{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-oss-120b","slug":"openai-gpt-oss-120b","name":"OpenAI: gpt-oss-120b","type":"model","url":"https://openrouter.ai/models/openai~gpt-oss-120b","page_url":"https://unfragile.ai/openai-gpt-oss-120b","categories":["deployment-infra"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.90e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-oss-120b__cap_0","uri":"capability://planning.reasoning.mixture.of.experts.reasoning.with.sparse.activation","name":"mixture-of-experts reasoning with sparse activation","description":"Implements a 117B-parameter Mixture-of-Experts architecture that activates only 5.1B parameters per forward pass, routing input tokens to specialized expert subnetworks based on learned gating functions. This sparse activation pattern reduces computational cost while maintaining model capacity for complex reasoning tasks, using a load-balancing mechanism to distribute tokens across experts and prevent collapse to a single dominant expert.","intents":["I need a model that can handle complex reasoning tasks without the full computational cost of a dense 117B model","I want to deploy a high-capacity model with lower inference latency and reduced memory footprint","I need a production-grade model optimized for agentic decision-making with efficient token routing"],"best_for":["teams building production AI agents requiring high reasoning capability with cost efficiency","enterprises deploying large language models where inference latency and compute cost are critical","developers building multi-step reasoning systems that need to scale across many concurrent requests"],"limitations":["MoE models exhibit higher variance in latency due to dynamic expert routing — some token sequences may route to computationally expensive expert combinations","Expert specialization can create imbalanced load distribution if gating function is not properly tuned, leading to underutilized experts","Requires sufficient batch size to amortize expert routing overhead; single-token inference may not see full efficiency gains","Memory footprint still requires loading all 117B parameters into VRAM even though only 5.1B are active per step"],"requires":["OpenAI API key or OpenRouter API key with gpt-oss-120b model access","HTTP/2 capable client library (OpenAI Python SDK 1.0+, Node.js 16+, or equivalent)","Sufficient context window support (model supports standard 128K token context)"],"input_types":["text (natural language prompts, code snippets, structured instructions)"],"output_types":["text (natural language responses, code, reasoning chains, structured completions)"],"categories":["planning-reasoning","language-model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_1","uri":"capability://planning.reasoning.agentic.multi.step.reasoning.and.tool.orchestration","name":"agentic multi-step reasoning and tool orchestration","description":"Supports structured reasoning chains where the model can decompose complex tasks into intermediate steps, make decisions about which tools or functions to invoke, and iteratively refine outputs based on tool results. The model is trained to generate reasoning tokens that explicitly show its decision-making process, enabling transparent multi-turn agent loops where each step's output feeds into the next step's input, with native support for function calling schemas and structured output formatting.","intents":["I need a model that can break down complex user requests into multiple reasoning steps and decide which tools to call at each step","I want to build an autonomous agent that can plan, execute, and adapt its strategy based on intermediate results","I need transparent reasoning traces from my AI system so I can audit and debug agent decision-making"],"best_for":["AI engineers building autonomous agents for research, code generation, or data analysis workflows","teams implementing ReAct (Reasoning + Acting) patterns where models must decide between thinking and tool invocation","enterprises requiring explainable AI where reasoning steps must be auditable and transparent"],"limitations":["Reasoning token generation increases latency by 30-50% compared to direct answer generation, as model must explicitly verbalize intermediate steps","Tool orchestration requires well-defined function schemas; ambiguous or poorly-specified tool definitions lead to incorrect invocations","Multi-step reasoning can accumulate errors — mistakes in early reasoning steps propagate through subsequent steps without automatic correction","Context window fills quickly with reasoning traces; complex multi-step tasks may exceed 128K token limit"],"requires":["OpenAI API key with function calling support enabled","Structured function schema definitions (JSON Schema format)","Client library supporting streaming for real-time reasoning token visibility (OpenAI Python SDK 1.0+)"],"input_types":["text (natural language task descriptions, user queries)","structured function schemas (JSON Schema defining available tools)"],"output_types":["text (reasoning chains, intermediate thoughts)","structured function calls (tool invocations with parameters)","final answers (synthesized from tool results)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_2","uri":"capability://memory.knowledge.long.context.semantic.understanding.with.128k.token.window","name":"long-context semantic understanding with 128k token window","description":"Processes up to 128,000 tokens in a single context window, enabling the model to maintain coherent understanding