{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-allenai-olmo-3.1-32b-instruct","slug":"allenai-olmo-3.1-32b-instruct","name":"AllenAI: Olmo 3.1 32B Instruct","type":"model","url":"https://openrouter.ai/models/allenai~olmo-3.1-32b-instruct","page_url":"https://unfragile.ai/allenai-olmo-3.1-32b-instruct","categories":["chatbots-assistants"],"tags":["allenai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_0","uri":"capability://text.generation.language.multi.turn.instruction.following.dialogue","name":"multi-turn instruction-following dialogue","description":"Processes sequential conversational exchanges with instruction-tuned weights optimized for following complex, multi-step directives across conversation turns. The model maintains coherence across dialogue context by leveraging transformer attention mechanisms trained on instruction-following datasets, enabling it to parse user intent, track conversation state, and respond with contextually appropriate actions without explicit state management from the caller.","intents":["Build a conversational AI assistant that understands multi-step user requests across multiple turns","Create a chatbot that can follow complex instructions embedded in natural language dialogue","Develop an interactive agent that maintains conversation context while executing user directives"],"best_for":["Teams building conversational AI products requiring instruction adherence","Developers creating multi-turn dialogue systems without custom fine-tuning","Startups prototyping chatbot MVPs with minimal infrastructure overhead"],"limitations":["Context window limited to model's training sequence length (typically 4K-8K tokens); longer conversations require external conversation management","No persistent memory across separate conversation sessions — each new session starts without prior dialogue history","Instruction-following quality degrades on highly domain-specific or proprietary instruction formats not seen during training"],"requires":["API key for OpenRouter or direct provider access","HTTP client capable of streaming or polling responses","Conversation state management layer if maintaining context beyond single API call"],"input_types":["text (natural language instructions and dialogue)"],"output_types":["text (natural language responses)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_1","uri":"capability://text.generation.language.zero.shot.task.generalization.across.domains","name":"zero-shot task generalization across domains","description":"Applies learned patterns from instruction-tuning to unseen task types without domain-specific fine-tuning or few-shot examples. The model leverages transformer-based in-context learning to infer task structure from natural language prompts, enabling it to handle novel problem classes (summarization, translation, question-answering, creative writing) by recognizing task semantics and applying appropriate reasoning patterns learned during pretraining and instruction-tuning.","intents":["Use a single model API for multiple task types without building separate specialized models","Quickly prototype solutions for new problem domains without collecting training data","Build flexible AI pipelines that adapt to user-specified tasks via natural language prompts"],"best_for":["Product teams needing a general-purpose model for diverse user tasks","Developers building no-code/low-code AI platforms with dynamic task routing","Researchers evaluating model generalization across task families"],"limitations":["Performance on highly specialized domains (medical diagnosis, legal analysis) may be lower than domain-specific fine-tuned models","Task performance varies significantly based on prompt clarity — ambiguous instructions lead to inconsistent outputs","No built-in task classification — caller must determine which task type to invoke or rely on model's inference"],"requires":["Clear, well-structured natural language task descriptions in prompts","Understanding of model's training data distribution to set realistic performance expectations"],"input_types":["text (task description and input data)"],"output_types":["text (task-specific output)"],"categories":["text-generation-language","task-generalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_10","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.solving","name":"reasoning and step-by-step problem solving","description":"Solves complex problems by generating intermediate reasoning steps (chain-of-thought) before producing final answers. The model's instruction-tuning on reasoning tasks enables it to interpret prompts requesting step-by-step explanations and generate coherent reasoning chains that decompose problems into sub-steps, improving accuracy on multi-step reasoning tasks compared to direct answer generation without explicit reasoning.","intents":["Solve math problems or logic puzzles by showing work and intermediate steps","Debug code by reasoning through execution flow and identifying error sources","Explain decision-making processes in complex scenarios with step-by-step justification"],"best_for":["Educational platforms teaching problem-solving with AI-generated explanations","Debugging tools that explain code logic and error sources","Decision-support systems requiring transparent reasoning"],"limitations":["Reasoning quality degrades on problems requiring specialized domain knowledge (advanced mathematics, physics)","Step-by-step reasoning increases token usage and latency compared to direct answers","Model may generate plausible-sounding but incorrect reasoning steps (reasoning hallucination)"],"requires":["Explicit prompt requesting step-by-step reasoning (e.g., 'solve step-by-step')","Problem specification clear enough for multi-step decomposition","Higher token budget to accommodate reasoning steps"],"input_types":["text (problem specification + reasoning request)"],"output_types":["text (reasoning steps + final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_2","uri":"capability://text.generation.language.streaming.token.generation.with.latency.optimization","name":"streaming token generation with latency optimization","description":"Generates text tokens sequentially via streaming API, returning partial responses as they become available rather than waiting for full completion. This is implemented through OpenRouter's streaming endpoint integration, which uses server-sent events (SSE) or chunked HTTP transfer encoding to deliver tokens incrementally, enabling real-time UI updates and perceived responsiveness improvements while the model continues inference on the backend.","intents":["Display real-time text generation in chat interfaces without waiting for full response completion","Reduce perceived latency in conversational AI by showing tokens as they're generated","Build interactive applications where users see model output appearing progressively"],"best_for":["Frontend developers building chat UIs requiring real-time token display","Teams optimizing perceived latency in conversational products","Builders creating streaming-first applications (e.g., AI writing assistants)"],"limitations":["Streaming adds complexity to error handling — partial responses may be displayed before failure detection","Token-by-token streaming increases HTTP overhead compared to single-request completion; not beneficial for latency-sensitive batch processing","Client must implement buffer management to handle variable token arrival rates and network jitter"],"requires":["HTTP client with streaming/SSE support (e.g., fetch API with ReadableStream, axios with responseType: 'stream')","OpenRouter API key with streaming endpoint access","Frontend state management to accumulate streamed tokens into coherent text"],"input_types":["text (prompt)"],"output_types":["text stream (tokens delivered incrementally)"],"categories":["text-generation-language","streaming-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_3","uri":"capability://text.generation.language.context.aware.response.generation.with.conversation.history","name":"context-aware response generation with conversation history","description":"Generates responses that incorporate full conversation history as context, using the transformer's attention mechanism to weight relevant prior messages when producing new tokens. The model processes the entire conversation thread (user messages, assistant responses, system prompts) as a single sequence, allowing it to reference earlier statements, maintain consistency with prior commitments, and adapt tone/style based on conversation evolution without explicit conversation state management.","intents":["Build chatbots that remember and reference earlier parts of the conversation","Create assistants that maintain consistent personality and commitments across multi-turn exchanges","Develop dialogue systems where context from 5+ turns back influences current response generation"],"best_for":["Teams building customer support chatbots requiring conversation continuity","Developers creating personal AI assistants with long-running conversations","Product builders needing context-aware responses without external memory systems"],"limitations":["Context window is finite (typically 4K-8K tokens) — conversations exceeding this limit require truncation or summarization strategies","Attention mechanism has quadratic complexity with sequence length; very long conversations (100+ turns) may cause latency degradation","No explicit conversation summarization — model must compress all history into token budget, potentially losing fine-grained details from early turns"],"requires":["Caller must format conversation history as message array (e.g., [{role: 'user', content: '...'}, {role: 'assistant', content: '...'}])","Token counting logic to ensure conversation + new prompt fits within context window","Strategy for handling context overflow (e.g., summarization, sliding window, or conversation reset)"],"input_types":["text (conversation history as structured messages)"],"output_types":["text (context-aware response)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_4","uri":"capability://text.generation.language.structured.output.generation.with.format.constraints","name":"structured output generation with format constraints","description":"Generates text constrained to specific formats (JSON, XML, YAML, CSV) by leveraging instruction-tuning and prompt engineering to bias the model toward producing well-formed structured data. While not using hard constraints (like token-level masking), the model's training on structured data examples and instruction-following enables it to reliably produce parseable output when prompted with format specifications, enabling downstream parsing and programmatic consumption without custom validation layers.","intents":["Extract structured data from unstructured text (e.g., 'extract person name, age, email as JSON')","Generate configuration files or data formats programmatically via natural language prompts","Build pipelines where model output feeds directly into JSON parsers or data loaders"],"best_for":["Developers building data extraction pipelines without custom NER/entity extraction models","Teams generating structured outputs (configs, API payloads) from natural language specifications","Builders creating no-code data transformation tools"],"limitations":["No hard format guarantees — model may occasionally produce malformed JSON/XML requiring fallback parsing or retry logic","Format adherence degrades on complex nested structures or ambiguous data; simple flat structures (single-level JSON) are more reliable","Requires explicit format instructions in prompt; implicit format expectations often fail"],"requires":["Clear format specification in prompt (e.g., 'respond with valid JSON matching schema: {...