{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-allenai-olmo-3-32b-think","slug":"allenai-olmo-3-32b-think","name":"AllenAI: Olmo 3 32B Think","type":"model","url":"https://openrouter.ai/models/allenai~olmo-3-32b-think","page_url":"https://unfragile.ai/allenai-olmo-3-32b-think","categories":["chatbots-assistants"],"tags":["allenai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-allenai-olmo-3-32b-think__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.token.budget.allocation","name":"extended-chain-of-thought reasoning with token budget allocation","description":"Olmo 3 32B Think implements an internal reasoning mechanism that allocates computational budget across multiple reasoning steps before generating final responses. The model uses a 'thinking' phase where it explores problem decomposition, validates intermediate logic, and backtracks on failed reasoning paths—similar to o1-style architectures but optimized for the 32B parameter scale. This approach enables structured exploration of complex multi-step problems without exposing intermediate reasoning to the user by default.","intents":["I need a model that can solve multi-step math and logic problems by showing its work internally before answering","I want to use a reasoning-focused model that doesn't hallucinate on complex instruction-following tasks","I need to decompose ambiguous problems into sub-problems and validate solutions before returning them"],"best_for":["AI engineers building reasoning-heavy agents for code analysis, mathematical problem-solving, or logical inference","Teams prototyping advanced RAG systems where retrieval validation and multi-hop reasoning are critical","Researchers evaluating open-source reasoning capabilities at the 32B scale"],"limitations":["Reasoning budget is fixed per request—cannot dynamically allocate more compute for exceptionally hard problems","Internal reasoning tokens are not exposed by default; debugging reasoning failures requires prompt engineering or API extensions","Latency is higher than standard LLMs due to extended thinking phase; typical response time 2-5x slower than base models","Reasoning quality degrades on out-of-distribution tasks not well-represented in training data"],"requires":["OpenRouter API key or compatible endpoint supporting Olmo 3 32B Think","HTTP/2 or HTTP/1.1 client with timeout tolerance for extended inference (30-60s typical)","Understanding of prompt engineering for reasoning tasks (explicit step-by-step instructions improve performance)"],"input_types":["text (natural language prompts, code snippets, mathematical problems, logic puzzles)","structured prompts with explicit reasoning directives (e.g., 'think step-by-step before answering')"],"output_types":["text (final answer with optional reasoning trace)","structured reasoning output (if API supports exposing thinking tokens)"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_1","uri":"capability://text.generation.language.instruction.following.with.complex.multi.turn.context.management","name":"instruction-following with complex multi-turn context management","description":"Olmo 3 32B Think maintains coherent multi-turn conversation state with explicit handling of nested instructions, conditional logic, and context-dependent responses. The model uses attention mechanisms optimized for long-range dependency tracking across conversation history, enabling it to follow complex instructions that reference earlier turns, maintain task state across interruptions, and resolve ambiguous pronouns and references within extended dialogues.","intents":["I need a model that can follow a series of conditional instructions across multiple turns without losing context","I want to build a chatbot that maintains task state (e.g., 'remember this constraint for all future code generation in this conversation')","I need to handle complex user requests that require referencing and modifying earlier conversation turns"],"best_for":["Developers building multi-turn AI assistants for code review, tutoring, or technical support","Teams implementing conversational AI systems where instruction consistency across turns is critical","Non-technical users prototyping chatbots that need to maintain complex task context"],"limitations":["Context window is finite (typically 4K-8K tokens); very long conversations require summarization or context pruning","Performance degrades with deeply nested conditional instructions (>5 levels of if-then logic in a single prompt)","No explicit memory mechanism between separate conversations; each new session starts with zero context","Instruction conflicts in multi-turn scenarios may cause the model to default to the most recent instruction rather than resolving ambiguity optimally"],"requires":["OpenRouter API key with Olmo 3 32B Think model access","Client library or HTTP wrapper supporting multi-turn conversation state management","Clear conversation history tracking (role-based: 'user', 'assistant')"],"input_types":["text (natural language instructions, code snippets, task descriptions)","multi-turn conversation history (array of user/assistant message pairs)"],"output_types":["text (assistant responses maintaining instruction context)","structured outputs (if prompts request JSON or code)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_10","uri":"capability://text.generation.language.translation.with.reasoning.aware.context.preservation","name":"translation with reasoning-aware context preservation","description":"Olmo 3 32B Think translates text across languages while internally reasoning about cultural context, idiomatic expressions, and domain-specific terminology. The reasoning phase enables the model to handle nuanced translations that preserve meaning and tone, resolve ambiguities in word sense, and validate that translations are contextually appropriate.","intents":["I need