{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507","slug":"qwen-qwen3-30b-a3b-thinking-2507","name":"Qwen: Qwen3 30B A3B Thinking 2507","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-30b-a3b-thinking-2507","page_url":"https://unfragile.ai/qwen-qwen3-30b-a3b-thinking-2507","categories":["model-training"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$8.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.separated.thinking.traces","name":"extended-chain-of-thought reasoning with separated thinking traces","description":"Implements a dual-stream architecture where internal reasoning processes are explicitly separated from final outputs, allowing the model to perform multi-step logical decomposition before generating responses. The model uses a Mixture-of-Experts (MoE) routing mechanism to allocate computational resources across specialized reasoning pathways, enabling deeper exploration of problem spaces without exposing intermediate scaffolding to users unless explicitly requested.","intents":["I need the model to show its work for complex multi-step problems so I can verify reasoning correctness","I want to extract intermediate reasoning traces to debug why the model arrived at a particular conclusion","I need to solve problems requiring 5+ logical steps where shallow reasoning would fail"],"best_for":["AI researchers and engineers building interpretable reasoning systems","developers building verification layers that need to audit model decision paths","teams solving complex technical problems (mathematics, logic puzzles, code analysis) where reasoning transparency is critical"],"limitations":["Thinking mode adds latency — extended reasoning traces require additional forward passes, typically 2-5x slower than standard inference","Separated thinking traces increase token consumption; reasoning tokens are billable and can 3-10x the cost of simple queries","Thinking traces are model-generated approximations of reasoning, not guaranteed to be logically sound or complete","No built-in mechanism to constrain reasoning depth; runaway reasoning chains can exhaust token budgets"],"requires":["API access to OpenRouter or compatible inference endpoint supporting Qwen3 models","Support for extended context windows (minimum 32K tokens recommended for complex reasoning)","Client-side parsing logic to extract and handle thinking vs. response content streams"],"input_types":["natural language text","code snippets","mathematical problems","logical reasoning tasks","structured prompts with explicit reasoning instructions"],"output_types":["text response with optional separated thinking traces","structured reasoning chains (when parsed from output)","token-level attribution of reasoning steps"],"categories":["planning-reasoning","interpretability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_1","uri":"capability://text.generation.language.30b.parameter.mixture.of.experts.inference.with.dynamic.expert.routing","name":"30b parameter mixture-of-experts inference with dynamic expert routing","description":"Implements a sparse MoE architecture where the 30B parameter model dynamically routes tokens to specialized expert sub-networks based on learned routing decisions, reducing per-token computational cost compared to dense models while maintaining reasoning capacity. The routing mechanism learns which experts are optimal for different token types and reasoning phases, enabling efficient allocation of the full parameter capacity without computing all parameters for every token.","intents":["I need faster inference than a 30B dense model while maintaining reasoning quality","I want to understand which specialized reasoning pathways the model is using for different problem types","I need to optimize inference cost by reducing compute per token without sacrificing capability"],"best_for":["teams deploying reasoning models at scale where latency and cost are critical","researchers studying expert specialization and routing behavior in sparse models","applications requiring reasoning on resource-constrained infrastructure"],"limitations":["MoE routing adds non-determinism — identical inputs may route through different experts, causing minor output variance","Expert load balancing is non-trivial; poorly balanced routing can cause some experts to be underutilized while others bottleneck","Requires sufficient batch size to amortize routing overhead; single-token inference may be slower than dense alternatives","No public visibility into expert specialization or routing decisions; black-box routing makes debugging difficult"],"requires":["Inference framework supporting MoE architectures (vLLM, TensorRT-LLM, or equivalent)","GPU memory sufficient for expert parameter storage (typically 24GB+ VRAM for full model)","API endpoint with MoE-aware batching and routing logic"],"input_types":["text prompts","multi-turn conversations","code and technical content","reasoning-heavy queries"],"output_types":["text completions","token-level expert routing metadata (if exposed by inference framework)"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_2","uri":"capability://text.generation.language.multi.turn.conversational.context.management.with.reasoning.state.preservation","name":"multi-turn conversational context management with reasoning state preservation","description":"Maintains conversation history across multiple turns while preserving reasoning traces and intermediate thinking states, allowing the model to reference prior reasoning steps and build on previous logical decompositions. The architecture manages separate context streams for thinking and response content, enabling coherent multi-turn reasoning where later turns can reference or refine earlier reasoning without losing interpretability.","intents":["I want to have a multi-turn conversation where the model can reference its previous reasoning steps","I need to iteratively refine a solution by asking follow-up questions that build on prior reasoning","I want to debug a reasoning chain by asking the model to re-examine earlier steps in the conversation"],"best_for":["interactive debugging and problem-solving workflows","educational applications