{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"qwq-32b","slug":"qwq-32b","name":"QwQ 32B","type":"model","url":"https://qwenlm.github.io/blog/qwq-32b/","page_url":"https://unfragile.ai/qwq-32b","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"qwq-32b__cap_0","uri":"capability://planning.reasoning.explicit.chain.of.thought.reasoning.with.visible.intermediate.tokens","name":"explicit chain-of-thought reasoning with visible intermediate tokens","description":"QwQ-32B generates intermediate reasoning tokens that are visible in the output stream before producing a final answer, implementing transparent chain-of-thought reasoning through a two-stage reinforcement learning process. The model was trained with outcome-based rewards on math and coding tasks using verification servers (accuracy verifiers for math, code execution servers for testing), then fine-tuned for general capabilities using a general reward model. This approach makes the reasoning process inspectable and auditable rather than hidden in latent representations.","intents":["I need to see how a model arrives at its answer for debugging and trust verification","I want to understand the reasoning steps before the final answer for educational purposes","I need to validate intermediate reasoning correctness in math and coding problems","I want to trace where a model made an error in its logical chain"],"best_for":["researchers studying model reasoning transparency","educators using AI for math and science instruction","developers building interpretable reasoning systems","teams requiring auditable decision-making in high-stakes domains"],"limitations":["Reasoning token overhead increases total output length and latency compared to non-reasoning models — exact overhead not quantified in documentation","Visible reasoning tokens consume API quota and increase inference costs proportionally to reasoning depth","Reasoning quality depends on problem domain — optimized for math/coding, unknown performance on open-ended reasoning"],"requires":["Python 3.8+ with PyTorch or transformers library","Support for streaming/token-by-token output in inference framework","Sufficient context window to accommodate both reasoning and final answer tokens"],"input_types":["natural language problem statements","mathematical equations and expressions","code snippets with execution context","multi-turn chat messages with role/content structure"],"output_types":["reasoning token stream (intermediate steps)","final answer text","combined output with reasoning visible before answer"],"categories":["planning-reasoning","interpretability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_1","uri":"capability://planning.reasoning.mathematical.problem.solving.with.outcome.based.verification","name":"mathematical problem-solving with outcome-based verification","description":"QwQ-32B solves mathematical problems by leveraging reinforcement learning trained with outcome-based rewards using accuracy verifiers that check solution correctness. The model was trained on math tasks where a verification system evaluates whether the final answer is correct, enabling the model to learn which reasoning paths lead to correct solutions. This approach achieves 79.5% on AIME 2024 and 96.4% on MATH-500 benchmarks, demonstrating strong performance on competition-level and standardized math problems.","intents":["I need to solve AIME-level competition math problems programmatically","I want to generate step-by-step solutions to standardized math problems","I need a model that can verify its own mathematical reasoning","I want to use AI for automated math tutoring with correct solutions"],"best_for":["math educators and tutoring platforms","competitive programming and math olympiad preparation","automated homework checking systems","research teams studying mathematical reasoning in LLMs"],"limitations":["Performance optimized for closed-form math problems with verifiable answers — unknown performance on open-ended mathematical reasoning or proof-writing","Benchmark results (AIME 79.5%, MATH-500 96.4%) represent peak performance; real-world accuracy on arbitrary math problems not documented","Reasoning token overhead for complex problems may result in very long output sequences with unclear token count impact"],"requires":["Python 3.8+ with transformers library","GPU with sufficient VRAM for 32B model inference (exact requirement unknown)","Input problems formatted as natural language mathematical statements or equations"],"input_types":["mathematical problem statements in natural language","equations and mathematical notation","multi-step word problems","competition math problems (AIME, MATH-500 format)"],"output_types":["step-by-step mathematical reasoning","intermediate calculation steps","final numerical or symbolic answer","verification of solution correctness"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_10","uri":"capability://planning.reasoning.parameter.efficient.reasoning.through.rl.scaling","name":"parameter-efficient reasoning through rl scaling","description":"QwQ-32B achieves reasoning performance comparable to much larger models (DeepSeek-R1 at 671B parameters) through efficient reinforcement learning training on robust foundation models. The model uses outcome-based rewards and verification servers to scale reasoning capability without proportional parameter increases. This approach demonstrates that RL-based training can achieve reasoning