across entire documents, codebases, or multi-turn conversations without losing semantic relationships between distant parts of the input. Uses efficient attention mechanisms (likely sparse or linear attention variants optimized for MoE) to handle long sequences while maintaining the reasoning capability needed for complex analysis across the full context.","intents":["I need to analyze entire source code files or repositories without splitting them into chunks","I want to maintain conversation history across 50+ turns without losing context about earlier discussion points","I need to extract insights from long documents (research papers, legal contracts, technical specifications) while preserving cross-document relationships"],"best_for":["developers analyzing large codebases for refactoring, security audits, or architectural decisions","researchers processing long-form documents and requiring semantic understanding across entire papers","customer support teams maintaining multi-turn conversations with full context of previous interactions"],"limitations":["Attention computation scales quadratically with sequence length in standard implementations; even with optimizations, 128K tokens incurs 10-15x latency vs. 4K token context","Model may dilute attention across very long contexts, reducing focus on most relevant information — requires careful prompt engineering to highlight key sections","Cost scales linearly with token count; processing 128K tokens costs ~30x more than 4K token context, making it expensive for high-volume applications","Long context can introduce hallucinations if model conflates information from distant parts of input"],"requires":["OpenAI API key with extended context support","Client library supporting streaming (recommended for latency visibility)","Sufficient API rate limits to handle longer token counts"],"input_types":["text (long documents, code files, conversation histories, concatenated sources)"],"output_types":["text (analysis, summaries, answers grounded in full context)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_3","uri":"capability://code.generation.editing.code.generation.and.multi.language.programming.support","name":"code generation and multi-language programming support","description":"Generates syntactically correct and semantically sound code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.), with understanding of language-specific idioms, frameworks, and best practices. The model is trained on diverse code repositories and can generate complete functions, classes, or multi-file solutions, with support for generating code that integrates with popular libraries and frameworks. Includes capability to understand existing code context and generate compatible additions or refactorings.","intents":["I need to generate boilerplate code or complete functions in multiple programming languages from natural language descriptions","I want to generate code that integrates with specific frameworks (React, Django, FastAPI) or libraries (NumPy, Pandas)","I need to understand and refactor existing code while maintaining compatibility with the broader codebase"],"best_for":["full-stack developers accelerating development across multiple languages and frameworks","teams with polyglot codebases requiring consistent code generation across different tech stacks","developers learning new languages or frameworks who need generated examples with correct idioms"],"limitations":["Generated code may contain subtle bugs or security vulnerabilities; all generated code requires human review before production deployment","Model may generate code using outdated library versions or deprecated APIs if training data is not recent","Complex multi-file refactoring may lose consistency across files; model cannot guarantee referential integrity across generated code","Performance-critical code generation often requires manual optimization; model-generated code prioritizes correctness over efficiency"],"requires":["OpenAI API key","Code context (existing files or function signatures) for better generation quality","IDE or editor integration for streaming code generation (optional but recommended)"],"input_types":["text (natural language code descriptions, function signatures, comments)","code (existing code context, file snippets, function bodies to complete)"],"output_types":["code (generated functions, classes, complete files, multi-file solutions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_4","uri":"capability://data.processing.analysis.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Reliably follows complex, multi-part instructions and generates output in specified structured formats (JSON, XML, YAML, CSV, Markdown tables) with high consistency. The model is trained to parse instruction hierarchies, handle conditional logic (if-then patterns), and generate output that strictly adheres to specified schemas or templates. Supports both explicit format requests (e.g., 'output as JSON') and implicit format inference from examples provided in the prompt.","intents":["I need to extract structured data from unstructured text and output it in a specific format (JSON, CSV, XML)","I want to generate responses that follow a specific template or schema without manual post-processing","I need to batch process multiple requests with consistent output formatting for downstream systems"],"best_for":["data engineers building ETL pipelines that consume model outputs as structured data","teams building LLM-powered APIs that need deterministic output formats for client