{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-arcee-ai-trinity-large-thinking","slug":"arcee-ai-trinity-large-thinking","name":"Arcee AI: Trinity Large Thinking","type":"model","url":"https://openrouter.ai/models/arcee-ai~trinity-large-thinking","page_url":"https://unfragile.ai/arcee-ai-trinity-large-thinking","categories":["ai-agents"],"tags":["arcee-ai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.20e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_0","uri":"capability://planning.reasoning.extended.reasoning.chain.of.thought.generation","name":"extended-reasoning-chain-of-thought-generation","description":"Generates explicit reasoning chains using an internal 'thinking' mechanism that decomposes complex problems into intermediate steps before producing final answers. The model uses a large thinking budget to explore multiple reasoning paths, backtrack when needed, and validate conclusions before output, similar to o1-style reasoning but optimized for open-source efficiency. This approach enables structured problem-solving for tasks requiring multi-step logical inference, mathematical reasoning, and code analysis.","intents":["I need a model that can solve complex math problems step-by-step with visible reasoning","I want to debug code by having the model trace through execution paths and explain its logic","I need reasoning transparency for agentic systems where intermediate steps must be auditable","I'm building a system that requires multi-hop reasoning across documents or code repositories"],"best_for":["AI engineers building reasoning-heavy agents and autonomous systems","researchers evaluating reasoning capabilities in open-source models","teams requiring interpretable AI decisions with auditable thought processes","developers optimizing for latency vs reasoning depth trade-offs"],"limitations":["Thinking tokens increase latency significantly — typical response time 5-15 seconds for complex reasoning vs <1 second for direct generation","Larger thinking budgets consume more API credits or compute resources, making per-request costs higher than standard LLMs","Thinking output may not be fully transparent or controllable — internal reasoning chains are generated but not always exposed to users","Performance gains over standard models diminish on simple tasks where reasoning overhead becomes a bottleneck"],"requires":["API access to Arcee AI via OpenRouter or direct endpoint","Support for streaming or polling long-running inference requests (typical timeout 30+ seconds)","Client-side handling of extended token sequences (thinking + output can exceed 50k tokens)"],"input_types":["text (natural language queries, problem statements)","code snippets (for debugging and analysis)","structured prompts (with explicit reasoning instructions)"],"output_types":["text (final answer with optional reasoning explanation)","structured reasoning traces (if exposed via API)","code solutions with step-by-step derivation"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_1","uri":"capability://planning.reasoning.agentic.task.decomposition.and.planning","name":"agentic-task-decomposition-and-planning","description":"Decomposes complex user requests into executable subtasks and generates plans for multi-step workflows, leveraging extended reasoning to evaluate dependencies, resource constraints, and alternative approaches. The model can identify which subtasks can run in parallel, estimate execution order, and adapt plans based on intermediate results. This capability is optimized for agentic systems where the model acts as a planner/orchestrator rather than a single-turn responder.","intents":["I need a model that can break down a complex project request into a prioritized task list with dependencies","I'm building an agent that needs to plan multi-step workflows (e.g., data pipeline design, system architecture)","I want the model to identify which tasks can run in parallel vs sequentially in a workflow","I need a planner that can re-evaluate and adjust plans when intermediate steps fail or return unexpected results"],"best_for":["AI engineers building autonomous agents and workflow orchestrators","teams implementing multi-step reasoning systems (e.g., research assistants, code generation pipelines)","product teams needing intelligent task prioritization and dependency resolution","developers creating planning layers for complex LLM-based applications"],"limitations":["Planning quality depends on prompt engineering — vague requests may produce incomplete or circular task graphs","No built-in execution engine — the model generates plans but doesn't execute them; integration with external task runners required","Reasoning overhead makes real-time planning impractical for latency-sensitive applications (typical planning latency 3-10 seconds)","Limited ability to handle dynamic constraints that change during execution without explicit re-planning prompts"],"requires":["API access to Arcee AI Trinity Large Thinking model","Task execution framework or orchestrator (e.g., Temporal, Airflow, custom agent loop) to consume generated plans","Structured prompt templates that define task format, dependencies, and success criteria"],"input_types":["text (high-level goal or project description)","structured task specifications (with constraints and resource requirements)","context about available tools and APIs the agent can invoke"],"output_types":["task graphs (DAG-like structures with dependencies)","prioritized task lists with estimated effort","execution plans with resource