high-quality translations that preserve meaning, tone, and cultural context","I want a model that can handle domain-specific terminology and idiomatic expressions in translation","I need to translate content while maintaining consistency with previous translations (glossaries, style guides)"],"best_for":["Translation teams using AI for draft generation and quality assurance","Developers building multilingual content management systems","Researchers evaluating reasoning capabilities on translation benchmarks"],"limitations":["Translation quality varies by language pair; less common language pairs may have lower accuracy","Reasoning phase increases latency; real-time translation is not practical","No built-in support for glossaries or style guides; consistency must be enforced via prompts","Cultural context understanding may be limited for non-English source languages"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Source text (plain text, markdown, or structured format)","Source and target language specification","Optional: glossaries, style guides, or previous translations for consistency"],"input_types":["text (source text in any language)","structured data (language pair specification, glossaries, style guides)"],"output_types":["text (translated text in target language)","structured data (translation with confidence assessments or alternative translations)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_11","uri":"capability://code.generation.editing.error.detection.and.debugging.with.reasoning.based.root.cause.analysis","name":"error detection and debugging with reasoning-based root cause analysis","description":"Olmo 3 32B Think detects errors in code, logic, or content by internally reasoning about expected behavior, identifying deviations, and performing root cause analysis. The reasoning phase enables the model to trace through code execution paths, identify subtle bugs that may not be immediately obvious, and suggest targeted fixes rather than generic recommendations.","intents":["I need a model that can identify subtle bugs in code that static analysis tools might miss","I want to understand why a particular piece of code or logic is failing and get targeted fix suggestions","I need to debug complex systems by reasoning about interactions between components"],"best_for":["Developers using AI for code review and debugging assistance","Teams building automated testing or quality assurance systems","Researchers evaluating reasoning capabilities on bug detection benchmarks"],"limitations":["Bug detection quality depends on code clarity and context; obfuscated or poorly-documented code reduces accuracy","Reasoning phase increases latency; real-time debugging is not practical","No built-in execution environment; detected bugs must be verified separately","Root cause analysis may be speculative if the actual error is not reproducible in the prompt"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Code or logic to be debugged (plain text or structured format)","Optional: error messages, expected behavior, or test cases for clarification"],"input_types":["text (code snippets, logic descriptions, error messages)","structured data (test cases, expected behavior specifications)"],"output_types":["text (bug report with root cause analysis and fix suggestions)","code (corrected code or patches)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.reasoning.aware.refactoring","name":"code generation and analysis with reasoning-aware refactoring","description":"Olmo 3 32B Think generates code across multiple programming languages while applying internal reasoning to validate correctness, identify edge cases, and suggest refactorings. The model's reasoning phase enables it to trace through code logic, simulate execution paths, and detect potential bugs before returning the final code. This is implemented via the extended thinking mechanism, which explores multiple implementation approaches and selects the most robust one.","intents":["I need to generate production-ready code that handles edge cases without requiring extensive manual review","I want a code assistant that can explain why a particular implementation is correct or identify bugs in existing code","I need to refactor legacy code while maintaining correctness; I want the model to validate the refactoring internally"],"best_for":["Solo developers and small teams using AI-assisted code generation for rapid prototyping","Teams migrating from GPT-3.5 to a more reasoning-capable model for code review automation","Researchers evaluating open-source code generation capabilities with reasoning"],"limitations":["Code generation latency is 3-5x higher than non-reasoning models due to internal thinking phase","Reasoning quality is best for algorithmic code; performance degrades on domain-specific or framework-heavy code (e.g., complex React patterns)","No built-in execution environment; generated code must be tested separately","Limited to code generation; does not provide real-time linting or static analysis integration"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Programming language specification in prompt (Python, JavaScript, Go, Rust, etc.)","Optional: code context or existing codebase snippets for better refactoring suggestions"],"input_types":["text (natural language code requests, pseudocode, algorithm descriptions)","code (existing code for refactoring, debugging, or analysis)","structured prompts (e.g., 'generate a function that does X with constraints Y')"],"output_types":["code (generated or refactored code in specified language)","text (explanations of code logic, bug reports, refactoring suggestions)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_3","uri":"capability://planning.reasoning.mathematical.problem.solving.with.step.by.step.validation","name":"mathematical problem-solving with step-by-step validation","description":"Olmo 