specialized and load-balanced.
vs alternatives: Achieves 235B-parameter reasoning quality at ~10% of dense inference cost compared to Llama 405B or GPT-4, while maintaining faster latency than dense models through selective expert activation
Supports a 262,144-token context window enabling processing of entire codebases, research papers, or multi-document reasoning tasks in a single forward pass. Uses position interpolation or ALiBi (Attention with Linear Biases) to extend context beyond training length without catastrophic performance degradation. This allows the model to maintain coherence across long reasoning chains and reference distant context without losing information to context truncation.
Unique: Implements 262K context through position interpolation combined with MoE sparse routing, allowing long-context reasoning without the full computational cost of dense 235B inference. The sparse activation means attention computation is still bounded by expert routing decisions, not full quadratic scaling.
vs alternatives: Supports 64x longer context than GPT-4 Turbo (4K) and 6x longer than Claude 3.5 Sonnet (200K) while maintaining faster inference through sparse MoE activation
Implements a thinking-token architecture where the model generates explicit intermediate reasoning steps before producing final answers, similar to OpenAI's o1 approach. The model allocates a portion of its output budget to internal reasoning (marked with special thinking tokens) that are hidden from users but influence the final answer generation. This enables the model to decompose complex problems into sub-steps, backtrack on reasoning paths, and verify intermediate conclusions before committing to a final response.
Unique: Uses explicit thinking tokens during generation that are processed by the model but not returned to users by default, enabling internal reasoning verification without exposing intermediate steps. This differs from prompt-based chain-of-thought (which requires explicit user prompting) by making reasoning a native architectural feature.
vs alternatives: Provides reasoning transparency similar to o1 but with faster inference than o1 (which uses reinforcement learning) through architectural thinking tokens rather than learned reasoning policies
Supports reasoning and generation across 100+ languages using a unified tokenizer and shared expert pool, enabling code-switching and cross-lingual reasoning without language-specific model variants. The model was trained on multilingual data with shared MoE experts that specialize in linguistic patterns rather than language-specific experts, allowing knowledge transfer across languages and enabling reasoning tasks that mix multiple languages in a single prompt.
Unique: Uses a single unified tokenizer and shared MoE expert pool for 100+ languages rather than language-specific experts or separate tokenizers, enabling true cross-lingual reasoning where experts learn language-agnostic reasoning patterns. This contrasts with models that have language-specific expert subgroups.
vs alternatives: Supports more languages than GPT-4 with unified reasoning (no language-specific degradation) and faster inference than separate language-specific models through shared expert routing
Generates and reasons about code across 40+ programming languages using syntax-aware token prediction and language-specific expert routing. The model recognizes language-specific patterns (indentation, syntax rules, common idioms) and routes tokens to experts specialized in particular languages or programming paradigms. This enables generation of syntactically correct code, reasoning about code structure, and cross-language refactoring suggestions without requiring explicit language specification in prompts.
Unique: Routes code generation through language-specific MoE experts that learn syntax patterns and idioms for each language, enabling syntax-aware generation without explicit language specification. The sparse routing means the model activates only relevant language experts per token, reducing interference from unrelated languages.
vs alternatives: Supports more programming languages than Copilot with unified reasoning (no separate model per language) and faster inference than dense models through sparse expert activation
Generates structured outputs (JSON, XML, YAML) that conform to user-provided schemas through constrained decoding and schema-aware expert routing. The model reasons about schema constraints during generation and routes tokens through experts that specialize in structured data formatting, ensuring output validity without post-processing. This enables reliable extraction of structured data from unstructured inputs and generation of API-ready responses without validation overhead.
Unique: Implements schema-aware expert routing where experts specialize in structured formatting patterns, combined with constrained decoding that validates tokens against schema at generation time. This ensures structural validity without post-processing, unlike models that generate freely and require validation.
vs alternatives: Guarantees schema-compliant output without post-processing validation (unlike GPT-4 which requires output validation) and faster than models using external constraint solvers
Supports function calling through a unified interface that routes function invocations to specialized experts and integrates with multiple tool providers (OpenAI-compatible APIs, custom webhooks, MCP servers). The model generates function calls in a standardized format, and the inference platform routes these calls to appropriate handlers based on function registry configuration. This enables building agentic systems where the model can invoke external tools, APIs, and services without requiring separate tool-calling models.
Unique: Routes function-calling decisions through MoE experts that specialize in tool selection and parameter generation, enabling the model to learn which tools are appropriate for different task types. The sparse activation means only relevant tool-selection experts are active, reducing interference from unrelated tools.
vs alternatives: Supports more simultaneous tool integrations than Copilot and faster function-calling latency than dense models through sparse expert routing
Learns new tasks and adapts behavior from examples provided in the prompt context without requiring model fine-tuning or retraining. The model uses in-context learning mechanisms where examples are processed through the same reasoning pipeline as the main task, enabling rapid task adaptation. This allows the model to handle domain-specific terminology, custom output formats, and specialized reasoning patterns by simply providing examples in the prompt.
Unique: Implements in-context learning through the same MoE routing mechanism as main task reasoning, allowing examples to influence expert routing decisions for the main task. This enables the model to learn task-specific expert specializations from context without fine-tuning.
vs alternatives: Faster few-shot adaptation than fine-tuning-based approaches and more flexible than models requiring explicit task-specific training
+2 more capabilities
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 Qwen: Qwen3 235B A22B Thinking 2507 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