{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-tngtech-deepseek-r1t2-chimera","slug":"tngtech-deepseek-r1t2-chimera","name":"TNG: DeepSeek R1T2 Chimera","type":"model","url":"https://openrouter.ai/models/tngtech~deepseek-r1t2-chimera","page_url":"https://unfragile.ai/tngtech-deepseek-r1t2-chimera","categories":["chatbots-assistants"],"tags":["tngtech","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_0","uri":"capability://text.generation.language.mixture.of.experts.text.generation.with.merged.checkpoint.ensemble","name":"mixture-of-experts text generation with merged checkpoint ensemble","description":"Generates text using a 671B-parameter mixture-of-experts architecture assembled from three DeepSeek checkpoints (R1-0528, R1, V3-0324) via Assembly-of-Experts merge technique. Routes input tokens through sparse expert networks where only a subset of parameters activate per token, reducing computational cost while maintaining model capacity. The merge combines reasoning-optimized (R1) and instruction-following (V3) checkpoints to balance chain-of-thought depth with practical task performance.","intents":["Generate long-form reasoning and multi-step problem solutions with explicit thinking traces","Get high-quality text completions for code, creative writing, and analysis tasks","Run inference on complex reasoning tasks without full 671B parameter activation overhead","Access a model that balances deep reasoning capability with practical instruction-following"],"best_for":["AI researchers evaluating merged MoE architectures and ensemble techniques","Builders requiring reasoning-capable models with lower per-token inference cost","Teams prototyping applications needing both chain-of-thought and instruction-tuned behavior"],"limitations":["Mixture-of-experts routing adds ~15-25ms latency overhead per inference step compared to dense models","Expert load balancing may cause uneven token distribution, reducing effective parallelization on some hardware","Merged checkpoint approach may introduce subtle inconsistencies in reasoning patterns across different task domains","No built-in context window specification provided; actual maximum context length unknown from artifact data","Requires API access via OpenRouter; no local deployment option available"],"requires":["OpenRouter API key","HTTP/REST client capability","Network connectivity to OpenRouter endpoints","Understanding of MoE token routing behavior for cost estimation"],"input_types":["text (natural language prompts)","code snippets (for code generation and analysis)","structured prompts with reasoning directives"],"output_types":["text (completions, reasoning traces, explanations)","code (generation and refactoring)","structured reasoning with explicit thinking steps"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_1","uri":"capability://planning.reasoning.chain.of.thought.reasoning.with.explicit.thinking.traces","name":"chain-of-thought reasoning with explicit thinking traces","description":"Generates intermediate reasoning steps and explicit thinking traces before producing final answers, leveraging the R1 checkpoint components in the merged model. The model learns to decompose complex problems into substeps, showing work for mathematical reasoning, logical deduction, and multi-stage problem solving. This capability is inherited from DeepSeek-R1's training on reasoning-focused datasets and is preserved through the Assembly-of-Experts merge.","intents":["Understand the model's reasoning process for complex problems by examining intermediate steps","Solve multi-step math, logic, and coding problems with verifiable reasoning chains","Debug model outputs by inspecting where reasoning diverged from correct solution paths","Build applications that require transparent, auditable decision-making processes"],"best_for":["Researchers studying reasoning capabilities and failure modes in large language models","Developers building educational tools that need to explain problem-solving steps","Teams requiring interpretable AI for high-stakes decisions (medical, financial, legal analysis)"],"limitations":["Reasoning traces increase output token count by 2-5x, raising API costs proportionally","Chain-of-thought reasoning may hallucinate plausible-sounding but incorrect intermediate steps","Reasoning quality degrades on out-of-distribution problems not similar to training data","No guarantee that reasoning traces reflect actual model computation — may be post-hoc rationalization","Explicit thinking output format not standardized; parsing reasoning traces requires custom logic"],"requires":["OpenRouter API key with sufficient token quota","Client code to parse and extract reasoning traces from response","Understanding that longer outputs increase latency and cost"],"input_types":["text prompts requesting step-by-step reasoning","math problems with explicit 'show your work' instructions","logic puzzles and multi-stage decision scenarios"],"output_types":["text with intermediate reasoning steps","structured thinking traces (format varies by prompt engineering)","final answers with supporting reasoning chains"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.multi.language.support","name":"code generation and analysis with multi-language support","description":"Generates, completes, and analyzes code across multiple programming languages by leveraging training on diverse code repositories and instruction-tuning from the V3 checkpoint. The model understands code structure, syntax, and semantics for languages including Python, JavaScript, Java, C++, Go, Rust, and others. Supports code generation from natural language descriptions, code completion, refactoring suggestions, and bug analysis through token-level understanding of programming constructs.","intents":["Generate working code snippets from natural language descriptions or partial implementations","Complete code functions and methods with context-aware suggestions","Analyze code for bugs, performance issues, and security vulnerabilities","Refactor existing code to improve readability, performance, or architectural patterns"],"best_for":["Full-stack developers accelerating implementation of well-defined features","Teams conducting code reviews and seeking automated analysis of pull requests","Developers learning new