}')","JSON schema or format examples in few-shot examples for complex structures","Robust error handling and retry logic for malformed outputs"],"input_types":["text (unstructured input + format specification)"],"output_types":["text (structured format: JSON, XML, YAML, CSV)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_5","uri":"capability://code.generation.editing.code.generation.and.explanation","name":"code generation and explanation","description":"Generates executable code snippets and explanations in multiple programming languages (Python, JavaScript, Java, C++, etc.) by leveraging instruction-tuning on code datasets and code-explanation pairs. The model understands code semantics, syntax rules, and common patterns, enabling it to produce functional code from natural language specifications and explain existing code logic without requiring language-specific fine-tuning or external code analysis tools.","intents":["Generate boilerplate code or utility functions from natural language descriptions","Explain how existing code works to developers unfamiliar with the codebase","Translate code between programming languages or refactor code for readability"],"best_for":["Developers using AI-assisted coding for rapid prototyping and boilerplate generation","Teams building code documentation or explanation features","Educators creating interactive coding tutorials with AI-generated explanations"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance on domain-specific code (low-level systems programming, specialized ML frameworks) is lower than on common patterns","No real-time compilation feedback — model cannot verify generated code executes correctly"],"requires":["Clear specification of desired code behavior in natural language","Context about target language, framework, and coding style preferences","Human code review and testing before deployment"],"input_types":["text (natural language code specification or existing code to explain)"],"output_types":["text (code snippets in target language, explanations)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_6","uri":"capability://text.generation.language.creative.content.generation.with.style.control","name":"creative content generation with style control","description":"Generates creative text (stories, poetry, marketing copy, dialogue) with style and tone control through instruction-based prompting. The model's instruction-tuning enables it to interpret style descriptors ('write in the style of Hemingway', 'use a sarcastic tone', 'target audience: teenagers') and apply them consistently throughout generated content by leveraging learned associations between style descriptors and linguistic patterns from training data.","intents":["Generate marketing copy or product descriptions with brand voice consistency","Create creative writing (stories, poetry) with specified style or genre","Produce dialogue for characters with distinct personalities and speech patterns"],"best_for":["Content creators and marketers using AI for rapid content ideation","Game developers generating NPC dialogue and narrative content","Agencies producing bulk creative content with style consistency"],"limitations":["Style control is probabilistic — same prompt may produce variable style adherence across runs; requires sampling/temperature tuning","Originality is limited by training data — generated content may closely resemble training examples, raising copyright concerns","Long-form content (1000+ words) may lose style consistency or narrative coherence in later sections"],"requires":["Clear style/tone descriptors in prompt (e.g., 'write in a humorous, conversational tone')","Optional: style examples or reference texts to anchor style expectations","Temperature/sampling parameter tuning to balance creativity vs. consistency"],"input_types":["text (content specification + style descriptors)"],"output_types":["text (creative content)"],"categories":["text-generation-language","creative-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_7","uri":"capability://text.generation.language.question.answering.with.source.grounding","name":"question-answering with source grounding","description":"Answers questions about provided text or documents by processing the source material as context and generating answers grounded in that content. The model uses attention mechanisms to identify relevant passages and synthesize answers from multiple source locations, enabling it to provide cited or source-aware responses without requiring external retrieval systems or explicit passage ranking — though without explicit citation mechanisms, grounding is implicit in the model's reasoning.","intents":["Build QA systems over documents or knowledge bases without external retrieval infrastructure","Create FAQ assistants that answer questions based on provided documentation","Develop reading comprehension features that extract and synthesize information from source texts"],"best_for":["Teams building document-based QA systems with limited infrastructure","Developers creating customer support bots over knowledge bases","Educators building interactive reading comprehension tools"],"limitations":["Context window limits source material size — documents exceeding 4K-8K tokens require chunking or summarization","No explicit citation mechanism — answers are grounded implicitly; model may hallucinate details not in source","Performance degrades on questions requiring reasoning across multiple distant passages or implicit inference"],"requires":["Source document or context provided in prompt","Question phrased clearly in natural language","Token budget accounting for source + question + answer"],"input_types":["text (source document + question)"],"output_types":["text (answer grounded in source)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_8","uri":"capability://text.generation.language.summarization.with.length.and.style.control","name":"summarization