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| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $4.55e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Qwen3-235B-A22B implements a sparse mixture-of-experts (MoE) architecture that selectively activates 22B parameters per forward pass from a total 235B parameter pool. This routing mechanism uses learned gating functions to dynamically select expert subnetworks based on input tokens, reducing computational cost while maintaining model capacity. The architecture enables efficient inference by computing only active expert pathways rather than the full dense network.
Unique: Qwen3-235B-A22B uses a 235B/22B parameter ratio (10.7x sparsity) with learned routing gates that dynamically select expert pathways, enabling inference cost comparable to 22-30B dense models while maintaining reasoning capacity closer to 235B-scale models through expert specialization
vs alternatives: More parameter-efficient than dense 235B models (10x lower active compute) while maintaining stronger reasoning than 22B baselines through expert diversity, though with higher latency variance than dense models due to routing overhead
Qwen3-235B-A22B implements a two-stage inference pipeline where a 'thinking' mode generates internal reasoning traces (chain-of-thought) before producing final responses. This mode uses a separate token stream for scratchpad computation, allowing the model to decompose complex problems (math, logic, code analysis) into explicit reasoning steps before committing to outputs. The thinking tokens are generated but not exposed to users by default, enabling transparent reasoning without cluttering response text.
Unique: Qwen3 implements thinking mode as a native architectural feature with separate token streams for reasoning vs response, rather than post-hoc prompting tricks, enabling the model to allocate compute budget explicitly to reasoning before response generation
vs alternatives: More efficient reasoning than prompting dense models to 'think step-by-step' because reasoning tokens are generated in a dedicated stream, reducing response latency and allowing the model to optimize reasoning depth independently of response length
Qwen3-235B-A22B supports extended context windows (32K tokens minimum, potentially up to 128K or higher depending on provider configuration) using position interpolation or similar techniques to extend the base training context. This enables the model to maintain semantic coherence across long documents, multi-turn conversations, and large code repositories without losing information from earlier context. The sparse MoE architecture helps manage memory overhead of long contexts by activating only relevant expert pathways.
Unique: Qwen3-235B-A22B combines long-context support with sparse MoE architecture, allowing efficient processing of 32K+ token contexts by activating only expert pathways relevant to the input, reducing memory overhead compared to dense models with equivalent context windows
vs alternatives: Handles longer contexts more efficiently than dense 235B models due to MoE sparsity, while maintaining better semantic coherence than smaller models (7B-13B) that struggle with very long documents despite lower latency
Qwen3-235B-A22B is trained on multilingual corpora and can generate coherent text in 30+ languages including English, Chinese, Spanish, French, German, Japanese, and others. The model maintains semantic understanding across languages and can perform cross-lingual tasks (e.g., translate while reasoning, answer questions in a different language than the prompt). The sparse MoE architecture includes language-specific expert pathways that activate based on detected input language, optimizing inference for each language.
Unique: Qwen3-235B-A22B integrates language-specific expert pathways into its MoE architecture, allowing the model to route computation to language-optimized experts based on input language, rather than using a single dense pathway for all languages
vs alternatives: Stronger multilingual performance than English-centric models (GPT-4, Claude) for non-English languages, particularly Chinese and other Asian languages, due to balanced training data and language-specific expert routing
Qwen3-235B-A22B generates syntactically correct code across 20+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using language-specific training data and expert pathways. The model understands code structure, APIs, and common patterns, enabling it to complete functions, generate unit tests, refactor code, and explain implementation details. The thinking mode can be leveraged for complex algorithmic problems to generate step-by-step solutions before code output.