programming languages or frameworks"],"limitations":["Generated code may contain subtle bugs or security issues; always requires human review before production use","Performance degrades on domain-specific languages or very new language versions not well-represented in training data","Code generation quality depends heavily on prompt clarity; ambiguous specifications produce inconsistent results","No built-in testing or validation of generated code — requires external test harnesses","Context window limitations may truncate large codebases, reducing code-aware suggestions"],"requires":["OpenRouter API key","Code snippets or descriptions as input","Understanding that generated code requires review and testing"],"input_types":["natural language code descriptions","partial code with completion requests","full code files for analysis and refactoring","code snippets with bug-finding prompts"],"output_types":["generated code (functions, classes, modules)","code completions and suggestions","analysis reports (bugs, performance issues, security concerns)","refactored code with explanations"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_3","uri":"capability://text.generation.language.instruction.following.and.task.specific.adaptation","name":"instruction-following and task-specific adaptation","description":"Follows complex, multi-part instructions and adapts behavior to task-specific requirements through training on the V3-0324 checkpoint, which emphasizes instruction-tuning and alignment. The model interprets nuanced directives about output format, tone, style, and constraints, and maintains consistency across multi-turn conversations. This capability enables the model to function as a specialized assistant for domain-specific tasks without requiring fine-tuning.","intents":["Get outputs in specific formats (JSON, markdown, structured tables) by specifying format requirements","Maintain consistent persona or tone across multiple interactions (professional, casual, technical, etc.)","Execute complex multi-step workflows described in natural language instructions","Adapt model behavior to domain-specific conventions (medical terminology, legal language, technical jargon)"],"best_for":["Product teams building chatbots and conversational interfaces requiring consistent behavior","Enterprises deploying AI assistants for customer service, content creation, or knowledge work","Developers building domain-specific applications (legal analysis, medical documentation, technical writing)"],"limitations":["Instruction-following quality degrades with conflicting or ambiguous directives","Model may misinterpret complex nested instructions or edge cases not well-represented in training","No persistent memory across separate API calls — each request requires full context re-specification","Instruction injection attacks possible if user input is not properly sanitized before passing to model","Instruction-following may conflict with safety guidelines, requiring careful prompt engineering"],"requires":["OpenRouter API key","Well-structured prompts with clear instructions","Input sanitization if accepting user-provided instructions"],"input_types":["natural language instructions with format specifications","multi-part task descriptions","domain-specific terminology and conventions","user queries with style/tone preferences"],"output_types":["text in specified formats (JSON, markdown, plain text, etc.)","structured data (tables, lists, hierarchies)","domain-specific outputs (medical reports, legal briefs, technical documentation)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_4","uri":"capability://text.generation.language.multi.turn.conversation.with.context.preservation","name":"multi-turn conversation with context preservation","description":"Maintains conversation history and context across multiple turns within a single API session, enabling coherent multi-turn dialogue where the model references previous messages and builds on prior context. The model tracks conversation state, understands pronouns and references to earlier statements, and adapts responses based on accumulated context. This is implemented through standard transformer attention mechanisms that process the full conversation history as input tokens.","intents":["Build conversational AI applications where users ask follow-up questions and expect contextual responses","Conduct multi-turn interviews, tutoring sessions, or customer support conversations","Maintain consistent character or persona across a conversation thread","Debug or refine outputs iteratively by asking clarifying questions and requesting modifications"],"best_for":["Developers building chatbot and conversational UI applications","Customer support teams deploying AI-powered help desk systems","Educational platforms requiring interactive tutoring or Q&A functionality"],"limitations":["Context window size limits conversation length; very long conversations require summarization or pruning","Model may lose track of context in conversations exceeding 10,000-15,000 tokens (varies by model configuration)","Each API call includes full conversation history, increasing token usage and latency as conversation grows","No persistent storage of conversation state — client must manage and resend history for each request","Context confusion possible in conversations with multiple speakers or complex topic switches"],"requires":["OpenRouter API key","Client code to manage and maintain conversation history","Token budget sufficient for growing conversation context","Understanding of context window limitations and token counting"],"input_types":["user messages in conversational format","conversation history (previous turns)","system prompts defining conversation behavior"],"output_types":["contextual responses referencing prior messages","follow-up suggestions and clarifying questions","conversation summaries and state updates"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_5","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.problem.solving","name":"mathematical reasoning and symbolic problem solving","description":"Solves mathematical problems including algebra, calculus, statistics, and symbolic reasoning through training on mathematical datasets and R1 checkpoint's reasoning capability. The model can work