{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mixtral-8x7b-instruct","slug":"mistralai-mixtral-8x7b-instruct","name":"Mistral: Mixtral 8x7B Instruct","type":"model","url":"https://openrouter.ai/models/mistralai~mixtral-8x7b-instruct","page_url":"https://unfragile.ai/mistralai-mixtral-8x7b-instruct","categories":["chatbots-assistants"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.40e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.instruction.following","name":"sparse-mixture-of-experts instruction following","description":"Mixtral 8x7B uses a Sparse Mixture of Experts (SMoE) architecture with 8 expert feed-forward networks that dynamically route tokens based on learned gating mechanisms, enabling 47B total parameters while activating only ~13B per forward pass. Each token is routed to 2 experts via a learned router network, allowing selective computation and efficient inference compared to dense models of equivalent capacity.","intents":["I need a model that can follow complex multi-step instructions with lower latency than dense 70B models","I want instruction-following capability without the full computational cost of a 47B parameter dense model","I need to understand which expert pathways are being activated for different instruction types"],"best_for":["teams building cost-sensitive instruction-following systems with latency constraints","developers prototyping multi-turn instruction agents where inference speed matters","organizations evaluating sparse architectures vs dense alternatives for production deployment"],"limitations":["Expert load balancing can be uneven during inference, causing some experts to be underutilized or overloaded depending on input distribution","Sparse routing adds ~5-10% latency overhead compared to dense forward passes due to gating computation and expert selection","No fine-grained control over expert routing at inference time — routing is entirely learned and deterministic per input","Requires sufficient batch size or sequence length to amortize expert computation; single-token inference may not see full SMoE benefits"],"requires":["API access via OpenRouter or direct Mistral API with valid authentication token","HTTP/REST client capable of streaming responses","Support for 32k token context window in calling application"],"input_types":["text (natural language instructions, prompts, multi-turn conversations)"],"output_types":["text (instruction responses, reasoning chains, structured outputs via prompt engineering)"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Mixtral 8x7B Instruct maintains conversation state across multiple turns by accepting full conversation history as input context, with a 32k token context window allowing deep multi-turn interactions. The model uses standard transformer attention mechanisms to track discourse context, speaker roles, and semantic dependencies across turns without explicit memory structures or external state management.","intents":["I need to build a chatbot that remembers context across 20+ conversation turns without losing coherence","I want to implement a multi-turn assistant that can reference earlier statements and maintain consistent reasoning across the conversation","I need to handle long-form dialogues where context from 5+ turns back influences current responses"],"best_for":["developers building conversational AI systems with deep context requirements","teams implementing customer support chatbots requiring multi-turn problem-solving","builders creating interactive tutoring or Socratic dialogue systems"],"limitations":["Context window is fixed at 32k tokens — conversations exceeding this length require truncation or summarization strategies","No explicit memory mechanism — all context must be included in each API call, increasing latency and token costs for long conversations","Attention computation scales quadratically with context length, causing noticeable latency increases beyond 20k tokens","No built-in conversation state persistence — external storage required to maintain conversation history across sessions"],"requires":["API client capable of formatting multi-turn messages in OpenAI-compatible chat format","Application-level conversation history management to track and format prior turns","Token counting utility to stay within 32k context window limits"],"input_types":["text (user messages, system prompts, conversation history)"],"output_types":["text (assistant responses, reasoning, follow-up questions)"],"categories":["text-generation-language","conversation-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_2","uri":"capability://code.generation.editing.code.aware.instruction.following.with.syntax.preservation","name":"code-aware instruction following with syntax preservation","description":"Mixtral 8x7B Instruct is trained on code-heavy instruction datasets and maintains syntactic correctness when generating code snippets, scripts, and technical explanations. The model learns to preserve language-specific syntax, indentation, and semantic structure through instruction-tuning on diverse programming tasks, without explicit AST parsing or syntax validation.","intents":["I need an instruction-following model that can generate syntactically correct code across multiple programming languages","I want to build a coding assistant that understands technical instructions and produces working code examples","I need a model that can explain code, refactor snippets, and answer programming questions while maintaining code quality"],"best_for":["developers building coding assistants or technical documentation generators","teams creating educational platforms for programming instruction","builders implementing code review or explanation features in IDEs"],"limitations":["No AST-based validation — generated code may have subtle logical errors or inefficiencies despite syntactic correctness","Limited to code patterns seen in training data; novel or domain-specific languages may produce lower-quality output","No real-time syntax checking or linting — errors only surface when code is executed or parsed externally","Context window limits prevent generating very large codebases or complex multi-file refactoring in single request"],"requires":["API access via OpenRouter or Mistral API","External code execution or validation environment to verify generated code correctness","Language-specific syntax highlighting or parsing for post-processing if strict validation needed"],"input_types":["text (code snippets, programming questions, refactoring requests, technical instructions)"],"output_types":["text (code snippets, explanations, refactored code, technical documentation)"],"categories":["code-generation-editing","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_3","uri":"capability://text.generation.language.structured.output.generation.via.prompt.engineering","name":"structured output generation via prompt engineering","description":"Mixtral 8x7B Instruct can generate structured outputs (JSON, YAML, XML, CSV) through instruction-based prompting that specifies output format constraints and examples. The model learns to follow format specifications from training data and prompt examples, producing parseable structured data without native schema validation or constrained decoding mechanisms.","intents":["I need to extract structured data from unstructured text using an instruction-following model","I want to generate JSON responses from natural language queries without building a separate parsing layer","I need a model that can follow format specifications to produce machine-readable