Z.ai: GLM 5.1 ranks higher at 24/100 vs Sao10K: Llama 3.1 Euryale 70B v2.2 at 21/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Sao10K: Llama 3.1 Euryale 70B v2.2 | Z.ai: GLM 5.1 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 21/100 | 24/100 |
| Adoption |
| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $8.50e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates detailed character personas, backstories, and dialogue patterns optimized for immersive roleplay scenarios. The model uses instruction-tuning specifically calibrated for creative fiction and character consistency, enabling multi-turn conversations where the model maintains character voice, motivations, and narrative coherence across extended interactions without breaking character or losing context.
Unique: Built on Llama 3.1 70B with specialized instruction-tuning for creative roleplay scenarios, optimizing for character consistency and narrative immersion rather than general-purpose instruction-following. The v2.2 iteration refines character voice stability and dialogue authenticity through targeted fine-tuning on curated creative fiction datasets.
vs alternatives: Outperforms general-purpose models like base Llama 3.1 and GPT-4 for sustained character roleplay by maintaining persona consistency and creative voice over extended conversations, though sacrifices factual accuracy and technical reasoning capabilities in exchange for narrative coherence.
Maintains coherent conversation state across multiple turns by preserving character context, narrative details, and conversational history within a single session. The model processes the full conversation history as context for each response, enabling it to reference prior exchanges, maintain consistent characterization, and build narrative continuity without explicit memory management or external state stores.
Unique: Leverages Llama 3.1's extended context window (typically 8K-16K tokens) combined with fine-tuning for roleplay to maintain character consistency across dialogue turns by processing the entire conversation history as input context, rather than using external memory systems or summarization layers.
vs alternatives: Simpler to implement than models requiring external RAG or memory systems, but less scalable than architectures with persistent vector stores for very long-running campaigns or multi-session narratives.
Accepts detailed system prompts and user instructions to define character traits, narrative rules, and creative boundaries, then generates responses that adhere to these constraints while maintaining natural dialogue flow. The model interprets structured instructions (character sheets, world-building rules, tone guidelines) and applies them consistently across responses without requiring explicit constraint-checking or validation layers.
Unique: Fine-tuned to prioritize adherence to creative constraints and system instructions while maintaining natural dialogue, using instruction-tuning that weights constraint-following heavily during training on curated roleplay datasets with explicit character and narrative rules.
vs alternatives: More responsive to detailed creative constraints than general-purpose models, but less reliable than formal rule engines or constraint-satisfaction solvers for complex, multi-faceted rule systems.
Generates extended prose passages, scene descriptions, and narrative exposition that maintain coherence, pacing, and literary quality across hundreds of tokens. The model applies narrative structure patterns (setup, conflict, resolution) and literary techniques (dialogue, description, internal monologue) to produce immersive storytelling that reads naturally without repetition or structural breakdown.
Unique: Optimized through fine-tuning on creative fiction datasets to maintain narrative coherence and literary quality across extended passages, with particular attention to dialogue integration, pacing variation, and avoiding repetitive patterns that plague general-purpose models.
vs alternatives: Produces more narratively coherent and stylistically consistent long-form prose than base Llama 3.1, though less polished than specialized creative writing models trained on published fiction corpora.
Provides access to the Euryale 70B v2.2 model through OpenRouter's API infrastructure, enabling remote inference without local hardware requirements. Requests are routed through OpenRouter's load-balanced endpoints, with support for standard LLM API patterns (messages format, streaming, token counting) and integration with OpenRouter's provider abstraction layer.
Unique: Accessed exclusively through OpenRouter's API abstraction layer, which provides standardized LLM API patterns (compatible with OpenAI message format) and load-balanced routing to Euryale endpoints, abstracting away infrastructure complexity while maintaining compatibility with existing LLM client libraries.
vs alternatives: Easier to integrate than self-hosted inference (no GPU/VRAM requirements), but higher latency and per-token costs compared to local deployment; more specialized than general-purpose OpenAI API but less flexible than self-hosted fine-tuning.
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 Sao10K: Llama 3.1 Euryale 70B v2.2 at 21/100.
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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