Unique: Qwen3-235B-A22B combines code generation with optional thinking mode, allowing developers to request step-by-step algorithmic reasoning before code output, improving correctness for complex problems while maintaining fast inference for simple completions
vs alternatives: Stronger code generation for non-English programming contexts and mathematical algorithms compared to Copilot (which optimizes for English-first workflows), while maintaining comparable or better performance on common languages due to larger model scale
Qwen3-235B-A22B can extract structured information from unstructured text and generate outputs conforming to specified JSON schemas or structured formats. The model understands schema constraints and generates valid JSON, CSV, or other structured outputs without requiring external parsing or validation layers. This capability leverages the model's reasoning abilities to map natural language content to structured representations while respecting type constraints and required fields.
Unique: Qwen3-235B-A22B leverages its reasoning capabilities to understand schema constraints and generate compliant structured outputs, rather than using post-hoc regex or parsing; the thinking mode can be used to reason through complex extraction logic before output
vs alternatives: More flexible than rule-based extraction tools (regex, XPath) for complex, context-dependent extraction, while maintaining better schema compliance than smaller models due to larger capacity for understanding constraints
Qwen3-235B-A22B maintains coherent multi-turn conversations by processing the full conversation history (all previous messages) in each forward pass, without requiring external state management or session storage. The model tracks context, user preferences, and conversation flow across 50+ turns while managing token budgets through intelligent context windowing. This stateless design simplifies deployment but requires clients to manage conversation history and pass it with each request.
Unique: Qwen3-235B-A22B uses stateless multi-turn conversation processing where full history is passed with each request, enabling deployment without session storage while leveraging MoE sparsity to manage context window overhead efficiently
vs alternatives: Simpler deployment than stateful systems (no session database required) while maintaining conversation quality comparable to models with explicit session management, though with higher per-request bandwidth due to history transmission
Qwen3-235B-A22B demonstrates strong mathematical reasoning capabilities, including solving algebra, calculus, geometry, and discrete math problems. The thinking mode is particularly effective for math, allowing the model to generate step-by-step solutions with intermediate calculations before final answers. The model can work with symbolic expressions, equations, and mathematical notation, though it does not perform symbolic computation (e.g., cannot simplify complex expressions symbolically like Mathematica).
Unique: Qwen3-235B-A22B integrates thinking mode specifically optimized for mathematical reasoning, allowing the model to allocate compute budget to step-by-step derivations before committing to final answers, improving accuracy on complex problems
vs alternatives: Stronger mathematical reasoning than smaller models (7B-13B) due to scale, while thinking mode provides accuracy improvements comparable to or exceeding prompting techniques like 'chain-of-thought' in dense models
+1 more capabilities
GLM-4.5 uses a Mixture-of-Experts (MoE) architecture to dynamically route tokens through specialized expert networks based on input characteristics, enabling efficient processing of 128k-token contexts without proportional latency increases. The MoE design allows selective expert activation per token, reducing computational overhead while maintaining reasoning depth across extended conversations and multi-document analysis tasks typical of agent-based workflows.
Unique: Mixture-of-Experts routing specifically tuned for agent workloads rather than generic dense models; expert activation patterns are optimized for tool-use sequences and multi-step reasoning rather than general language tasks
vs alternatives: Outperforms dense models like GPT-4 Turbo on agent tasks within 128k context by routing computational budget to relevant experts, reducing latency and cost vs. models that process all tokens through identical layers
GLM-4.5 implements native function calling through a schema-based registry where tools are defined as JSON schemas with parameter constraints, type validation, and description metadata. The model learns to emit structured tool invocations that map directly to function signatures, enabling deterministic tool orchestration without post-processing or regex parsing. Integration with OpenRouter's API exposes this via standard function-calling parameters compatible with OpenAI's format.
Unique: Schema-based function calling is trained directly into the model weights rather than implemented as post-hoc decoding constraints, allowing the model to learn semantic relationships between tool purposes and input context during training
vs alternatives: More reliable than constraint-based function calling (e.g., Guidance, LMQL) because tool selection is learned rather than enforced, reducing parsing failures and enabling the model to reason about tool applicability
Z.ai: GLM 4.5 scores higher at 24/100 vs Qwen: Qwen3 235B A22B at 23/100.