3 32B Think solves mathematical problems by internally decomposing them into sub-problems, validating intermediate calculations, and backtracking if a solution path fails. The reasoning phase enables the model to explore multiple solution strategies (e.g., algebraic vs. geometric approaches) and select the most efficient one. This is particularly effective for multi-step word problems, proof-based mathematics, and problems requiring constraint satisfaction.","intents":["I need to solve complex math problems (calculus, linear algebra, combinatorics) with high accuracy","I want a model that can explain mathematical reasoning step-by-step and validate its own work","I need to generate math tutoring content that shows correct solution paths without errors"],"best_for":["Educators building AI-powered tutoring systems for mathematics","Researchers evaluating reasoning capabilities on standardized math benchmarks (AMC, AIME, etc.)","Teams building automated homework checking or math problem generation systems"],"limitations":["Performance is best on problems solvable within the reasoning budget; very long proofs may exceed token limits","Symbolic mathematics (e.g., simplifying complex expressions) may require external tools like SymPy for verification","Reasoning quality depends on problem clarity; ambiguous or poorly-formatted math problems reduce accuracy","No built-in support for mathematical notation rendering; outputs are plain text or LaTeX"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Clear mathematical problem statement (natural language or LaTeX)","Optional: context on problem domain (e.g., 'this is a calculus problem', 'this requires combinatorial reasoning')"],"input_types":["text (natural language math problems, word problems)","LaTeX or mathematical notation","structured problem descriptions with constraints"],"output_types":["text (step-by-step solution with explanations)","LaTeX (formatted mathematical expressions)","structured data (if prompts request JSON with solution steps)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_4","uri":"capability://planning.reasoning.logical.reasoning.and.constraint.satisfaction","name":"logical reasoning and constraint satisfaction","description":"Olmo 3 32B Think solves constraint satisfaction problems, logical puzzles, and inference tasks by internally exploring the solution space, tracking constraints, and validating proposed solutions against all constraints. The reasoning phase enables the model to handle problems with multiple interdependent constraints (e.g., scheduling, graph coloring, satisfiability problems) by systematically exploring valid assignments and backtracking on conflicts.","intents":["I need to solve logic puzzles or constraint satisfaction problems (e.g., Sudoku, scheduling problems)","I want a model that can perform multi-hop logical inference and validate conclusions against premises","I need to generate test cases or scenarios that satisfy complex business logic constraints"],"best_for":["Teams building automated reasoning systems for business logic validation or test case generation","Researchers evaluating reasoning capabilities on constraint satisfaction benchmarks","Developers prototyping AI systems for planning, scheduling, or resource allocation"],"limitations":["Performance degrades on NP-hard problems with large solution spaces; the model may not find optimal solutions within the reasoning budget","Constraint representation must be clear and unambiguous; implicit or poorly-specified constraints reduce accuracy","No built-in integration with constraint solvers (e.g., Z3, Gurobi); complex problems may require external tools","Reasoning quality depends on problem structure; highly irregular or novel constraint types may not be handled well"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Clear specification of constraints and problem structure","Optional: examples of valid and invalid solutions to clarify the problem"],"input_types":["text (natural language problem descriptions, constraint specifications)","structured data (constraint lists, variable domains)","examples (valid/invalid solutions for clarification)"],"output_types":["text (solution with explanation of constraint satisfaction)","structured data (variable assignments, solution verification)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_5","uri":"capability://tool.use.integration.api.schema.understanding.and.function.calling.with.reasoning.validation","name":"api schema understanding and function calling with reasoning validation","description":"Olmo 3 32B Think understands API schemas and generates correct function calls by internally reasoning about parameter types, constraints, and dependencies before selecting the appropriate function. The reasoning phase enables the model to validate that proposed function calls satisfy schema constraints, handle optional parameters correctly, and resolve ambiguities in function selection when multiple functions could satisfy a user intent.","intents":["I need a model that can reliably call APIs with complex schemas without generating invalid requests","I want to build an agent that can reason about which API function to call based on user intent and available schemas","I need to validate that generated function calls satisfy all schema constraints before execution"],"best_for":["Teams building AI agents that interact with external APIs (e.g., payment processors, cloud services)","Developers creating tool-use systems where incorrect function calls are costly or dangerous","Researchers evaluating reasoning capabilities on function calling benchmarks"],"limitations":["Schema understanding is limited to schemas provided in the prompt; no built-in schema registry or discovery mechanism","Complex nested schemas or recursive data structures may confuse the model","No built-in execution