{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-stepfun-step-3.5-flash","slug":"stepfun-step-3.5-flash","name":"StepFun: Step 3.5 Flash","type":"model","url":"https://openrouter.ai/models/stepfun~step-3.5-flash","page_url":"https://unfragile.ai/stepfun-step-3.5-flash","categories":["chatbots-assistants"],"tags":["stepfun","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-stepfun-step-3.5-flash__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.text.generation.with.selective.parameter.activation","name":"sparse mixture-of-experts text generation with selective parameter activation","description":"Generates text by selectively activating only 11B of 196B parameters per token using a sparse Mixture of Experts (MoE) architecture. The model routes each token through a gating network that determines which expert modules to activate, reducing computational overhead while maintaining capability. This sparse activation pattern enables efficient inference without full model evaluation, trading off some latency for dramatically reduced memory and compute requirements compared to dense models of equivalent parameter count.","intents":["I need a capable language model that runs efficiently on consumer hardware without sacrificing reasoning quality","I want to deploy a foundation model with lower inference costs and faster response times than dense alternatives","I need to understand how sparse MoE routing decisions affect model behavior for specific domains or tasks"],"best_for":["teams building cost-sensitive LLM applications requiring sub-second latency","developers deploying models on edge devices or resource-constrained infrastructure","researchers studying sparse activation patterns and expert specialization in language models"],"limitations":["Sparse activation may cause inconsistent expert utilization across different input domains, leading to variable performance on out-of-distribution tasks","MoE routing adds computational overhead (~5-10% of inference time) for gating network evaluation before expert selection","Expert load balancing during training can be unstable, potentially causing some experts to remain underutilized or overloaded","Requires inference infrastructure optimized for sparse computation; standard dense tensor operations may not fully leverage efficiency gains"],"requires":["API access via OpenRouter or compatible inference endpoint","Support for sparse tensor operations in inference framework (vLLM, TensorRT-LLM, or equivalent)","Minimum 24GB VRAM for single-GPU deployment, or distributed inference setup for larger deployments"],"input_types":["text (natural language prompts, code snippets, structured queries)","multi-turn conversation history with role-based formatting"],"output_types":["text (natural language responses, code generation, structured completions)","token-level logits and probability distributions (via API extensions)"],"categories":["text-generation-language","foundation-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management.with.role.based.message.formatting","name":"multi-turn conversational context management with role-based message formatting","description":"Maintains and processes multi-turn conversation history by accepting role-based message sequences (system, user, assistant) and maintaining coherent context across exchanges. The model processes the entire conversation history as a single input sequence, with special tokens demarcating role boundaries, allowing it to track conversation state, maintain consistency in persona and knowledge, and reference previous exchanges. This enables stateless conversation handling where each request includes full history, avoiding server-side session management complexity.","intents":["I need to build a chatbot that remembers context across multiple user turns without managing server-side session state","I want to implement system prompts that persist across conversation turns to maintain consistent assistant behavior","I need to handle multi-turn reasoning tasks where the model references and builds upon previous responses"],"best_for":["developers building stateless conversational APIs that scale horizontally","teams implementing chatbot applications with simple deployment requirements","builders prototyping multi-turn reasoning chains without complex session management"],"limitations":["Context window is fixed (likely 4K-8K tokens based on StepFun architecture); conversations exceeding this length require truncation or summarization","Full conversation history must be re-processed on each turn, creating quadratic token consumption as conversations grow longer","No built-in conversation summarization or context compression; developers must implement their own strategies for long conversations","Role-based formatting requires strict adherence to message structure; malformed messages may degrade context understanding"],"requires":["API client supporting message array format with 'role' and 'content' fields","Understanding of token counting to manage context window limits","Mechanism to truncate or summarize conversation history before reaching token limits"],"input_types":["message array with role-based structure: [{role: 'system'|'user'|'assistant', content: string}]","system prompts (optional, typically first message with role='system')"],"output_types":["text response from assistant role","conversation continuation that maintains consistency with prior exchanges"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_10","uri":"capability://text.generation.language.summarization.and.text.compression.with.configurable.detail.levels","name":"summarization