{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mixtral-8x22b-instruct","slug":"mistralai-mixtral-8x22b-instruct","name":"Mistral: Mixtral 8x22B Instruct","type":"finetune","url":"https://openrouter.ai/models/mistralai~mixtral-8x22b-instruct","page_url":"https://unfragile.ai/mistralai-mixtral-8x22b-instruct","categories":["model-training"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.instruction.following","name":"sparse-mixture-of-experts instruction following","description":"Implements a sparse mixture-of-experts (MoE) architecture with 8 expert modules, each containing 22B parameters, where only 2 experts are activated per token via a learned gating mechanism. This design achieves 39B active parameters out of 141B total, enabling instruction-following at near-70B model quality while maintaining inference efficiency comparable to 13B models. The routing mechanism learns which expert combinations best handle different token types (code, math, reasoning, general text) during fine-tuning.","intents":["I need a model that can handle complex instructions (math, coding, reasoning) without the latency and cost of running a full 70B parameter model","I want to deploy an instruction-following model with reasonable token throughput for production chat applications","I need strong performance across diverse domains (code, math, general reasoning) with efficient resource utilization"],"best_for":["teams building cost-sensitive production chat APIs","developers deploying multi-domain instruction-following systems with throughput constraints","organizations migrating from larger models (70B+) seeking efficiency without major quality loss"],"limitations":["MoE routing adds ~5-10ms latency per token due to gating computation and expert selection overhead","Expert load balancing can be uneven; some experts may be underutilized for certain input distributions, reducing effective parameter efficiency","Requires sufficient VRAM to hold all 141B parameters in memory even though only 39B are active per forward pass (typically 80GB+ GPU memory)","Fine-tuning on custom domains may require careful data distribution to avoid expert specialization collapse"],"requires":["API access via OpenRouter or direct Mistral API","Minimum context window of 32K tokens supported","For self-hosted deployment: 80GB+ VRAM (A100 80GB or equivalent)","HTTP/REST client or SDK supporting streaming responses"],"input_types":["text (natural language instructions)","code snippets (for code generation/analysis tasks)","mathematical expressions and problem statements","multi-turn conversation history"],"output_types":["text (instruction responses)","code (generation, refactoring, explanation)","structured reasoning (step-by-step math solutions)","streaming tokens (for real-time response generation)"],"categories":["text-generation-language","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_1","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Trained with specialized instruction data for mathematical problem-solving, enabling step-by-step symbolic reasoning, algebraic manipulation, and multi-step calculation chains. The model learns to decompose complex math problems into intermediate steps, apply mathematical rules, and verify solutions. This capability emerges from both the base Mixtral architecture and the instruct fine-tuning process that emphasizes reasoning transparency.","intents":["I need to solve multi-step math problems with intermediate reasoning shown","I want to verify mathematical correctness of solutions or identify errors in calculations","I need symbolic algebra, calculus, or statistics problem solving in an automated system"],"best_for":["educational technology platforms requiring math tutoring or problem verification","scientific computing pipelines needing symbolic reasoning before numerical computation","developers building math-heavy chatbots or homework assistance tools"],"limitations":["Performance degrades on highly specialized mathematical domains (advanced topology, category theory) not well-represented in training data","May produce plausible-sounding but incorrect symbolic manipulations without explicit verification against a computer algebra system","Context window limits prevent solving extremely long multi-step problems requiring >32K tokens of intermediate work","No built-in integration with symbolic math engines (Wolfram Alpha, SymPy) — requires separate orchestration for verification"],"requires":["API access to Mixtral 8x22B Instruct via OpenRouter or Mistral API","Structured prompting with clear problem statement and expected output format","Optional: integration with SymPy or similar for symbolic verification"],"input_types":["text (math problem statements in natural language or LaTeX)","structured problem definitions (JSON with problem parameters)","multi-turn conversation with follow-up questions"],"output_types":["text (step-by-step solutions with reasoning)","LaTeX-formatted mathematical expressions","structured JSON with intermediate steps and final answer"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Generates syntactically correct code across 