efficiency gains, enabling competitive performance at 32B parameters.","intents":["I need reasoning capability with minimal computational requirements","I want to understand how RL training improves reasoning efficiency","I need to deploy reasoning models on resource-constrained hardware","I want to study parameter-efficient reasoning approaches"],"best_for":["resource-constrained environments","research teams studying reasoning efficiency","teams optimizing inference costs","edge deployment scenarios"],"limitations":["Efficiency gains attributed to RL training approach — specific architectural innovations not documented","Comparison with DeepSeek-R1 based on claimed performance — detailed benchmark comparisons not provided","Training efficiency metrics (compute, data requirements) not documented","Unknown how efficiency scales to other parameter ranges or domains"],"requires":["Understanding of RL training approaches","Baseline foundation model for RL fine-tuning","Verification systems for outcome-based rewards"],"input_types":["foundation model weights","training data for RL","verification systems for rewards"],"output_types":["RL-trained model weights","reasoning capability metrics","efficiency comparisons"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_11","uri":"capability://planning.reasoning.benchmark.validated.reasoning.performance.on.standardized.datasets","name":"benchmark-validated reasoning performance on standardized datasets","description":"QwQ-32B provides documented performance metrics on standardized reasoning benchmarks including AIME 2024 (79.5%), MATH-500 (96.4%), and LiveCodeBench, enabling quantitative comparison with other reasoning models. These benchmark results are publicly reported and provide concrete evidence of reasoning capability on well-defined problem sets. The benchmarks cover mathematical reasoning, coding, and general problem-solving domains.","intents":["I need to compare reasoning models quantitatively","I want to understand model capability on specific problem types","I need to validate reasoning performance before deployment","I want to benchmark reasoning models against standardized datasets"],"best_for":["teams evaluating reasoning models for specific use cases","researchers comparing model capabilities","organizations requiring performance validation","benchmarking and evaluation workflows"],"limitations":["Benchmark performance may not generalize to real-world problems outside benchmark domains","LiveCodeBench score not provided — only mentioned without quantitative results","Benchmark results represent peak performance — actual production performance may vary","No error analysis or failure mode documentation for benchmark problems","Comparison with other models (DeepSeek-R1, o1-mini) mentioned but detailed comparative scores not provided"],"requires":["Access to benchmark datasets (AIME 2024, MATH-500, LiveCodeBench)","Evaluation infrastructure for running benchmarks","Comparison baseline models"],"input_types":["benchmark problem statements","test cases","evaluation metrics"],"output_types":["benchmark scores","performance metrics","comparative analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_2","uri":"capability://code.generation.editing.code.generation.and.execution.verification","name":"code generation and execution verification","description":"QwQ-32B generates code solutions and verifies them through reinforcement learning trained with outcome-based rewards using code execution servers that run test cases against generated code. The model learns to produce code that passes execution tests by receiving feedback from actual test case runs, enabling it to refine solutions based on execution results. This approach achieves strong performance on LiveCodeBench and enables the model to generate executable, tested code rather than syntactically-correct but functionally-incorrect solutions.","intents":["I need to generate code that passes test cases automatically","I want a model that can verify its own code correctness through execution","I need to solve coding problems with guaranteed working solutions","I want to use AI for automated code review and test-driven development"],"best_for":["competitive programming platforms and coding interview preparation","automated code generation with test-driven verification","coding education platforms requiring executable solutions","development teams building code generation pipelines with quality gates"],"limitations":["Code generation optimized for problems with clear test cases and verifiable correctness — unknown performance on open-ended code design or architectural problems","Execution verification requires sandboxed environment for code execution — not suitable for generating code that requires external API calls or system-level operations","Reasoning token overhead for complex code problems may result in very long output sequences with unclear impact on latency"],"requires":["Python 3.8+ with transformers library","GPU with sufficient VRAM for 32B model inference","Test case definitions or execution environment for code verification","Sandboxed code execution environment for safety"],"input_types":["coding problem statements in natural language","algorithm descriptions and requirements","test case specifications","code snippets requiring completion or debugging"],"output_types":["executable code in multiple programming languages","step-by-step code generation reasoning","test