and text compression with configurable detail levels","description":"Summarizes long documents or conversations into concise overviews while preserving key information. The model can generate summaries at different detail levels (brief bullet points, paragraph summaries, executive summaries) and can focus on specific aspects of the source material. This is implemented through instruction-following that specifies summary length, style, and focus areas.","intents":["I need to create executive summaries of long documents for quick review","I want to condense conversation histories or meeting notes into key takeaways","I need to extract the most important information from large documents for further processing"],"best_for":["teams managing large volumes of documents or conversations","developers building document management or knowledge management systems","professionals needing quick overviews of lengthy content"],"limitations":["Summarization quality depends on source material clarity; poorly written sources produce poor summaries","Important information may be omitted if it's not prominent in the source material","Very long documents (>8000 tokens) may require chunking and multiple summarization passes","No built-in mechanism to verify that summaries are factually accurate","Summary length control through prompting is approximate; actual length may vary"],"requires":["Source text to summarize","Specification of desired summary length or detail level","Optional: focus areas or key topics to emphasize in summary"],"input_types":["long documents or articles","conversation histories or meeting transcripts","technical documentation or research papers"],"output_types":["bullet-point summaries","paragraph-length summaries","executive summaries with key findings","focused summaries emphasizing specific topics"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates and completes code across multiple programming languages by understanding syntax, semantics, and common patterns. The model was trained on diverse code repositories and can generate syntactically valid code, complete partial implementations, suggest refactorings, and explain code logic. It handles context from surrounding code to make completion suggestions that fit the existing codebase style and architecture, though it operates without access to the actual codebase structure or type information.","intents":["I need to generate boilerplate code or implement common patterns quickly across different languages","I want to get code completion suggestions that understand the context of my current file","I need to understand or refactor existing code snippets with explanations of what they do"],"best_for":["developers using IDE integrations or API-based code completion tools","teams building code generation features into development tools","engineers prototyping or scaffolding new projects quickly"],"limitations":["No access to actual codebase structure, type definitions, or import resolution; suggestions may not account for project-specific conventions or dependencies","Cannot verify generated code against test suites or type checkers; output requires manual validation","Performance degrades for very long code contexts (>2000 lines) due to context window limits","May generate code that compiles but has logical errors or security vulnerabilities; not suitable for critical systems without review","Limited understanding of framework-specific patterns; suggestions may not follow best practices for specialized frameworks"],"requires":["API access to Step 3.5 Flash via OpenRouter or compatible endpoint","Code context provided as text (typically 100-5000 tokens)","IDE or application layer to handle code insertion and formatting"],"input_types":["code snippets (partial or complete files)","natural language descriptions of desired functionality","code comments describing intent"],"output_types":["generated code (syntactically valid for target language)","code completions (continuations of partial implementations)","explanations of code logic and suggestions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_3","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.task.decomposition","name":"reasoning and chain-of-thought task decomposition","description":"Performs multi-step reasoning by generating intermediate thinking steps that break down complex problems into manageable sub-tasks. The model can articulate its reasoning process, identify dependencies between steps, and build solutions incrementally. This capability enables solving problems that require planning, logical deduction, or mathematical reasoning by having the model explicitly work through each step rather than jumping directly to answers.","intents":["I need the model to explain its reasoning for complex decisions or problem-solving tasks","I want to decompose a complex task into steps that can be verified or executed sequentially","I need to solve math problems, logic puzzles, or planning tasks that require step-by-step reasoning"],"best_for":["developers building AI agents that need to justify decisions or explain reasoning","teams implementing task planning systems or workflow automation","educators or researchers studying model reasoning capabilities"],"limitations":["Reasoning quality degrades on tasks requiring specialized domain knowledge not well-represented in training data","Chain-of-thought generation adds latency (typically 2-5x longer response time) and token consumption compared to direct answers","Model may generate plausible-sounding but incorrect intermediate steps; reasoning transparency doesn't guarantee correctness","Very long