where reasoning transparency across turns is valuable","iterative refinement of complex solutions (code reviews, mathematical proofs, system design)"],"limitations":["Context window is finite; long conversations will eventually exceed token limits and require summarization or pruning","Thinking traces accumulate across turns, consuming tokens rapidly — a 10-turn conversation with reasoning can easily exceed 100K tokens","No built-in mechanism to selectively preserve only relevant reasoning traces; all prior thinking is retained or discarded as a unit","Model cannot explicitly 'forget' earlier reasoning; all context is available, which can lead to reasoning loops or contradictions"],"requires":["API client supporting multi-turn message history (OpenAI-compatible chat format)","Context window of at least 32K tokens (64K+ recommended for reasoning-heavy conversations)","Client-side conversation state management to track thinking vs. response content"],"input_types":["natural language follow-up questions","clarifications and refinements","requests to re-examine prior reasoning","new information to incorporate into existing reasoning"],"output_types":["text responses with optional thinking traces","references to prior reasoning steps","refined or corrected reasoning chains"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_3","uri":"capability://planning.reasoning.complex.problem.decomposition.with.structured.reasoning.paths","name":"complex problem decomposition with structured reasoning paths","description":"Automatically decomposes complex problems into sub-problems and reasoning phases, using the MoE architecture to route different problem aspects through specialized reasoning experts. The model learns to identify problem structure (e.g., mathematical vs. logical vs. code-based reasoning) and allocate reasoning capacity accordingly, producing structured reasoning traces that show problem decomposition steps.","intents":["I need to solve a complex problem that requires breaking it into sub-problems and solving them in sequence","I want to understand how the model is decomposing a problem into logical steps","I need to verify that a complex solution is correct by examining the decomposition strategy"],"best_for":["technical problem-solving (mathematics, algorithms, system design)","code analysis and debugging tasks requiring multi-step reasoning","educational contexts where problem decomposition strategy is important"],"limitations":["Decomposition strategy is learned implicitly; no explicit control over how problems are broken down","Model may decompose problems in ways that are correct but non-intuitive or inefficient","No guarantee that decomposition will find the optimal solution path; reasoning can explore dead ends","Decomposition traces are model-generated and may not match human problem-solving strategies"],"requires":["Well-structured problem statements with clear scope","Sufficient context window to accommodate full decomposition traces (32K+ tokens)","API access to thinking-mode inference"],"input_types":["mathematical problems","algorithmic challenges","code analysis tasks","system design problems","logical reasoning puzzles"],"output_types":["structured reasoning traces showing decomposition steps","sub-problem solutions","final integrated solution","reasoning confidence indicators (implicit in trace structure)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.token.level.control","name":"api-based inference with streaming and token-level control","description":"Exposes the model through OpenRouter's API with support for streaming responses, token counting, and fine-grained control over thinking vs. response token allocation. Clients can stream thinking traces and responses separately, control maximum thinking tokens, and receive detailed token usage metrics including thinking token costs, enabling precise cost management and real-time response handling.","intents":["I want to stream responses in real-time while the model is reasoning","I need to control how many tokens the model spends on reasoning vs. generating the final response","I want to track and optimize the cost of reasoning tokens separately from response tokens"],"best_for":["web applications and chatbots requiring real-time streaming","cost-sensitive deployments where thinking token budgets must be managed","integrations with existing LLM platforms using OpenAI-compatible APIs"],"limitations":["Streaming thinking traces may arrive out-of-order or fragmented; client-side buffering required for coherent trace reconstruction","Token counting is approximate until final response; cost estimates may differ from actual billing","API rate limits and quota management add operational complexity","Thinking token limits are hard constraints; queries exceeding the limit will fail or truncate reasoning"],"requires":["OpenRouter API key with Qwen3 model access","HTTP client supporting streaming (Server-Sent Events or chunked transfer encoding)","Token counting library compatible with Qwen3 tokenizer","Error handling for rate limits and token budget exhaustion"],"input_types":["text prompts via HTTP POST","multi-turn conversation history","optional system prompts and parameters"],"output_types":["streaming text chunks (thinking and response separated)","token usage metadata (thinking tokens, response tokens, total cost)","completion reason (stop, max_tokens, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_5","uri":"capability://code.generation.editing.code.analysis.and.generation.with.reasoning.aware.context","name":"code analysis and generation with reasoning-aware context","description":"Analyzes and generates code by leveraging extended reasoning to understand code structure, dependencies, and correctness properties before generating solutions. The model uses reasoning experts to decompose code problems (refactoring, debugging, optimization) into logical steps, producing code with explicit reasoning traces that justify design decisions and correctness claims.","intents":["I need to understand why a piece of code has a bug and get a fix with reasoning about the root cause","I want to refactor complex code and understand the