case execution results","verification status (pass/fail)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_3","uri":"capability://tool.use.integration.agent.based.reasoning.with.tool.use.and.environmental.feedback","name":"agent-based reasoning with tool use and environmental feedback","description":"QwQ-32B supports agent-based reasoning where the model can use tools and adapt based on environmental feedback, enabling it to interact with external systems and refine solutions based on execution results. The model was trained with reinforcement learning to handle tool use and environmental feedback, allowing it to function as an autonomous agent that can call functions, receive results, and adjust its reasoning accordingly. This capability enables multi-step problem-solving where the model can iteratively refine solutions based on real-world feedback.","intents":["I need an AI agent that can call external tools and APIs to solve problems","I want a model that can adapt its reasoning based on tool execution results","I need to build autonomous agents that can interact with external systems","I want to use AI for multi-step problem-solving with real-world feedback loops"],"best_for":["developers building autonomous AI agents","teams implementing tool-use systems for complex problem-solving","applications requiring iterative refinement based on external feedback","research teams studying agent-based reasoning and planning"],"limitations":["Specific tool-use protocol and agent framework not documented — integration approach unknown","Tool availability and function definitions must be provided at inference time — no built-in tool registry","Agent reasoning quality depends on tool reliability and feedback quality — poor tool implementations may degrade agent performance","No explicit documentation of maximum tool-use depth or iteration limits"],"requires":["Python 3.8+ with transformers library","Tool/function definitions in supported format (specific format unknown)","External systems or APIs for tool execution","Feedback mechanism to communicate tool execution results back to model"],"input_types":["natural language problem statements","tool/function definitions and signatures","tool execution results and feedback","multi-turn agent interactions"],"output_types":["tool calls with parameters","reasoning steps between tool calls","final solution based on tool feedback","agent action sequences"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_4","uri":"capability://text.generation.language.general.instruction.following.and.human.preference.alignment","name":"general instruction following and human preference alignment","description":"QwQ-32B follows general instructions and aligns with human preferences through a second stage of reinforcement learning training using a general reward model and rule-based verifiers. After initial math and coding-specific RL training, the model was fine-tuned with a general reward model to improve performance on diverse tasks and align with human preferences. This two-stage approach enables the model to maintain strong reasoning capabilities while also following general instructions and producing human-preferred outputs.","intents":["I need a model that follows complex multi-step instructions accurately","I want AI that produces outputs aligned with human preferences and values","I need to use reasoning models for general-purpose tasks beyond math and coding","I want a model that balances reasoning depth with practical instruction-following"],"best_for":["general-purpose AI applications requiring instruction following","teams building AI systems with human preference alignment requirements","applications combining reasoning with practical task execution","users needing reasoning models for diverse problem domains"],"limitations":["General instruction-following performance not benchmarked in documentation — no metrics provided for non-math/coding tasks","Preference alignment based on general reward model — specific alignment criteria and training data not documented","Reasoning overhead may be unnecessary for simple instruction-following tasks, increasing latency and cost","Unknown how well general alignment transfers to domain-specific preferences"],"requires":["Python 3.8+ with transformers library","GPU with sufficient VRAM for 32B model inference","Clear, well-structured instructions for optimal performance"],"input_types":["natural language instructions","multi-step task descriptions","preference specifications","context and background information"],"output_types":["instruction-following responses","reasoning steps (when applicable)","final answers aligned with preferences","structured outputs based on instruction format"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_5","uri":"capability://automation.workflow.local.self.hosted.inference.on.single.gpu","name":"local self-hosted inference on single gpu","description":"QwQ-32B can be deployed for inference on a single GPU using the HuggingFace Transformers library with PyTorch, enabling self-hosted reasoning applications without cloud API dependencies. The model is distributed as open-weight model files (SafeTensors format) on HuggingFace Hub and ModelScope, allowing developers to download and run the model locally with standard inference code. This approach provides full control over inference, data privacy, and eliminates API latency and quota constraints.","intents":["I need to run a