reasoning chains (>50 steps) may lose coherence or contradict earlier steps","No built-in verification mechanism; intermediate steps require external validation"],"requires":["Prompting strategy that explicitly requests step-by-step reasoning (e.g., 'Let's think step by step')","Sufficient context window to accommodate both reasoning steps and final answer","Post-processing logic to extract and validate intermediate reasoning steps if needed"],"input_types":["complex problems requiring multi-step solutions","questions with implicit dependencies or prerequisites","tasks requiring logical deduction or mathematical reasoning"],"output_types":["step-by-step reasoning traces with intermediate conclusions","final answers with supporting logic","task decompositions showing dependencies and execution order"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_4","uri":"capability://text.generation.language.instruction.following.and.task.adaptation.with.system.prompts","name":"instruction-following and task adaptation with system prompts","description":"Follows detailed instructions and adapts behavior based on system prompts that define role, constraints, output format, and task-specific rules. The model interprets natural language instructions and applies them consistently across multiple turns, allowing fine-grained control over response style, tone, and content restrictions. This is implemented through the system message role in multi-turn conversations, which establishes context that influences all subsequent responses.","intents":["I need to customize the model's behavior for specific use cases (e.g., act as a code reviewer, technical writer, or domain expert)","I want to enforce output format constraints (e.g., JSON, markdown, specific structure) across all responses","I need to implement content policies or safety guardrails by instructing the model to refuse certain requests"],"best_for":["teams building specialized AI assistants for specific domains or use cases","developers implementing content moderation or safety policies through prompting","builders creating white-label AI applications with customizable behavior"],"limitations":["Instruction-following quality degrades with conflicting or ambiguous instructions; model may prioritize earlier instructions over later ones","System prompts don't provide hard guarantees; determined users can often override instructions through clever prompting","Very long or complex system prompts consume context window tokens, reducing space for actual task content","Instruction adherence varies by task type; some domains (e.g., creative writing) show weaker instruction-following than others (e.g., structured data extraction)","No built-in audit trail of which instructions were followed; compliance verification requires post-hoc analysis"],"requires":["Clear, well-written system prompts that specify desired behavior","Understanding of prompt injection risks and mitigation strategies","Testing and validation to verify instruction-following quality for specific use cases"],"input_types":["system prompts (role definitions, constraints, format specifications)","user queries that should be processed according to system instructions"],"output_types":["responses formatted and styled according to system prompt specifications","refusals or redirects for requests violating system-defined constraints"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_5","uri":"capability://memory.knowledge.knowledge.synthesis.and.question.answering.from.context","name":"knowledge synthesis and question-answering from context","description":"Answers questions and synthesizes information by processing provided context (documents, code, data) and extracting relevant information to formulate responses. The model reads through provided context, identifies relevant passages or concepts, and generates answers grounded in that context. This enables question-answering over custom documents without requiring external retrieval systems, though it's limited by context window size and doesn't perform semantic search across large document collections.","intents":["I need to answer questions about specific documents or code without setting up a RAG system","I want to extract and synthesize information from multiple sources provided in a single prompt","I need to verify that answers are grounded in provided context rather than relying on model training data"],"best_for":["developers building document Q&A features without complex infrastructure","teams implementing quick prototypes that need context-aware answers","users analyzing specific documents or code files for information extraction"],"limitations":["Context window limits (likely 4K-8K tokens) restrict the amount of source material that can be processed in a single query","No semantic indexing or retrieval; all provided context is processed equally regardless of relevance to the question","Performance degrades with very long documents; model may lose track of information in early context","Cannot perform cross-document reasoning or synthesis across multiple large documents","No built-in mechanism to cite sources or provide confidence scores for answers"],"requires":["Context provided as text within the same prompt as the question","Context window management to ensure question + context fits within token limits","Mechanism to chunk large documents if they exceed context window"],"input_types":["document text or code snippets (provided as context)","natural language questions about the provided context"],"output_types":["answers grounded in provided context","extracted information or summaries","explanations