allocation"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_2","uri":"capability://code.generation.editing.code.reasoning.and.debugging.analysis","name":"code-reasoning-and-debugging-analysis","description":"Analyzes code for bugs, performance issues, and architectural problems by using extended reasoning to trace execution paths, identify edge cases, and evaluate alternative implementations. The model can reason through complex control flow, state mutations, and cross-module dependencies to pinpoint root causes of issues. This is particularly effective for debugging multi-file codebases, understanding legacy code, and validating correctness of algorithms.","intents":["I have a bug in my code and need the model to trace through execution and explain what's going wrong","I want to understand why my algorithm is slow and get suggestions for optimization with reasoning","I need to review code for security vulnerabilities and have the model explain the attack vectors","I'm refactoring legacy code and need help understanding complex control flow and dependencies"],"best_for":["software engineers debugging complex systems or unfamiliar codebases","code reviewers analyzing pull requests for correctness and performance","security engineers evaluating code for vulnerabilities","teams migrating or refactoring large codebases"],"limitations":["Reasoning latency makes real-time IDE integration impractical — typical analysis takes 5-15 seconds vs instant feedback from linters","Model may miss context-dependent bugs that require runtime state or external service behavior","Explanation quality varies with code clarity — poorly documented or obfuscated code may confuse reasoning","Limited ability to analyze code that depends on external libraries without explicit documentation of library behavior"],"requires":["API access to Arcee AI Trinity Large Thinking","Code snippets or file paths (model can accept up to context window limit, typically 100k+ tokens)","Optional: test cases, error logs, or performance profiles to provide additional context"],"input_types":["code snippets (single file or multi-file context)","error messages and stack traces","performance profiles or logs","test cases demonstrating the issue"],"output_types":["bug analysis with root cause explanation","step-by-step execution traces","suggested fixes with reasoning","performance optimization recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_3","uri":"capability://planning.reasoning.mathematical.reasoning.and.problem.solving","name":"mathematical-reasoning-and-problem-solving","description":"Solves mathematical problems by generating detailed step-by-step derivations, validating intermediate results, and exploring alternative solution approaches using extended reasoning. The model can handle symbolic manipulation, proof generation, numerical computation reasoning, and multi-step problem solving across algebra, calculus, linear algebra, and discrete mathematics. Reasoning tokens enable the model to verify solutions and backtrack if an approach fails.","intents":["I need to solve a complex math problem and see every step of the derivation","I want to verify that a mathematical proof is correct and understand the logic","I'm teaching math and need detailed explanations of problem-solving approaches","I need to validate numerical computations and understand where errors might occur"],"best_for":["students and educators needing detailed math explanations","researchers validating mathematical derivations and proofs","engineers solving physics and engineering problems requiring mathematical reasoning","data scientists understanding the math behind algorithms and statistical methods"],"limitations":["Symbolic computation is reasoning-based, not exact — model may make algebraic errors or miss elegant solutions","Very large numerical computations may exceed reasoning budget or produce approximate rather than exact answers","Reasoning approach is slower than specialized math engines (Mathematica, Wolfram Alpha) for straightforward calculations","Model cannot verify proofs against formal logic systems — reasoning is intuitive, not formally verified"],"requires":["API access to Arcee AI Trinity Large Thinking","Mathematical notation support in client (LaTeX rendering optional but recommended)","Sufficient context window for multi-step problems (typically 50k+ tokens for complex derivations)"],"input_types":["text (natural language math problems)","mathematical notation (LaTeX, ASCII math)","problem context (course level, domain, constraints)"],"output_types":["step-by-step derivations with reasoning","symbolic solutions","numerical answers with confidence assessment","alternative solution approaches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_4","uri":"capability://planning.reasoning.complex.query.answering.with.reasoning","name":"complex-query-answering-with-reasoning","description":"Answers complex, multi-faceted questions by using extended reasoning to break down the question into sub-questions, gather relevant information from reasoning, synthesize answers, and validate consistency. The model can handle questions requiring integration of multiple domains, temporal reasoning, counterfactual analysis, and nuanced trade-off evaluation. This is distinct from simple retrieval-based QA because reasoning enables inference beyond training data.","intents":["I have a complex question that requires reasoning across multiple domains and perspectives","I need to understand trade-offs and implications of different