40+ programming languages through instruction-tuned patterns learned from diverse code repositories and technical documentation. The model understands code structure, common idioms, error patterns, and best practices for each language. It can generate complete functions, debug existing code, explain technical concepts, and suggest optimizations by leveraging both the base model's code understanding and the instruct fine-tuning that emphasizes clarity and correctness.","intents":["I need to generate working code snippets or complete functions in multiple languages","I want to understand what existing code does or get suggestions for refactoring","I need help debugging code or understanding error messages and their solutions"],"best_for":["developers using AI-assisted coding in IDEs or chat interfaces","teams building code generation pipelines or automated testing systems","technical documentation systems that need to generate code examples"],"limitations":["Generated code may contain subtle bugs or security vulnerabilities not caught by syntax checking — requires human review and testing","Performance varies significantly by language; excels at Python, JavaScript, Java but may struggle with niche languages or domain-specific languages","Cannot access external package documentation or latest library APIs beyond training cutoff; may generate code using deprecated patterns","Context window of 32K tokens limits ability to work with very large codebases or generate extremely long functions"],"requires":["API access to Mixtral 8x22B Instruct","Clear code context or problem statement in the prompt","For IDE integration: compatible plugin/extension (VS Code, JetBrains, etc.)","Testing framework to validate generated code before deployment"],"input_types":["text (natural language code requests)","code snippets (for refactoring, debugging, or completion)","error messages and stack traces","technical specifications or pseudocode"],"output_types":["code (functions, classes, complete programs)","explanations (how code works, why errors occur)","structured suggestions (refactoring options, performance improvements)","test cases and usage examples"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_3","uri":"capability://text.generation.language.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Maintains coherent conversation state across multiple turns by processing full conversation history within the 32K token context window, allowing the model to reference previous statements, correct misunderstandings, and build on prior context. The instruction fine-tuning teaches the model to track conversation state, acknowledge context shifts, and maintain consistent persona and knowledge across turns without explicit state management.","intents":["I need a chatbot that remembers what was discussed earlier in the conversation","I want to have natural back-and-forth dialogue where the model understands context and can clarify or expand on previous answers","I need to build a multi-turn assistant that can handle complex workflows requiring conversation history"],"best_for":["developers building conversational AI applications (chatbots, customer support, tutoring)","teams creating interactive debugging or code review assistants","applications requiring stateful dialogue without external session management"],"limitations":["Context window of 32K tokens limits conversation history to roughly 8,000-10,000 words before requiring truncation or summarization","No built-in conversation persistence — requires external database to store and retrieve conversation history across sessions","Model has no explicit memory of conversations beyond the current context window; cannot reference discussions from previous sessions without explicit retrieval","Long conversations may experience degraded performance as context grows, with earlier turns receiving less attention than recent ones"],"requires":["API access to Mixtral 8x22B Instruct with streaming support","Conversation history management system (client-side or server-side)","Optional: vector database or summarization system for managing context overflow","HTTP client supporting streaming responses for real-time token delivery"],"input_types":["text (user messages in multi-turn format)","conversation history (array of user/assistant message pairs)","system prompts (for persona/behavior specification)"],"output_types":["text (assistant responses)","streaming tokens (for real-time response generation)","structured conversation metadata (turn count, token usage)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_4","uri":"capability://text.generation.language.streaming.token.generation.with.real.time.response.delivery","name":"streaming token generation with real-time response delivery","description":"Generates responses token-by-token and streams them to the client in real-time via HTTP streaming (Server-Sent Events or chunked transfer encoding), enabling progressive response display without waiting for complete generation. The API returns tokens as they are generated by the model, allowing clients to display partial responses and provide immediate