environment; generated function calls must be validated and executed separately","Reasoning quality depends on schema clarity; poorly-documented or ambiguous schemas reduce accuracy"],"requires":["OpenRouter API key with Olmo 3 32B Think access","API schema specification (OpenAPI, JSON Schema, or natural language description)","Clear mapping of user intents to available functions"],"input_types":["text (user requests, natural language intent descriptions)","structured data (API schemas, function definitions)","examples (sample function calls for clarification)"],"output_types":["structured data (function calls in JSON or code format)","text (explanation of function selection and parameter choices)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_6","uri":"capability://data.processing.analysis.document.analysis.and.information.extraction.with.reasoning.based.validation","name":"document analysis and information extraction with reasoning-based validation","description":"Olmo 3 32B Think analyzes documents and extracts structured information by internally reasoning about document structure, identifying relevant sections, and validating extracted information against the document context. The reasoning phase enables the model to handle complex documents with multiple sections, resolve ambiguities in information extraction, and validate that extracted data is consistent with the source material.","intents":["I need to extract structured data from unstructured documents (contracts, reports, forms) with high accuracy","I want a model that can identify and resolve ambiguities in document interpretation before returning extracted data","I need to validate that extracted information is consistent with the source document and flag potential errors"],"best_for":["Teams building document processing pipelines for legal, financial, or compliance use cases","Developers creating information extraction systems where accuracy is critical","Researchers evaluating reasoning capabilities on document understanding benchmarks"],"limitations":["Document length is limited by context window (typically 4K-8K tokens); very long documents require chunking or summarization","Extraction quality depends on document clarity and structure; poorly-formatted or ambiguous documents reduce accuracy","No built-in OCR or image processing; documents must be provided as text","Reasoning quality may degrade on domain-specific documents (e.g., technical specifications, medical records) without domain-specific training"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Document text (plain text, markdown, or structured format)","Schema or template specifying what information to extract","Optional: examples of correctly extracted information for clarification"],"input_types":["text (document content, plain text or markdown)","structured data (extraction schema or template)","examples (sample extractions for clarification)"],"output_types":["structured data (extracted information in JSON or CSV format)","text (extraction explanations and confidence assessments)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.reasoning.aware.coherence","name":"creative writing and content generation with reasoning-aware coherence","description":"Olmo 3 32B Think generates creative content (stories, essays, marketing copy) while internally reasoning about narrative structure, character consistency, and thematic coherence. The reasoning phase enables the model to plan multi-paragraph narratives, maintain character voice across sections, and validate that generated content aligns with specified constraints (tone, length, audience).","intents":["I need to generate long-form creative content (stories, essays) with consistent narrative structure and character development","I want a model that can maintain specific tone and voice across multiple paragraphs of generated content","I need to generate marketing or promotional content that aligns with brand guidelines and audience expectations"],"best_for":["Content creators and writers using AI for brainstorming and draft generation","Marketing teams generating product descriptions, ad copy, and promotional content","Educators creating writing prompts and example content for students"],"limitations":["Generated content may be verbose or repetitive; post-editing is often required for publication-quality output","Reasoning phase increases latency; real-time content generation is not practical","Creativity is constrained by training data; novel or highly original content may be limited","No built-in fact-checking; generated content may contain plausible-sounding but false information"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Clear content specification (topic, tone, length, audience, constraints)","Optional: examples of desired writing style or tone"],"input_types":["text (content prompts, writing briefs, topic descriptions)","structured data (content specifications: tone, length, audience, constraints)","examples (reference content for style matching)"],"output_types":["text (generated creative content, essays, stories, marketing copy)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_8","uri":"capability://planning.reasoning.question.answering.with.multi.hop.reasoning.and.source.validation","name":"question answering with multi-hop reasoning and source validation","description":"Olmo 3 32B Think answers complex questions by internally decomposing them into sub-questions, retrieving or reasoning about relevant information, and validating answers against the source material. The reasoning phase enables the model to handle questions requiring multiple reasoning steps, resolve ambiguities in question interpretation, and provide confidence assessments for answers.","intents":["I need a model that can answer complex questions requiring multiple reasoning steps or knowledge synthesis","I want to build a QA system