reasoning model locally without sending data to cloud APIs","I want to deploy reasoning capabilities on my own infrastructure","I need to avoid API rate limits and quota constraints for reasoning tasks","I want to maintain data privacy by keeping reasoning on-premises"],"best_for":["enterprises with data privacy requirements","developers building reasoning into local applications","teams with high-volume reasoning workloads requiring cost optimization","researchers experimenting with reasoning models without API dependencies"],"limitations":["Exact VRAM requirement for single-GPU deployment not documented — requires empirical testing to determine minimum GPU memory needed","No quantization formats (GGUF, int8, int4) mentioned in documentation — full precision inference may require high-end GPUs","Inference latency not benchmarked — reasoning token generation may be slow on consumer-grade GPUs","Requires GPU with sufficient VRAM; CPU-only inference not documented as supported","No built-in batching or optimization for throughput — single-request inference likely"],"requires":["Python 3.8+","PyTorch 1.13+ or compatible deep learning framework","transformers library 4.36+","NVIDIA GPU with CUDA support (specific VRAM requirement unknown, likely 40GB+ for full precision)","HuggingFace account for model access (model is open-weight but requires authentication)"],"input_types":["text prompts in chat format","problem statements","code snippets","multi-turn conversation history"],"output_types":["text generation with reasoning tokens","streaming token output","complete response with reasoning and answer"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_6","uri":"capability://automation.workflow.apache.2.0.licensed.open.weight.model.distribution","name":"apache 2.0 licensed open-weight model distribution","description":"QwQ-32B is distributed under Apache 2.0 license as open-weight model files, allowing unrestricted commercial and non-commercial use with attribution. The model weights are publicly available on HuggingFace Hub (Qwen/QwQ-32B) and ModelScope, enabling free download and deployment without licensing restrictions. This open-source approach provides legal clarity for commercial applications and enables community contributions and fine-tuning.","intents":["I need a reasoning model I can use commercially without licensing restrictions","I want to fine-tune a reasoning model for my specific domain","I need to build commercial products using reasoning capabilities","I want to contribute improvements to a reasoning model"],"best_for":["commercial AI product developers","enterprises requiring open-source models for compliance","researchers fine-tuning models for specialized domains","teams building on top of reasoning models"],"limitations":["Apache 2.0 license requires attribution in derivative works","No warranty or liability protection — model provided as-is","Commercial support not included in open-source distribution","Fine-tuning and redistribution require maintaining Apache 2.0 compliance"],"requires":["Acceptance of Apache 2.0 license terms","Attribution in any derivative works or products","Compliance with open-source licensing requirements"],"input_types":["model weights in SafeTensors format","model configuration files","tokenizer definitions"],"output_types":["downloadable model files","model cards with documentation","license and attribution information"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_7","uri":"capability://code.generation.editing.huggingface.transformers.compatible.inference.api","name":"huggingface transformers compatible inference api","description":"QwQ-32B integrates with HuggingFace Transformers library using standard PyTorch APIs, enabling inference through familiar AutoModelForCausalLM and AutoTokenizer interfaces. The model uses standard chat template formatting for multi-turn conversations and supports device mapping for automatic GPU/CPU allocation. This compatibility enables drop-in integration with existing HuggingFace-based inference pipelines and tools.","intents":["I want to use QwQ-32B with existing HuggingFace inference code","I need to integrate reasoning into my HuggingFace-based application","I want to use standard transformers APIs for model loading and inference","I need automatic device mapping for GPU memory management"],"best_for":["developers already using HuggingFace Transformers","teams with existing HuggingFace inference pipelines","researchers using standard transformers APIs","applications requiring minimal integration effort"],"limitations":["Limited to PyTorch backend — no native ONNX or TensorFlow support documented","Chat template application required for multi-turn conversations — raw token generation may not follow expected format","Device mapping is automatic but may not be optimal for all hardware configurations","No built-in streaming or token-by-token output in standard transformers API — requires custom implementation"],"requires":["Python 3.8+","transformers library 4.36+","PyTorch 1.13+","HuggingFace account for model access"],"input_types":["text prompts","chat messages with role/content structure","tokenized input IDs"],"output_types":["generated text tokens","token logits","generation with attention masks"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_8","uri":"capability://automation.workflow.alibaba.cloud.dashscope.api.access","name":"alibaba cloud dashscope api access","description":"QwQ-32B