integration","developers automating content generation workflows where output must conform to specific schemas"],"limitations":["Structured output generation can fail for complex nested schemas; deeply nested JSON or XML may have malformed closing tags","Model may hallucinate fields or values to complete a schema, even if source data doesn't contain the information","Output validation still requires post-processing to ensure schema compliance; model-generated JSON may have syntax errors","Instruction-following degrades with very long or ambiguous instruction sets; clarity and specificity of instructions directly impact output quality"],"requires":["OpenAI API key","Clear format specification in prompt (JSON schema, XML template, or example output)","JSON schema validation library for post-processing (optional but recommended)"],"input_types":["text (unstructured data, instructions, format specifications)","structured examples (sample outputs showing desired format)"],"output_types":["structured text (JSON, XML, YAML, CSV, Markdown tables)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_5","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batching.support","name":"api-based inference with streaming and batching support","description":"Provides inference through OpenAI's REST API with support for both streaming (real-time token-by-token output) and batch processing (asynchronous processing of multiple requests). Streaming mode returns tokens as they are generated, enabling real-time user feedback and progressive rendering in applications. Batch mode accepts multiple requests in a single API call, optimizing throughput for non-latency-sensitive workloads and reducing per-request overhead through request consolidation.","intents":["I need real-time streaming responses for interactive chat applications where users see tokens appear as they're generated","I want to process thousands of requests efficiently without making individual API calls for each one","I need to integrate model inference into my application with standard HTTP APIs and minimal infrastructure"],"best_for":["web application developers building chat interfaces requiring real-time token streaming","data teams processing large datasets through the model for batch inference and cost optimization","startups and small teams avoiding infrastructure overhead by using managed API inference"],"limitations":["Streaming mode incurs higher per-token latency due to HTTP overhead for each token transmission; not suitable for latency-critical applications","Batch processing introduces variable latency (hours to days depending on queue); unsuitable for real-time applications","API rate limits constrain throughput; high-volume applications may hit rate limits and require quota increases","Streaming responses cannot be easily cached or reused; each request generates unique token sequences","Network latency and API availability become critical dependencies; outages directly impact application availability"],"requires":["OpenAI API key with gpt-oss-120b model access","HTTP/2 capable client (OpenAI Python SDK 1.0+, Node.js 16+, or equivalent)","Network connectivity to OpenAI API endpoints","Handling for streaming responses (event-stream parsing) if using streaming mode"],"input_types":["text (prompts, messages, instructions)"],"output_types":["text (streamed tokens or complete responses)","metadata (token counts, finish reasons, usage statistics)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_6","uri":"capability://text.generation.language.multilingual.understanding.and.generation","name":"multilingual understanding and generation","description":"Understands and generates text in 50+ languages with reasonable fluency, including major languages (Spanish, French, German, Mandarin, Japanese, Arabic) and many lower-resource languages. The model maintains semantic understanding across language boundaries and can perform tasks like translation, cross-lingual information retrieval, and multilingual summarization. Uses language-agnostic tokenization and embedding spaces to handle diverse character sets and linguistic structures.","intents":["I need to build a chatbot that serves users in multiple languages without separate models for each language","I want to translate content between languages while preserving semantic meaning and context","I need to analyze or summarize documents in multiple languages simultaneously"],"best_for":["global companies serving users across multiple countries and language regions","teams building multilingual content platforms or customer support systems","researchers working with multilingual datasets or cross-lingual NLP tasks"],"limitations":["Quality varies significantly across languages; high-resource languages (English, Spanish, French) have better quality than low-resource languages","Multilingual models may experience language interference where one language's patterns affect generation in another language","Character encoding and tokenization differences across languages can lead to inconsistent token efficiency (some languages require 2-3x more tokens than English)","Cultural and linguistic nuances may be lost in translation; idioms and context-specific meanings don't always transfer across languages"],"requires":["OpenAI API key","UTF-8 text encoding for input (supports all Unicode characters)","Language specification in prompt (optional but recommended for better quality)"],"input_types":["text (in any of 50+ supported languages)"],"output_types":["text (in requested language or inferred