approaches to a problem","I want to ask counterfactual questions (what if X happened?) and get reasoned analysis","I need detailed answers to open-ended questions that don't have simple factual answers"],"best_for":["researchers and analysts needing deep reasoning on complex topics","business strategists evaluating multi-faceted decisions","educators and students exploring nuanced topics","knowledge workers synthesizing information across domains"],"limitations":["Answers are reasoning-based, not fact-checked — model may confidently provide incorrect information if reasoning is flawed","Latency is high (5-15 seconds) making real-time conversational interaction impractical","Model cannot access real-time information or external knowledge bases without explicit context injection","Reasoning quality degrades on questions outside model's training distribution or requiring specialized domain knowledge"],"requires":["API access to Arcee AI Trinity Large Thinking","Sufficient context window for multi-part questions and detailed answers (100k+ tokens recommended)","Optional: domain-specific context or reference materials to ground reasoning"],"input_types":["text (natural language questions)","context documents (for grounding reasoning)","structured question formats (with sub-questions or constraints)"],"output_types":["detailed answers with reasoning chains","trade-off analysis and implications","alternative perspectives and counterarguments","confidence assessments and caveats"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.with.validation","name":"structured-data-extraction-with-validation","description":"Extracts structured data from unstructured text using reasoning to validate consistency, resolve ambiguities, and ensure output conforms to specified schemas. The model can reason about entity relationships, handle missing or conflicting information, and provide confidence scores for extracted fields. This is particularly useful for complex extraction tasks where simple pattern matching fails due to ambiguity or context-dependence.","intents":["I need to extract structured information from documents and validate that the extraction is consistent","I want to parse complex text and resolve ambiguities in entity relationships or attributes","I need to extract data that requires reasoning about context and implicit information","I want confidence scores for extracted fields to identify uncertain extractions"],"best_for":["data engineers building ETL pipelines with complex extraction logic","teams processing unstructured documents (contracts, reports, emails) at scale","researchers extracting structured datasets from text corpora","product teams building knowledge bases from unstructured sources"],"limitations":["Reasoning latency makes real-time extraction impractical — typical extraction takes 3-10 seconds per document","Extraction quality depends on schema clarity and prompt engineering — ambiguous schemas produce inconsistent results","Model may hallucinate data if source text is ambiguous or incomplete","Batch processing at scale becomes expensive due to reasoning token consumption per document"],"requires":["API access to Arcee AI Trinity Large Thinking","Clearly defined schema (JSON Schema, Pydantic models, or similar) for output structure","Unstructured text input (documents, emails, web content, etc.)"],"input_types":["unstructured text (documents, emails, web pages)","schema definitions (JSON Schema, Pydantic, or natural language)","extraction instructions and examples"],"output_types":["structured JSON or typed objects conforming to schema","confidence scores per field","extraction validation results","notes on ambiguities or missing information"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_6","uri":"capability://text.generation.language.multi.turn.reasoning.conversation","name":"multi-turn-reasoning-conversation","description":"Maintains coherent multi-turn conversations where each response builds on previous reasoning and context, using extended reasoning to track conversation state, validate consistency across turns, and adapt reasoning based on user feedback. The model can correct itself, explore alternative directions based on user input, and maintain a coherent reasoning thread across many turns without losing context or consistency.","intents":["I want to have a back-and-forth conversation where the model remembers and builds on previous reasoning","I need to iteratively refine a solution through conversation, with the model explaining its reasoning at each step","I want to explore different approaches to a problem through dialogue, with the model adapting based on feedback","I need the model to catch and correct its own mistakes when I point them out"],"best_for":["interactive problem-solving sessions (debugging, design, planning)","educational tutoring where dialogue enables deeper understanding","collaborative work where human and AI iterate on solutions","research and analysis where exploration requires back-and-forth reasoning"],"limitations":["Context window limits conversation length — very long conversations may lose early context or require summarization","Reasoning latency accumulates across turns, making rapid back-and-forth impractical (each turn adds 3-10 seconds)","Model may drift from original reasoning if conversation becomes very long or takes unexpected directions","Cost scales with conversation length due to token consumption for reasoning and context"],"requires":["API access to Arcee AI Trinity Large Thinking with conversation/session