with length and style control","description":"Condenses long text into summaries of specified length and style by interpreting natural language summarization instructions ('summarize in 3 bullet points', 'create an executive summary', 'extract key facts'). The model identifies salient information through attention mechanisms and generates concise output while respecting length constraints and style preferences learned during instruction-tuning on diverse summarization tasks.","intents":["Generate executive summaries of long documents for quick consumption","Create bullet-point summaries of articles or reports","Produce abstractive summaries in specific formats (bullet points, paragraphs, key facts)"],"best_for":["Content platforms summarizing user-generated content or news articles","Enterprise tools generating document summaries for knowledge workers","Researchers analyzing large document collections"],"limitations":["Abstractive summarization may omit important details or introduce subtle inaccuracies","Length constraints are soft (probabilistic) — model may exceed specified length; requires post-processing truncation","Performance on domain-specific documents (legal, medical) may be lower than domain-specific summarization models"],"requires":["Source text to summarize","Clear length specification (e.g., '3 bullet points', '100 words')","Optional: style preference (e.g., 'executive summary', 'key facts')"],"input_types":["text (source document + summarization instruction)"],"output_types":["text (summary)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3.1-32b-instruct__cap_9","uri":"capability://text.generation.language.translation.with.context.awareness","name":"translation with context awareness","description":"Translates text between languages while maintaining context, tone, and domain-specific terminology through instruction-tuning on translation pairs and multilingual data. The model leverages cross-lingual attention patterns to preserve meaning across language boundaries and can interpret translation instructions ('translate to Spanish, maintaining formal tone') to apply style constraints during translation without requiring separate language-specific models.","intents":["Translate user-generated content or documents across multiple language pairs","Build multilingual products with dynamic translation of UI text or content","Create localized versions of content with tone and style preservation"],"best_for":["Global product teams needing dynamic translation without separate translation services","Content platforms serving multilingual audiences","Developers building internationalization features"],"limitations":["Translation quality varies by language pair — high-resource pairs (English-Spanish) are more accurate than low-resource pairs (English-Amharic)","Domain-specific terminology may be mistranslated without explicit terminology dictionaries or fine-tuning","Idioms and cultural references may not translate naturally; requires human review for high-stakes content"],"requires":["Source text in supported language","Target language specification","Optional: tone/style preferences or terminology guidance"],"input_types":["text (source text + target language)"],"output_types":["text (translated text)"],"categories":["text-generation-language","translation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct provider access","HTTP client capable of streaming or polling responses","Conversation state management layer if maintaining context beyond single API call","Clear, well-structured natural language task descriptions in prompts","Understanding of model's training data distribution to set realistic performance expectations","Explicit prompt requesting step-by-step reasoning (e.g., 'solve step-by-step')","Problem specification clear enough for multi-step decomposition","Higher token budget to accommodate reasoning steps","HTTP client with streaming/SSE support (e.g., fetch API with ReadableStream, axios with responseType: 'stream')","OpenRouter API key with streaming endpoint access"],"failure_modes":["Context window limited to model's training sequence length (typically 4K-8K tokens); longer conversations require external conversation management","No persistent memory across separate conversation sessions — each new session starts without prior dialogue history","Instruction-following quality degrades on highly domain-specific or proprietary instruction formats not seen during training","Performance on highly specialized domains (medical diagnosis, legal analysis) may be lower than domain-specific fine-tuned models","Task performance varies significantly based on prompt clarity — ambiguous instructions lead to inconsistent outputs","No built-in task classification — caller must determine which task type to invoke or rely on model's inference","Reasoning quality degrades on problems requiring specialized domain knowledge (advanced mathematics, physics)","Step-by-step reasoning increases token usage and latency compared to direct answers","Model may generate plausible-sounding but incorrect reasoning steps (reasoning hallucination)","Streaming adds complexity to error handling — partial responses may be displayed before failure detection","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":"2026-05-24T12:16:24.483Z","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=allenai-olmo-3.1-32b-instruct","compare_url":"https://unfragile.ai/compare?artifact=allenai-olmo-3.1-32b-instruct"}},"signature":"CVrSWSrJGz6EBv0sK8uHo4f+YEG1xXyzn9E1UdzFM+UYvxf08CZl4uEuq6NbaUjFjpnBbXigRHIfX9mPy8nuBA==","signedAt":"2026-06-20T09:28:58.811Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/allenai-olmo-3.1-32b-instruct","artifact":"https://unfragile.ai/allenai-olmo-3.1-32b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=allenai-olmo-3.1-32b-instruct","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"}}