that validates answers against source material and provides confidence assessments","I need to answer questions about specific documents or knowledge bases with high accuracy"],"best_for":["Teams building customer support or knowledge base QA systems","Developers creating educational QA systems for tutoring or assessment","Researchers evaluating reasoning capabilities on multi-hop QA benchmarks"],"limitations":["Answer quality depends on the clarity and completeness of source material; missing information reduces accuracy","Multi-hop reasoning is limited by the reasoning budget; very complex questions may exceed token limits","No built-in knowledge base integration; source material must be provided in the prompt or via RAG","Reasoning quality may degrade on questions requiring specialized domain knowledge"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Question text (natural language question)","Optional: source material or knowledge base context for answer validation"],"input_types":["text (natural language questions)","structured data (source material, knowledge base context)","examples (sample Q&A pairs for clarification)"],"output_types":["text (answer with explanation and confidence assessment)","structured data (answer with source citations and reasoning steps)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-allenai-olmo-3-32b-think__cap_9","uri":"capability://text.generation.language.summarization.with.reasoning.aware.content.selection","name":"summarization with reasoning-aware content selection","description":"Olmo 3 32B Think summarizes long documents or conversations by internally reasoning about content importance, identifying key themes, and validating that the summary captures essential information without losing critical details. The reasoning phase enables the model to handle documents with complex structure, resolve ambiguities in importance assessment, and generate summaries at specified abstraction levels.","intents":["I need to summarize long documents or conversations while preserving critical information","I want a model that can identify and extract key themes from unstructured content","I need to generate summaries at different abstraction levels (executive summary, detailed summary, bullet points)"],"best_for":["Teams building document management or knowledge management systems","Developers creating meeting transcription or conversation summarization tools","Researchers evaluating reasoning capabilities on summarization benchmarks"],"limitations":["Summary quality depends on document clarity and structure; poorly-organized content reduces accuracy","Reasoning phase increases latency; real-time summarization is not practical","Summaries may omit important details if the reasoning phase prioritizes brevity over completeness","No built-in support for multi-document summarization; each document must be summarized separately"],"requires":["OpenRouter API key with Olmo 3 32B Think access","Document text (plain text, markdown, or structured format)","Optional: summary specification (length, abstraction level, focus areas)"],"input_types":["text (document content, conversation transcripts, articles)","structured data (summary specifications: length, abstraction level, focus areas)"],"output_types":["text (summary at specified abstraction level)","structured data (summary with key themes and importance scores)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or compatible endpoint supporting Olmo 3 32B Think","HTTP/2 or HTTP/1.1 client with timeout tolerance for extended inference (30-60s typical)","Understanding of prompt engineering for reasoning tasks (explicit step-by-step instructions improve performance)","OpenRouter API key with Olmo 3 32B Think model access","Client library or HTTP wrapper supporting multi-turn conversation state management","Clear conversation history tracking (role-based: 'user', 'assistant')","OpenRouter API key with Olmo 3 32B Think access","Source text (plain text, markdown, or structured format)","Source and target language specification","Optional: glossaries, style guides, or previous translations for consistency"],"failure_modes":["Reasoning budget is fixed per request—cannot dynamically allocate more compute for exceptionally hard problems","Internal reasoning tokens are not exposed by default; debugging reasoning failures requires prompt engineering or API extensions","Latency is higher than standard LLMs due to extended thinking phase; typical response time 2-5x slower than base models","Reasoning quality degrades on out-of-distribution tasks not well-represented in training data","Context window is finite (typically 4K-8K tokens); very long conversations require summarization or context pruning","Performance degrades with deeply nested conditional instructions (>5 levels of if-then logic in a single prompt)","No explicit memory mechanism between separate conversations; each new session starts with zero context","Instruction conflicts in multi-turn scenarios may cause the model to default to the most recent instruction rather than resolving ambiguity optimally","Translation quality varies by language pair; less common language pairs may have lower accuracy","Reasoning phase increases latency; real-time translation is not practical","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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-3-32b-think","compare_url":"https://unfragile.ai/compare?artifact=allenai-olmo-3-32b-think"}},"signature":"B6/8p4yFRjQkBbrofYUsap+AwixDwkDLDTeSWrZ430q3lF5Orh8ViQn1APfc5DTAtyW6kwkSWQKteOAlHbvLCg==","signedAt":"2026-06-20T16:16:02.550Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/allenai-olmo-3-32b-think","artifact":"https://unfragile.ai/allenai-olmo-3-32b-think","verify":"https://unfragile.ai/api/v1/verify?slug=allenai-olmo-3-32b-think","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"}}