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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $3.00e-8 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes inference using a Mixture-of-Experts (MoE) architecture where only 2B of 24B total parameters are active per forward pass, reducing computational cost and latency through sparse gating mechanisms. The model routes input tokens to specialized expert subnetworks based on learned routing weights, enabling efficient deployment on resource-constrained devices while maintaining quality comparable to dense models. This hybrid architecture balances model capacity with inference efficiency through selective expert activation rather than full parameter computation.
Unique: LFM2-24B-A2B implements a hybrid MoE architecture with only 2B active parameters per token, achieving 8x parameter efficiency compared to dense 24B models while maintaining reasoning quality through specialized expert routing. This design specifically targets on-device deployment where memory bandwidth and compute are bottlenecks, using learned gating to dynamically select relevant experts rather than static pruning.
vs alternatives: More parameter-efficient than dense 24B models (Llama 2 24B, Mistral 24B) with lower latency and memory footprint, while maintaining competitive quality through expert specialization; more capable than 7B dense models due to larger total parameter capacity despite sparse activation.
Maintains coherent dialogue across multiple turns by processing conversation history as context, enabling the model to track entities, maintain conversational state, and reason about prior exchanges. The model uses standard transformer attention mechanisms to weight relevant historical context, allowing it to reference earlier statements, correct misunderstandings, and build on previous reasoning chains. This capability supports both stateless API calls (where full history is passed each turn) and stateful conversation management patterns.
Unique: LFM2-24B-A2B achieves multi-turn reasoning with sparse MoE activation, routing conversation context tokens through specialized experts for dialogue understanding. This allows efficient processing of long conversation histories compared to dense models, as only relevant expert pathways activate for context integration rather than full parameter computation.
vs alternatives: More efficient multi-turn processing than dense 24B models due to sparse activation, enabling longer conversation histories within the same latency budget; comparable dialogue quality to larger dense models (70B+) while using 1/3 the active parameters.
Generates and completes code across multiple programming languages by predicting syntactically and semantically valid continuations of code snippets. The model uses transformer attention to understand code structure, variable scope, and API patterns from context, enabling both single-line completions and multi-function generation. Supports both inline completion (filling gaps in existing code) and full-function generation from docstrings or type signatures.
Unique: LFM2-24B-A2B generates code using sparse MoE routing, where language-specific experts activate based on detected programming language, enabling efficient multi-language support without full parameter activation per language. This architecture allows the model to maintain specialized code generation quality across 10+ languages while using only 2B active parameters.
vs alternatives: More efficient code generation than dense 24B models with lower latency per completion, while maintaining quality competitive with larger models (Codex, GPT-4) for common languages; better multi-language support than single-language-optimized models due to expert specialization.
Interprets natural language instructions and decomposes complex tasks into subtasks or step-by-step execution plans. The model uses attention mechanisms to identify task constraints, dependencies, and success criteria from instruction text, then generates structured plans or reasoning traces. Supports both implicit task decomposition (reasoning internally) and explicit plan generation (outputting step-by-step instructions for external execution).
Unique: LFM2-24B-A2B performs task decomposition using sparse expert routing where planning-specific experts activate for instruction parsing and subtask generation. This enables efficient reasoning without full parameter activation, allowing the model to handle complex multi-step tasks within latency budgets suitable for interactive systems.
vs alternatives: More efficient task decomposition than dense 24B models with lower latency for real-time planning; comparable reasoning quality to larger models (70B+) while using 1/3 the active parameters, making it suitable for cost-sensitive agent deployments.
Generates text informed by provided context or knowledge documents, using attention mechanisms to ground responses in supplied information rather than relying solely on training data. The model integrates context passages into the attention computation, allowing it to cite sources, synthesize information from multiple documents, and reduce hallucination by constraining generation to supported facts. This capability is commonly used in retrieval-augmented generation (RAG) pipelines where external knowledge is injected into the prompt.
Unique: LFM2-24B-A2B grounds text generation using sparse MoE routing where knowledge-integration experts activate when context documents are present, enabling efficient RAG without full parameter computation. This allows the model to handle large context windows (with external retrieval) while maintaining low latency compared to dense models.
vs alternatives: More efficient knowledge grounding than dense 24B models, enabling longer context windows within latency budgets; comparable RAG quality to larger models (70B+) while using 1/3 the active parameters, reducing API costs for knowledge-grounded applications.
Provides real-time text generation through streaming API endpoints, where tokens are emitted incrementally as they are generated rather than waiting for full response completion. The model uses token-by-token generation with streaming protocols (e.g., Server-Sent Events, WebSocket) to enable low-latency user feedback and progressive response rendering. Supports both buffered (full response at once) and streaming (incremental token) output modes.
Unique: LFM2-24B-A2B streaming inference via OpenRouter uses sparse MoE token generation, where each token activates only relevant experts, reducing per-token latency compared to dense models. This enables faster streaming output and lower time-to-first-token (TTFT) for interactive applications.
vs alternatives: Faster token generation than dense 24B models due to sparse activation, enabling more responsive streaming UX; comparable streaming quality to larger models (70B+) while using 1/3 the active parameters, reducing infrastructure costs for streaming applications.
Generates text constrained to specific formats or schemas (e.g., JSON, XML, CSV, function calls) by using prompt engineering, output validation, or constrained decoding techniques. The model learns to follow format specifications from examples or explicit instructions, enabling reliable extraction of structured data from unstructured prompts. Supports both soft constraints (instructions in prompt) and hard constraints (validation/filtering of generated tokens).
Unique: LFM2-24B-A2B generates structured output using sparse MoE routing where format-specific experts activate based on detected output schema, enabling efficient multi-format support without full parameter activation. This allows the model to maintain format consistency across diverse output types while using only 2B active parameters.
vs alternatives: More efficient structured generation than dense 24B models with lower latency for format-constrained tasks; comparable format adherence to larger models (70B+) while using 1/3 the active parameters, reducing costs for data extraction and function-calling applications.
Generates and translates text across multiple languages by routing language-specific tokens through specialized expert pathways in the MoE architecture. The model learns language-specific patterns and vocabulary during training, enabling both translation (source-to-target language conversion) and code-switching (mixing languages in single response). Supports both explicit translation prompts and implicit multilingual generation based on input language.
Unique: LFM2-24B-A2B implements cross-lingual generation using language-specific MoE experts that activate based on detected input/output language, enabling efficient multilingual support without full parameter activation per language. This architecture allows the model to maintain translation quality across 50+ languages while using only 2B active parameters.
vs alternatives: More efficient multilingual generation than dense 24B models with lower latency for translation tasks; comparable translation quality to larger models (70B+) while using 1/3 the active parameters, reducing costs for multilingual applications and enabling broader language coverage than single-language-optimized models.
+1 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 LiquidAI: LFM2-24B-A2B 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