from input)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_7","uri":"capability://text.generation.language.context.aware.conversation.with.multi.turn.memory","name":"context-aware conversation with multi-turn memory","description":"Maintains coherent conversation state across multiple turns, where each response is informed by the full conversation history and previous context. The model tracks entities, relationships, and discussion topics across turns, enabling natural follow-up questions and references to earlier statements without explicit re-specification. Uses attention mechanisms to weight recent context more heavily while still maintaining awareness of earlier conversation points, with support for explicit context management through system prompts and conversation summaries.","intents":["I need to build a chatbot that understands context from previous messages and can answer follow-up questions naturally","I want to maintain conversation state across multiple turns without losing track of earlier discussion points","I need to handle complex conversations where users reference earlier statements or ask clarifying questions"],"best_for":["customer support teams building conversational AI that needs to understand customer history and context","developers building interactive tutoring systems where conversation context affects learning outcomes","teams building personal assistants or agents that maintain long-term conversation context"],"limitations":["Context window fills quickly with multi-turn conversations; after 50+ turns, earlier context may be pushed out or deprioritized","Model may conflate information from different conversation branches if user changes topics and returns to earlier topics","Conversation summaries (used to compress context) may lose important details or nuances from original conversation","Long conversation histories increase latency and cost; each new message requires processing the entire history"],"requires":["OpenAI API key","Message history management (client-side or server-side storage of conversation turns)","Conversation context formatting (typically as array of messages with roles: system, user, assistant)"],"input_types":["text (user messages, conversation history)"],"output_types":["text (contextually-aware responses)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-120b__cap_8","uri":"capability://memory.knowledge.knowledge.cutoff.and.training.data.awareness","name":"knowledge cutoff and training data awareness","description":"Model has a training data cutoff date (typically April 2024 or later based on OpenAI's release patterns) and is aware of its knowledge limitations. The model can acknowledge when information is outside its training data and can be prompted to reason about recent events using provided context. Does not have real-time internet access but can be augmented with retrieval-augmented generation (RAG) systems to access current information.","intents":["I need to understand what information the model has access to and when its knowledge was last updated","I want to augment the model with current information through RAG or context injection for recent events","I need to build systems that gracefully handle questions about information outside the model's training data"],"best_for":["teams building applications that need current information and plan to implement RAG systems","developers building systems that must acknowledge knowledge limitations and handle out-of-distribution queries","researchers studying model knowledge cutoffs and temporal reasoning"],"limitations":["No real-time internet access; cannot fetch current information without external systems","Knowledge cutoff means model may provide outdated information for rapidly-changing domains (news, stock prices, scientific discoveries)","Model may hallucinate recent events if prompted about current information without providing context","RAG augmentation requires external knowledge sources and retrieval infrastructure; adds latency and complexity"],"requires":["OpenAI API key","External knowledge sources (for RAG augmentation) if current information is needed","Prompt engineering to specify knowledge cutoff and request context-based reasoning"],"input_types":["text (queries about knowledge cutoff, requests for reasoning about recent events with provided context)"],"output_types":["text (acknowledgments of knowledge limitations, context-based reasoning)"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or OpenRouter API key with gpt-oss-120b model access","HTTP/2 capable client library (OpenAI Python SDK 1.0+, Node.js 16+, or equivalent)","Sufficient context window support (model supports standard 128K token context)","OpenAI API key with function calling support enabled","Structured function schema definitions (JSON Schema format)","Client library supporting streaming for real-time reasoning token visibility (OpenAI Python SDK 1.0+)","OpenAI API key with extended context support","Client library supporting streaming (recommended for latency visibility)","Sufficient API rate limits to handle longer token counts","OpenAI API key"],"failure_modes":["MoE models exhibit higher variance in latency due to dynamic expert routing — some token sequences may route to computationally expensive expert combinations","Expert specialization can create imbalanced load distribution if gating function is not properly tuned, leading to underutilized experts","Requires sufficient batch size to amortize expert routing overhead; 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