with references to source material"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_6","uri":"capability://text.generation.language.creative.content.generation.with.style.and.tone.control","name":"creative content generation with style and tone control","description":"Generates creative content (stories, poetry, marketing copy, dialogue) with controllable style and tone through natural language instructions. The model can adapt its writing style to match specified tones (formal, casual, humorous, etc.), genres, and audience levels. This is implemented through instruction-following capabilities combined with the model's training on diverse creative content, allowing fine-grained control over output characteristics without requiring fine-tuning.","intents":["I need to generate marketing copy or creative content in a specific style or tone","I want to create multiple variations of content with different styles or perspectives","I need to generate dialogue, stories, or other creative writing with consistent voice and tone"],"best_for":["content creators and marketers generating variations quickly","teams building AI-assisted creative tools (writing assistants, content generators)","developers implementing personalized content generation with style control"],"limitations":["Creative quality is subjective and varies significantly based on prompt quality and specificity","Model may generate clichéd or generic content without detailed guidance","Longer creative pieces (>2000 tokens) may lose coherence or consistency in voice","No built-in fact-checking; generated content may contain plausible-sounding but false information","Style control through prompting is less precise than fine-tuning; results may not exactly match desired tone"],"requires":["Clear descriptions of desired style, tone, and content characteristics","Examples or reference material to guide the model's output","Manual review and editing of generated content for quality assurance"],"input_types":["prompts describing desired content, style, and tone","reference examples or style guides","topic or subject matter for content generation"],"output_types":["creative text (stories, poetry, marketing copy, dialogue)","multiple variations with different styles or perspectives","content formatted for specific use cases (social media, email, etc.)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_7","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Provides inference access through OpenRouter's API, supporting both streaming responses (token-by-token output) and batch processing modes. Streaming enables real-time response display and lower perceived latency, while batch processing allows efficient processing of multiple requests. The API handles load balancing, rate limiting, and infrastructure management, abstracting away the complexity of running the sparse MoE model directly.","intents":["I need to integrate a capable language model into my application without managing infrastructure","I want to stream responses to users for real-time feedback and lower perceived latency","I need to process multiple requests efficiently with automatic batching and load balancing"],"best_for":["developers building applications that need LLM capabilities without infrastructure expertise","teams requiring scalable inference without managing GPU clusters","builders prototyping or deploying applications that need reliable, managed inference"],"limitations":["API latency adds 100-500ms overhead compared to local inference due to network round-trips","Rate limiting and quota restrictions may apply based on pricing tier","Streaming responses consume tokens at the same rate as non-streaming; no token savings","API availability depends on OpenRouter's infrastructure; no SLA guarantees for free tier","Data sent to API is subject to OpenRouter's privacy policy; not suitable for highly sensitive information"],"requires":["OpenRouter API key (free or paid account)","HTTP client library supporting streaming (e.g., requests, httpx, fetch)","Network connectivity to OpenRouter's API endpoints","Understanding of token counting for cost estimation"],"input_types":["text prompts and conversation histories","batch requests with multiple prompts"],"output_types":["streamed text responses (token-by-token)","complete responses with metadata (tokens used, finish reason)","batch processing results with per-request status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_8","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.generation","name":"structured data extraction and json generation","description":"Extracts structured information from unstructured text and generates valid JSON output by understanding schema requirements and formatting constraints. The model can parse natural language descriptions, identify relevant entities and relationships, and output them in specified JSON structures. This enables programmatic processing of model outputs without requiring post-hoc parsing or validation.","intents":["I need to extract structured data (entities, relationships, attributes) from documents or text","I want to generate JSON responses that conform to specific schemas for downstream processing","I need to convert natural language descriptions into structured formats for database insertion or API calls"],"best_for":["developers building data extraction pipelines or ETL workflows","teams implementing structured output requirements for LLM applications","builders creating APIs that need to return structured data from unstructured inputs"],"limitations":["JSON generation quality depends on schema clarity; ambiguous schemas