is available through Alibaba Cloud DashScope API, providing cloud-hosted inference without local GPU requirements. The API provides managed inference with automatic scaling, monitoring, and integration with Alibaba Cloud services. This option enables teams without GPU infrastructure to access reasoning capabilities through standard REST/gRPC APIs.","intents":["I need to use reasoning without managing GPU infrastructure","I want to scale reasoning inference automatically","I need to integrate reasoning into cloud-native applications","I want managed inference with monitoring and reliability"],"best_for":["teams without GPU infrastructure","applications requiring auto-scaling inference","Alibaba Cloud customers","teams preferring managed services over self-hosted"],"limitations":["Pricing not documented in provided materials — cost comparison with self-hosted unknown","API rate limits and quotas not specified","Data sent to Alibaba Cloud servers — not suitable for strict data privacy requirements","API latency not benchmarked — may be higher than local inference","Vendor lock-in to Alibaba Cloud ecosystem"],"requires":["Alibaba Cloud account","DashScope API credentials","Network connectivity to Alibaba Cloud","Understanding of DashScope API format (specific format not documented)"],"input_types":["text prompts","chat messages","problem statements"],"output_types":["text responses","streaming responses","reasoning tokens with final answer"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__cap_9","uri":"capability://text.generation.language.multi.language.chat.interface.with.role.based.formatting","name":"multi-language chat interface with role-based formatting","description":"QwQ-32B supports multi-turn conversations using standard chat template formatting with role/content message structure, enabling natural dialogue interactions. The model applies chat templates automatically to format messages with system, user, and assistant roles, enabling multi-turn reasoning conversations. This approach enables interactive reasoning where users can ask follow-up questions and receive contextual responses.","intents":["I want to have multi-turn reasoning conversations with the model","I need to provide system instructions and context for reasoning","I want to ask follow-up questions based on previous reasoning","I need to build chatbot interfaces with reasoning capabilities"],"best_for":["interactive reasoning applications","chatbot interfaces with reasoning","educational tutoring systems","conversational problem-solving"],"limitations":["Chat template application required — raw token generation may not follow expected format","Context window length not documented — maximum conversation history unknown","Multi-turn reasoning may accumulate reasoning tokens, increasing total output length","No explicit conversation state management — requires external tracking"],"requires":["Chat template support in inference framework","Message formatting with role/content structure","Conversation history management"],"input_types":["user messages","system prompts","previous conversation history","role-based message structure"],"output_types":["assistant responses","reasoning tokens","final answers","multi-turn conversation history"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwq-32b__headline","uri":"capability://planning.reasoning.compact.reasoning.model.for.math.science.and.coding","name":"compact reasoning model for math, science, and coding","description":"QwQ 32B is a 32 billion parameter reasoning model that excels in math, science, and coding tasks, providing transparent chain-of-thought reasoning in its outputs, making it ideal for developers seeking efficient self-hosted solutions.","intents":["best reasoning model for coding","AI model for math problem solving","top model for science reasoning","self-hosted reasoning model for developers","best compact AI model for coding tasks"],"best_for":["developers needing efficient reasoning models"],"limitations":[],"requires":["single GPU for deployment"],"input_types":["text-based prompts"],"output_types":["tokens with reasoning process"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ with PyTorch or transformers library","Support for streaming/token-by-token output in inference framework","Sufficient context window to accommodate both reasoning and final answer tokens","Python 3.8+ with transformers library","GPU with sufficient VRAM for 32B model inference (exact requirement unknown)","Input problems formatted as natural language mathematical statements or equations","Understanding of RL training approaches","Baseline foundation model for RL fine-tuning","Verification systems for outcome-based rewards","Access to benchmark datasets (AIME 2024, MATH-500, LiveCodeBench)"],"failure_modes":["Reasoning token overhead increases total output length and latency compared to non-reasoning models — exact overhead not quantified in documentation","Visible reasoning tokens consume API quota and increase inference costs proportionally to reasoning depth","Reasoning quality depends on problem domain — optimized for math/coding, unknown performance on open-ended reasoning","Performance optimized for closed-form math problems with verifiable answers — unknown performance on open-ended mathematical reasoning or proof-writing","Benchmark results (AIME 79.5%, MATH-500 96.4%) represent peak performance; 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