feedback to users while the full response is still being computed.","intents":["I want to display model responses progressively to users without waiting for the complete response","I need to reduce perceived latency in chat applications by showing tokens as they arrive","I want to build interactive applications that can process partial responses or interrupt generation mid-stream"],"best_for":["web and mobile chat applications requiring responsive UX","real-time collaborative tools (pair programming, live tutoring)","applications with strict latency requirements where progressive display improves user experience"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial response has been sent to client","Token-by-token delivery prevents certain optimizations (e.g., batching, caching) that could improve throughput","Client must implement proper stream handling to avoid connection timeouts or incomplete response processing","Streaming responses cannot be easily cached or reused since they are consumed as a stream rather than stored as complete objects"],"requires":["HTTP client with streaming support (fetch API with ReadableStream, axios with responseType: 'stream', etc.)","Server-side infrastructure supporting Server-Sent Events or chunked transfer encoding","Proper error handling for mid-stream failures","API key for OpenRouter or Mistral API with streaming enabled"],"input_types":["text (prompts and conversation history)","streaming configuration (max tokens, temperature, etc.)"],"output_types":["streaming tokens (individual tokens delivered via HTTP stream)","structured metadata (token count, finish reason, usage statistics)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_5","uri":"capability://text.generation.language.instruction.following.with.format.specification","name":"instruction-following with format specification","description":"Responds to structured instructions that specify output format (JSON, XML, Markdown, plain text, code blocks) and follows those format constraints with high consistency. The instruction fine-tuning teaches the model to parse format requirements from prompts and generate responses that conform to specified schemas, enabling reliable structured output extraction without requiring separate parsing layers.","intents":["I need the model to return responses in a specific format (JSON, XML, CSV) for downstream processing","I want to extract structured data from unstructured text using format-constrained generation","I need to integrate model outputs directly into pipelines that expect specific data structures"],"best_for":["developers building data extraction pipelines or ETL systems","teams integrating LLM outputs into structured databases or APIs","applications requiring reliable format compliance without post-processing"],"limitations":["Format compliance is probabilistic, not guaranteed — model may occasionally violate format constraints, especially for complex nested structures","No schema validation built-in; requires client-side validation and retry logic for format errors","Complex nested structures or very strict schemas may require multiple attempts or explicit schema examples in the prompt","Format specification adds tokens to the prompt, increasing API costs and reducing available context for the actual task"],"requires":["Clear format specification in the prompt (e.g., 'Return JSON with keys: name, age, email')","Optional: JSON schema or format examples in the prompt for complex structures","Client-side validation and error handling for format violations","API access to Mixtral 8x22B Instruct"],"input_types":["text (instructions with format specification)","unstructured data (for extraction tasks)","schema definitions (JSON schema, XML DTD, etc.)"],"output_types":["structured text (JSON, XML, CSV, YAML)","code blocks (for code generation with format constraints)","markdown (for formatted documentation)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_6","uri":"capability://planning.reasoning.domain.specific.knowledge.synthesis.across.code.math.and.reasoning","name":"domain-specific knowledge synthesis across code, math, and reasoning","description":"Synthesizes knowledge across multiple specialized domains (software engineering, mathematics, logic, natural language reasoning) by routing different types of problems to specialized expert modules within the MoE architecture. When processing a request, the gating mechanism activates experts that have learned to handle that specific domain, enabling coherent responses that combine domain-specific knowledge with general reasoning capabilities.","intents":["I need to solve problems that span multiple domains (e.g., implementing a mathematical algorithm in code)","I want consistent, high-quality responses across diverse technical domains without switching between different models","I need a single model that can handle code, math, and general reasoning equally well"],"best_for":["technical teams building multi-domain AI assistants","educational platforms covering STEM subjects comprehensively","research or engineering teams needing cross-domain problem-solving"],"limitations":["Expert