produce inconsistent output","Model may generate syntactically valid JSON that violates semantic constraints (e.g., invalid enum values)","Very complex schemas (>20 fields with nested objects) may exceed model's ability to maintain consistency","No built-in validation; generated JSON requires schema validation before use","Extraction accuracy varies by domain; specialized domains may require fine-tuning for reliable results"],"requires":["Clear JSON schema or format specification in the prompt","Examples of desired output format to guide the model","JSON validation logic to verify generated output before processing","Error handling for malformed JSON responses"],"input_types":["unstructured text or documents to extract from","JSON schema or format specifications","examples of desired output format"],"output_types":["valid JSON objects conforming to specified schemas","arrays of structured records","nested JSON structures with relationships"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-stepfun-step-3.5-flash__cap_9","uri":"capability://text.generation.language.translation.and.multilingual.text.generation","name":"translation and multilingual text generation","description":"Translates text between multiple languages and generates content in non-English languages with appropriate grammar, idioms, and cultural context. The model understands linguistic nuances and can maintain meaning and tone across language boundaries. This capability extends to code comments, technical documentation, and creative content in multiple languages.","intents":["I need to translate content into multiple languages for international audiences","I want to generate content directly in non-English languages without translating from English","I need to maintain technical accuracy and terminology consistency across language translations"],"best_for":["teams building multilingual applications or content platforms","developers implementing localization features for global products","content creators and marketers targeting international audiences"],"limitations":["Translation quality varies significantly by language pair; less common languages may have lower accuracy","Idioms and cultural context may not translate perfectly; human review recommended for marketing content","Technical terminology may not translate consistently across documents; requires glossary management","Very long documents may lose consistency in terminology or tone across sections","No built-in quality metrics; translation accuracy requires manual verification"],"requires":["Source text in supported language","Target language specification","Optional: glossary or terminology guide for consistent technical translation","Manual review process for quality assurance"],"input_types":["text in any supported language","code with comments and documentation","technical or creative content requiring translation"],"output_types":["translated text in target language","multilingual content generation","localized documentation and guides"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible inference endpoint","Support for sparse tensor operations in inference framework (vLLM, TensorRT-LLM, or equivalent)","Minimum 24GB VRAM for single-GPU deployment, or distributed inference setup for larger deployments","API client supporting message array format with 'role' and 'content' fields","Understanding of token counting to manage context window limits","Mechanism to truncate or summarize conversation history before reaching token limits","Source text to summarize","Specification of desired summary length or detail level","Optional: focus areas or key topics to emphasize in summary","API access to Step 3.5 Flash via OpenRouter or compatible endpoint"],"failure_modes":["Sparse activation may cause inconsistent expert utilization across different input domains, leading to variable performance on out-of-distribution tasks","MoE routing adds computational overhead (~5-10% of inference time) for gating network evaluation before expert selection","Expert load balancing during training can be unstable, potentially causing some experts to remain underutilized or overloaded","Requires inference infrastructure optimized for sparse computation; standard dense tensor operations may not fully leverage efficiency gains","Context window is fixed (likely 4K-8K tokens based on StepFun architecture); conversations exceeding this length require truncation or summarization","Full conversation history must be re-processed on each turn, creating quadratic token consumption as conversations grow longer","No built-in conversation summarization or context compression; developers must implement their own strategies for long conversations","Role-based formatting requires strict adherence to message structure; malformed messages may degrade context understanding","Summarization quality depends on source material clarity; poorly written sources produce poor summaries","Important information may be omitted if it's not prominent in the source material","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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=stepfun-step-3.5-flash","compare_url":"https://unfragile.ai/compare?artifact=stepfun-step-3.5-flash"}},"signature":"RmP9naTiSOEmXDksD0qz56IB1JlWOZtCltADNCiFKQZzaQFJChHHuKv5SgpTDho4+hxE73uDM0n+zm1xJYpFCg==","signedAt":"2026-06-20T19:56:28.977Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stepfun-step-3.5-flash","artifact":"https://unfragile.ai/stepfun-step-3.5-flash","verify":"https://unfragile.ai/api/v1/verify?slug=stepfun-step-3.5-flash","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"}}