management","Client-side conversation state management (message history, context tracking)","Sufficient API quota for multi-turn interactions (each turn consumes significant tokens)"],"input_types":["text (user messages in natural language)","conversation history (previous turns)","optional: structured context or constraints for the conversation"],"output_types":["text responses with reasoning","clarifications and follow-up questions","corrections and alternative approaches","reasoning traces showing how previous context influenced current response"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-trinity-large-thinking__cap_7","uri":"capability://planning.reasoning.performance.benchmarking.and.evaluation","name":"performance-benchmarking-and-evaluation","description":"Evaluates AI system performance by reasoning through benchmark results, identifying performance bottlenecks, and suggesting optimizations based on detailed analysis of metrics and trade-offs. The model can interpret benchmark results, explain why certain approaches perform better, and reason about optimization strategies without requiring code execution. This capability is particularly useful for understanding model behavior on standardized benchmarks like PinchBench.","intents":["I want to understand why my model performs differently on various benchmarks and what it means","I need to identify performance bottlenecks in my AI system and get optimization suggestions","I want to compare different model architectures or approaches based on benchmark results","I need to reason about trade-offs between different optimization strategies"],"best_for":["ML engineers optimizing model performance","researchers evaluating model capabilities across benchmarks","teams comparing different AI approaches or models","product teams understanding model behavior on specific tasks"],"limitations":["Analysis is based on reasoning about metrics, not actual profiling or execution — may miss implementation-specific bottlenecks","Suggestions are heuristic-based, not guaranteed to improve performance in practice","Model may misinterpret benchmark results if context about benchmark design is missing","Reasoning quality depends on clarity of benchmark descriptions and metrics provided"],"requires":["API access to Arcee AI Trinity Large Thinking","Benchmark results and metrics (scores, latency, resource usage, etc.)","Context about benchmark design and evaluation methodology"],"input_types":["benchmark results (scores, metrics, comparisons)","system descriptions (architecture, hyperparameters, constraints)","performance profiles or logs"],"output_types":["performance analysis and interpretation","bottleneck identification","optimization suggestions with reasoning","trade-off analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access to Arcee AI via OpenRouter or direct endpoint","Support for streaming or polling long-running inference requests (typical timeout 30+ seconds)","Client-side handling of extended token sequences (thinking + output can exceed 50k tokens)","API access to Arcee AI Trinity Large Thinking model","Task execution framework or orchestrator (e.g., Temporal, Airflow, custom agent loop) to consume generated plans","Structured prompt templates that define task format, dependencies, and success criteria","API access to Arcee AI Trinity Large Thinking","Code snippets or file paths (model can accept up to context window limit, typically 100k+ tokens)","Optional: test cases, error logs, or performance profiles to provide additional context","Mathematical notation support in client (LaTeX rendering optional but recommended)"],"failure_modes":["Thinking tokens increase latency significantly — typical response time 5-15 seconds for complex reasoning vs <1 second for direct generation","Larger thinking budgets consume more API credits or compute resources, making per-request costs higher than standard LLMs","Thinking output may not be fully transparent or controllable — internal reasoning chains are generated but not always exposed to users","Performance gains over standard models diminish on simple tasks where reasoning overhead becomes a bottleneck","Planning quality depends on prompt engineering — vague requests may produce incomplete or circular task graphs","No built-in execution engine — the model generates plans but doesn't execute them; integration with external task runners required","Reasoning overhead makes real-time planning impractical for latency-sensitive applications (typical planning latency 3-10 seconds)","Limited ability to handle dynamic constraints that change during execution without explicit re-planning prompts","Reasoning latency makes real-time IDE integration impractical — typical analysis takes 5-15 seconds vs instant feedback from linters","Model may miss context-dependent bugs that require runtime state or external service behavior","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.775Z","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=arcee-ai-trinity-large-thinking","compare_url":"https://unfragile.ai/compare?artifact=arcee-ai-trinity-large-thinking"}},"signature":"uuMKJWIPK4Np36WQA9tapn0p30UbOaPlY/1ZEGddTjXEBX8Dk1euHIiBz2hpfb0HmUtOpj2Az0SUVIPhGdTyCA==","signedAt":"2026-06-22T05:14:08.448Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/arcee-ai-trinity-large-thinking","artifact":"https://unfragile.ai/arcee-ai-trinity-large-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=arcee-ai-trinity-large-thinking","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"}}