through multi-step mathematical proofs, show intermediate calculations, and explain mathematical concepts. It understands mathematical notation, can parse equations, and applies appropriate mathematical techniques to problem categories.","intents":["Solve homework and exam problems with step-by-step mathematical reasoning","Verify mathematical correctness of proofs and derivations","Explain mathematical concepts and theorems in accessible language","Generate mathematical content (problem sets, solutions, explanations) for educational materials"],"best_for":["Educational technology platforms and tutoring applications","Researchers and engineers needing symbolic problem solving and verification","Content creators developing mathematics educational materials"],"limitations":["Mathematical reasoning quality degrades on novel or highly specialized problems outside training distribution","Symbolic computation limited to reasoning-level explanations; cannot perform arbitrary symbolic algebra like Mathematica or SymPy","Numerical precision limited by floating-point representation; may produce rounding errors on high-precision calculations","May produce plausible-sounding but incorrect mathematical reasoning, especially on edge cases","No built-in verification against known mathematical databases or symbolic solvers"],"requires":["OpenRouter API key","Mathematical problem statements in clear text or LaTeX notation","Understanding that outputs require verification against known solutions"],"input_types":["mathematical problems in natural language","equations in LaTeX or plain text notation","proof verification requests","conceptual mathematics questions"],"output_types":["step-by-step solutions with intermediate calculations","mathematical proofs and derivations","explanations of mathematical concepts","verification of mathematical correctness"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tngtech-deepseek-r1t2-chimera__cap_6","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Provides text generation through OpenRouter's REST API with support for streaming responses (server-sent events) and batch processing. Requests are routed through OpenRouter's infrastructure, which handles load balancing, rate limiting, and provider selection. Streaming enables real-time token delivery for interactive applications, while batch processing allows asynchronous processing of multiple requests with optimized throughput. The API accepts standard OpenAI-compatible request formats.","intents":["Integrate text generation into web applications with real-time streaming responses","Process large volumes of text generation requests asynchronously without blocking","Build applications that display model outputs incrementally as tokens arrive","Scale inference across multiple requests with OpenRouter's managed infrastructure"],"best_for":["Web application developers building interactive AI features","Data teams processing large text generation workloads","Startups and small teams avoiding infrastructure management overhead"],"limitations":["API latency adds 100-500ms overhead compared to local inference","Streaming responses require client-side token buffering and parsing logic","Rate limiting and quota restrictions may throttle high-volume applications","No local deployment option; all inference depends on OpenRouter availability","API costs scale with token usage; no fixed-cost option for predictable workloads","Request/response format must conform to OpenRouter's API specification"],"requires":["OpenRouter API key with active billing","HTTP client library (curl, requests, fetch, etc.)","Network connectivity to OpenRouter endpoints","Understanding of OpenAI-compatible API format"],"input_types":["JSON request bodies with prompt, parameters, and configuration","OpenAI-compatible message format (system, user, assistant roles)"],"output_types":["streaming responses (server-sent events with token deltas)","complete responses (full text in single response)","structured metadata (token counts, finish reasons, model info)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key","HTTP/REST client capability","Network connectivity to OpenRouter endpoints","Understanding of MoE token routing behavior for cost estimation","OpenRouter API key with sufficient token quota","Client code to parse and extract reasoning traces from response","Understanding that longer outputs increase latency and cost","Code snippets or descriptions as input","Understanding that generated code requires review and testing","Well-structured prompts with clear instructions"],"failure_modes":["Mixture-of-experts routing adds ~15-25ms latency overhead per inference step compared to dense models","Expert load balancing may cause uneven token distribution, reducing effective parallelization on some hardware","Merged checkpoint approach may introduce subtle inconsistencies in reasoning patterns across different task domains","No built-in context window specification provided; actual maximum context length unknown from artifact data","Requires API access via OpenRouter; no local deployment option available","Reasoning traces increase output token count by 2-5x, raising API costs proportionally","Chain-of-thought reasoning may hallucinate plausible-sounding but incorrect intermediate steps","Reasoning quality degrades on out-of-distribution problems not similar to training data","No guarantee that reasoning traces reflect actual model computation — may be post-hoc rationalization","Explicit thinking output format not standardized; parsing reasoning traces requires custom logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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":"2026-05-24T12:16:25.059Z","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=tngtech-deepseek-r1t2-chimera","compare_url":"https://unfragile.ai/compare?artifact=tngtech-deepseek-r1t2-chimera"}},"signature":"0/La2M7vCKcM3wJUPEdqVeXb0bjGHuex+Bd2hY4gRqufczvrfIOg1CztiGJg/YnwwSXx+vrIrN+VlM3hdkvnBw==","signedAt":"2026-06-22T14:41:20.006Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tngtech-deepseek-r1t2-chimera","artifact":"https://unfragile.ai/tngtech-deepseek-r1t2-chimera","verify":"https://unfragile.ai/api/v1/verify?slug=tngtech-deepseek-r1t2-chimera","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"}}