reasoning behind each refactoring step","I need to generate code for a complex algorithm and verify correctness through reasoning traces"],"best_for":["code review and debugging workflows where reasoning transparency is critical","educational contexts teaching algorithmic thinking and code design","complex code generation tasks (algorithms, system components) where correctness justification is needed"],"limitations":["Reasoning about code is slower than direct generation; expect 2-5x latency increase","Reasoning traces may not catch all bugs; model reasoning is fallible and can miss edge cases","Code generation quality depends on problem clarity; ambiguous requirements lead to ambiguous reasoning","Reasoning traces are verbose; extracting actionable insights requires post-processing"],"requires":["Code snippets or problem descriptions with sufficient context","Support for code-specific tokens and syntax highlighting in reasoning traces","Context window of 32K+ tokens for reasoning about non-trivial code"],"input_types":["code snippets (any language)","bug descriptions and error messages","refactoring requests","algorithm specifications","code review prompts"],"output_types":["corrected or generated code","reasoning traces explaining code decisions","bug analysis with root cause explanation","refactoring justifications"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-30b-a3b-thinking-2507__cap_6","uri":"capability://planning.reasoning.mathematical.problem.solving.with.step.by.step.proof.generation","name":"mathematical problem solving with step-by-step proof generation","description":"Solves mathematical problems by generating explicit step-by-step reasoning traces that function as proofs or derivations, using specialized mathematical reasoning experts to handle symbolic manipulation, logical inference, and numerical computation. The model produces reasoning traces that show each algebraic step, logical inference, or computational operation, enabling verification of mathematical correctness.","intents":["I need to solve a math problem and see every step of the solution for verification","I want to understand why a particular mathematical approach is correct","I need to generate a proof or derivation with explicit reasoning at each step"],"best_for":["educational mathematics (tutoring, homework help, exam preparation)","research contexts requiring verifiable mathematical reasoning","technical problem-solving involving mathematical modeling or analysis"],"limitations":["Mathematical reasoning is computationally expensive; expect 3-10x latency vs. non-reasoning models","Model reasoning about mathematics is not formally verified; proofs are plausible but not guaranteed correct","Symbolic manipulation is limited to what the model can represent in text; complex symbolic systems may be approximated","Numerical precision is limited by floating-point representation; high-precision mathematics may lose accuracy"],"requires":["Clear mathematical problem statements with sufficient context","Support for mathematical notation (LaTeX, Unicode symbols) in reasoning traces","Context window of 32K+ tokens for multi-step proofs"],"input_types":["mathematical problems (algebra, calculus, linear algebra, etc.)","proof requests","derivation problems","numerical computation tasks"],"output_types":["step-by-step solutions with reasoning traces","mathematical proofs or derivations","numerical answers with intermediate calculations shown","alternative solution approaches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["API access to OpenRouter or compatible inference endpoint supporting Qwen3 models","Support for extended context windows (minimum 32K tokens recommended for complex reasoning)","Client-side parsing logic to extract and handle thinking vs. response content streams","Inference framework supporting MoE architectures (vLLM, TensorRT-LLM, or equivalent)","GPU memory sufficient for expert parameter storage (typically 24GB+ VRAM for full model)","API endpoint with MoE-aware batching and routing logic","API client supporting multi-turn message history (OpenAI-compatible chat format)","Context window of at least 32K tokens (64K+ recommended for reasoning-heavy conversations)","Client-side conversation state management to track thinking vs. response content","Well-structured problem statements with clear scope"],"failure_modes":["Thinking mode adds latency — extended reasoning traces require additional forward passes, typically 2-5x slower than standard inference","Separated thinking traces increase token consumption; reasoning tokens are billable and can 3-10x the cost of simple queries","Thinking traces are model-generated approximations of reasoning, not guaranteed to be logically sound or complete","No built-in mechanism to constrain reasoning depth; runaway reasoning chains can exhaust token budgets","MoE routing adds non-determinism — identical inputs may route through different experts, causing minor output variance","Expert load balancing is non-trivial; poorly balanced routing can cause some experts to be underutilized while others bottleneck","Requires sufficient batch size to amortize routing overhead; single-token inference may be slower than dense alternatives","No public visibility into expert specialization or routing decisions; black-box routing makes debugging difficult","Context window is finite; long conversations will eventually exceed token limits and require summarization or pruning","Thinking traces accumulate across turns, consuming tokens rapidly — a 10-turn conversation with reasoning can easily exceed 100K tokens","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen-qwen3-30b-a3b-thinking-2507","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-30b-a3b-thinking-2507"}},"signature":"An2ju/S3V0V4KY7YjC9AAM7y1/H7Zb8JiNqniQ9we2T2JgHic3SVGcUkIZnOnuCXiIN/YmYNfalIhacVO9/HDw==","signedAt":"2026-06-21T04:37:57.425Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-30b-a3b-thinking-2507","artifact":"https://unfragile.ai/qwen-qwen3-30b-a3b-thinking-2507","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-30b-a3b-thinking-2507","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}