specialization may create domain boundaries where cross-domain problems are handled less effectively than single-domain models","Performance on hybrid problems (e.g., mathematical code generation) may be lower than using separate specialized models for each domain","Expert load balancing can be uneven; some domains may receive more expert capacity than others based on training data distribution","Fine-tuning for new domains may require careful data curation to avoid disrupting existing expert specializations"],"requires":["API access to Mixtral 8x22B Instruct","Clear problem statement that may span multiple domains","Optional: domain-specific context or examples to guide expert routing"],"input_types":["text (problems spanning code, math, reasoning, or general domains)","code snippets with mathematical or logical requirements","multi-part problems requiring reasoning across domains"],"output_types":["text (coherent responses synthesizing multiple domains)","code (with mathematical or logical correctness)","step-by-step reasoning (combining domain-specific and general reasoning)"],"categories":["planning-reasoning","code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_7","uri":"capability://text.generation.language.long.context.processing.with.32k.token.window","name":"long-context processing with 32k token window","description":"Processes input sequences up to 32,000 tokens (approximately 24,000 words or 100+ pages of text) in a single request, enabling analysis of entire documents, codebases, or conversation histories without chunking or summarization. The model maintains attention across the full context window, allowing it to reference information from any part of the input and generate coherent responses that integrate information from the entire context.","intents":["I need to analyze or summarize very long documents without splitting them into chunks","I want to work with entire code repositories or multiple files in a single request","I need to maintain conversation history across many turns without losing context"],"best_for":["document analysis and summarization systems","code review and refactoring tools working with large codebases","long-form content generation and editing","research and knowledge synthesis from multiple sources"],"limitations":["Longer context increases latency; processing 32K tokens takes significantly longer than processing 4K tokens","Longer context increases API costs proportionally (charged per input token)","Attention mechanism may dilute focus on specific parts of very long contexts; information from the middle of long documents may receive less attention than beginning or end","Not all downstream applications can handle 32K token responses; integration may require chunking or summarization on the output side"],"requires":["API access to Mixtral 8x22B Instruct with 32K context support","Sufficient API quota and budget for longer requests","Client-side handling of potentially large responses (may exceed typical response size limits)"],"input_types":["text (documents, code, conversation history up to 32K tokens)","multiple files or documents concatenated into a single input","long conversation histories with full message context"],"output_types":["text (analysis, summaries, responses integrating full context)","code (refactored or generated based on full codebase context)","structured output (JSON, XML) extracted from long documents"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_8","uri":"capability://text.generation.language.few.shot.learning.and.in.context.adaptation","name":"few-shot learning and in-context adaptation","description":"Learns task-specific patterns from examples provided in the prompt (few-shot learning) without requiring model fine-tuning or retraining. By including a few examples of the desired input-output pattern in the prompt, the model adapts its behavior to match those examples, enabling rapid task customization for specific use cases like custom classification, extraction patterns, or domain-specific formatting.","intents":["I need to customize the model's behavior for a specific task without fine-tuning","I want to teach the model a custom classification scheme or extraction pattern through examples","I need to adapt the model to domain-specific terminology or formatting conventions quickly"],"best_for":["rapid prototyping of custom NLP tasks","teams needing task-specific behavior without the overhead of fine-tuning","applications requiring dynamic task adaptation based on user-provided examples"],"limitations":["Few-shot learning quality depends heavily on example quality and relevance; poor examples can degrade performance","Examples consume tokens from the context window, reducing available space for actual task input","Performance on few-shot tasks is generally lower than fine-tuned models; typically requires 3-5 high-quality examples to achieve reasonable accuracy","No persistent learning; each request must include examples, and the model cannot improve from feedback across requests without explicit fine-tuning"],"requires":["Well-crafted examples demonstrating the desired