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GLM-4.5 can be used for batch inference through OpenRouter's API, enabling cost-optimized processing of large numbers of requests. Batch processing typically offers reduced pricing compared to real-time API calls and is suitable for non-urgent inference tasks. The model can process batches of prompts efficiently, with results returned after processing completes. This is valuable for agents running scheduled tasks or processing large datasets.
Unique: Batch processing is offered through OpenRouter's unified API rather than a separate batch service, enabling seamless switching between real-time and batch modes with the same client code
vs alternatives: More cost-effective than real-time API for high-volume inference; simpler than managing separate batch infrastructure because OpenRouter handles queuing and result delivery
GLM-4.5 maintains coherent conversation state across turns by encoding prior messages into a compressed representation that persists within the 128k context window. The model uses attention mechanisms to selectively retrieve relevant prior context, enabling agents to reference earlier decisions, tool results, and user preferences without explicit memory management. This is particularly effective for agent workflows where state accumulation (e.g., task progress, discovered facts) must inform subsequent actions.
Unique: Implicit memory management through attention-based context selection rather than explicit memory modules; the model learns which prior turns are relevant without separate retrieval or summarization steps
vs alternatives: More efficient than explicit memory systems (e.g., LangChain's ConversationBufferMemory) because attention is computed once during inference rather than requiring separate retrieval and summarization passes
GLM-4.5 generates code across 40+ programming languages by leveraging training data that includes diverse codebases and syntax patterns. The model understands language-specific idioms, library conventions, and structural patterns (e.g., async/await in JavaScript, type hints in Python, generics in Java) without explicit language-specific modules. Generation is context-aware, respecting indentation, existing code style, and project conventions when completing or extending code snippets.
Unique: Language-agnostic code generation trained on diverse codebases rather than language-specific fine-tuning; the model generalizes syntax patterns across languages, enabling reasonable code generation even for less common languages
vs alternatives: Broader language coverage than specialized models like Codex (which emphasizes Python/JavaScript) but lower quality on niche languages compared to language-specific models; better for polyglot teams than single-language specialists
GLM-4.5 is trained on extensive technical documentation, API references, and code examples, enabling it to understand and reason about complex technical concepts, library APIs, and system architectures. The model can parse API schemas (OpenAPI, GraphQL, Protocol Buffers), understand parameter constraints and type systems, and generate code that correctly uses APIs based on documentation. This is particularly valuable for agent workflows that must interact with external systems.
Unique: Semantic understanding of API schemas and documentation is learned from training data rather than implemented as a separate schema parser; the model reasons about API semantics holistically
vs alternatives: More flexible than code-generation-only models because it understands API semantics and can reason about correctness; better than generic LLMs for technical tasks because training includes extensive API documentation
GLM-4.5 can generate responses that explicitly show reasoning steps, enabling transparency into how conclusions were reached. When prompted with chain-of-thought patterns, the model generates intermediate reasoning steps before final answers, making it suitable for applications requiring explainability or verification. This is implemented through training on reasoning-annotated data and prompt patterns that encourage step-by-step decomposition.
Unique: Chain-of-thought reasoning is trained directly into the model rather than implemented as a decoding strategy; the model learns to generate reasoning steps as part of its core training objective
vs alternatives: More natural and coherent reasoning steps than prompt-injection approaches (e.g., appending 'think step by step') because reasoning is learned as a first-class capability
GLM-4.5 supports multiple languages (Chinese, English, and others) with training that enables cross-lingual reasoning — understanding concepts expressed in one language and reasoning about them in another. The model can translate, summarize, and reason across languages without language-specific degradation. This is particularly valuable for global applications and agents that must operate in multilingual environments.
Unique: Cross-lingual reasoning is learned from multilingual training data rather than implemented as separate language-specific models; the model develops a shared representation across languages
vs alternatives: More efficient than maintaining separate models per language because a single model handles all languages; better for cross-lingual reasoning than language-specific models because the shared representation enables concept transfer
+3 more capabilities