task behavior","Clear task description or system prompt","API access to Mixtral 8x22B Instruct","Understanding of prompt engineering best practices"],"input_types":["text (task description + examples + actual input)","structured examples (JSON, CSV, or other formats demonstrating input-output pairs)"],"output_types":["text (adapted to the pattern demonstrated by examples)","structured output (matching the format of provided examples)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x22b-instruct__cap_9","uri":"capability://planning.reasoning.natural.language.explanation.and.reasoning.transparency","name":"natural language explanation and reasoning transparency","description":"Generates detailed explanations of its reasoning process, breaking down complex problems into steps and articulating the logic behind conclusions. The instruction fine-tuning teaches the model to prioritize transparency, explicitly stating assumptions, intermediate reasoning steps, and decision points rather than jumping directly to answers. This enables users to understand and verify the model's reasoning.","intents":["I need the model to explain its reasoning so I can verify correctness or identify errors","I want to understand how the model arrived at a particular conclusion or recommendation","I need transparent reasoning for educational purposes or to build trust in the model's outputs"],"best_for":["educational applications where understanding reasoning is as important as the answer","high-stakes applications (medical, legal, financial) requiring explainability","debugging and verification workflows where reasoning transparency is critical"],"limitations":["Explicit reasoning increases response length and token consumption, raising API costs","Reasoning transparency does not guarantee correctness; the model can provide detailed but incorrect reasoning","Some domains (e.g., intuitive pattern recognition) may not have clear step-by-step reasoning to articulate","Overly detailed reasoning can obscure key insights or make responses harder to parse"],"requires":["Prompts that explicitly request reasoning or explanation","API access to Mixtral 8x22B Instruct","Client-side parsing of reasoning steps if structured output is needed"],"input_types":["text (questions or problems requesting explanation)","prompts with explicit requests for step-by-step reasoning"],"output_types":["text (detailed explanations with reasoning steps)","structured reasoning (numbered steps, decision trees, logical chains)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Mistral API","Minimum context window of 32K tokens supported","For self-hosted deployment: 80GB+ VRAM (A100 80GB or equivalent)","HTTP/REST client or SDK supporting streaming responses","API access to Mixtral 8x22B Instruct via OpenRouter or Mistral API","Structured prompting with clear problem statement and expected output format","Optional: integration with SymPy or similar for symbolic verification","API access to Mixtral 8x22B Instruct","Clear code context or problem statement in the prompt","For IDE integration: compatible plugin/extension (VS Code, JetBrains, etc.)"],"failure_modes":["MoE routing adds ~5-10ms latency per token due to gating computation and expert selection overhead","Expert load balancing can be uneven; some experts may be underutilized for certain input distributions, reducing effective parameter efficiency","Requires sufficient VRAM to hold all 141B parameters in memory even though only 39B are active per forward pass (typically 80GB+ GPU memory)","Fine-tuning on custom domains may require careful data distribution to avoid expert specialization collapse","Performance degrades on highly specialized mathematical domains (advanced topology, category theory) not well-represented in training data","May produce plausible-sounding but incorrect symbolic manipulations without explicit verification against a computer algebra system","Context window limits prevent solving extremely long multi-step problems requiring >32K tokens of intermediate work","No built-in integration with symbolic math engines (Wolfram Alpha, SymPy) — requires separate orchestration for verification","Generated code may contain subtle bugs or security vulnerabilities not caught by syntax checking — requires human review and testing","Performance varies significantly by language; excels at Python, JavaScript, Java but may struggle with niche languages or domain-specific languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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":"2026-05-03T15:20:45.777Z","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=mistralai-mixtral-8x22b-instruct","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mixtral-8x22b-instruct"}},"signature":"+vcJLGliXjXlGhRbfyIb6c6SiviVdP61Vk2nFSM3UpIdNH3Y52cameFz5OZWVkb5PlbkPHttJPYl6UoEcmvBAw==","signedAt":"2026-06-20T08:25:57.207Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mixtral-8x22b-instruct","artifact":"https://unfragile.ai/mistralai-mixtral-8x22b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mixtral-8x22b-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"}}