outputs for downstream processing"],"best_for":["developers building data extraction pipelines using LLMs","teams implementing form-filling or structured data generation features","builders creating API wrappers around LLMs that need deterministic output formats"],"limitations":["No native schema validation — generated JSON/YAML may be malformed or incomplete, requiring post-processing validation","Format adherence depends on prompt quality and model instruction-following capability; complex schemas may fail","No constrained decoding — model can deviate from specified format, especially under low temperature or with ambiguous instructions","Hallucination risk remains high for structured outputs; model may invent fields or values not present in source data"],"requires":["Prompt engineering expertise to specify format clearly with examples","JSON/YAML parsing library to validate and handle malformed outputs","Retry logic or fallback mechanisms for format validation failures"],"input_types":["text (natural language queries, unstructured data, format specifications)"],"output_types":["text (JSON, YAML, XML, CSV, or other structured formats via prompt specification)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_4","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.response.generation","name":"reasoning and chain-of-thought response generation","description":"Mixtral 8x7B Instruct can generate step-by-step reasoning chains and multi-step problem-solving responses through instruction-tuning on reasoning-heavy datasets. The model learns to decompose complex problems into intermediate steps, explain reasoning, and arrive at conclusions, using transformer attention to track logical dependencies across reasoning steps without explicit planning modules.","intents":["I need a model that can explain its reasoning for complex questions or decisions","I want to generate step-by-step solutions to math, logic, or technical problems","I need an assistant that can break down complex tasks into intermediate reasoning steps for transparency"],"best_for":["developers building explainable AI systems or reasoning-focused assistants","teams creating educational tools that require step-by-step problem explanations","builders implementing transparency features in decision-support systems"],"limitations":["Reasoning quality degrades on problems requiring specialized domain knowledge or novel reasoning patterns","No formal verification of reasoning chains — logical errors can occur in intermediate steps","Reasoning length is constrained by context window; very deep reasoning chains may be truncated","Reasoning patterns are learned from training data; out-of-distribution reasoning problems may fail"],"requires":["Prompts that explicitly request step-by-step reasoning or chain-of-thought responses","Evaluation framework to validate reasoning correctness for domain-specific problems","Sufficient context window allocation for multi-step reasoning outputs"],"input_types":["text (questions, problems, reasoning prompts with examples)"],"output_types":["text (step-by-step reasoning, intermediate conclusions, final answers with explanations)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_5","uri":"capability://text.generation.language.multilingual.instruction.following.and.translation","name":"multilingual instruction following and translation","description":"Mixtral 8x7B Instruct supports instruction-following and translation across 10+ languages including English, French, Spanish, German, Italian, Portuguese, Dutch, Russian, Chinese, and Japanese. The model handles multilingual instructions, cross-lingual reasoning, and language-specific formatting through shared transformer embeddings and language-agnostic expert routing, enabling code-switching and multilingual conversations.","intents":["I need an instruction-following model that works across multiple languages without separate model deployments","I want to build a multilingual assistant that can handle mixed-language conversations and code-switching","I need to translate content while preserving instruction-following capability and context"],"best_for":["teams building global applications requiring multilingual support","developers creating translation or localization services with instruction-following","builders implementing multilingual chatbots or customer support systems"],"limitations":["Performance varies significantly across languages; non-English languages may have 10-20% lower instruction-following accuracy","Language detection is implicit; ambiguous multilingual inputs may be misinterpreted","No explicit language tagging in API — language selection relies on prompt context or input language detection","Transliteration and script handling may fail for low-resource languages or non-standard encodings"],"requires":["API access via OpenRouter or Mistral API","Language detection library for routing multilingual inputs correctly","UTF-8 encoding support for non-Latin scripts"],"input_types":["text (instructions and prompts in 10+ languages, mixed-language inputs)"],"output_types":["text (responses in target language, translations, multilingual outputs)"],"categories":["text-generation-language","multilingual-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_6","uri":"capability://tool.use.integration.api.based.inference.with.streaming.response.support","name":"api-based inference with streaming response support","description":"Mixtral 8x7B Instruct is deployed via OpenRouter and Mistral's API with HTTP REST endpoints supporting streaming responses via Server-Sent Events (SSE). Responses are streamed token-by-token, enabling real-time display of model outputs and reduced perceived latency in user-facing applications. The API handles batching, load balancing, and infrastructure management transparently.","intents":["I need to integrate a powerful instruction-following model into my application without managing GPU infrastructure","I want to stream model responses to users in real-time for better UX","I need API-based access to a 47B parameter model with predictable pricing and SLA guarantees"],"best_for":["startups and small teams without ML infrastructure expertise","developers building user-facing applications requiring real-time response streaming","teams needing predictable inference costs without GPU procurement"],"limitations":["Network latency adds 50-200ms overhead compared to local inference; not suitable for sub-100ms latency requirements","API rate limits and quota constraints may throttle high-volume applications","Streaming adds complexity to client-side implementation; non-streaming requests may be simpler","API pricing scales with token usage; high-volume applications may exceed budget constraints"],"requires":["Valid API key from OpenRouter or Mistral AI","HTTP client library with streaming support (e.g., httpx, requests with streaming)","Network connectivity and HTTPS support","Token counting library to estimate costs and stay within budget"],"input_types":["text (prompts, instructions, conversation history)"],"output_types":["text (streamed tokens via SSE, complete responses)"],"categories":["tool-use-integration","api-access"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_7","uri":"capability://tool.use.integration.function.calling.and.tool.use.via.prompt.engineering","name":"function calling and tool use via prompt engineering","description":"Mixtral 8x7B Instruct can be prompted to generate function calls and tool invocations through instruction-based specification of available tools, their parameters, and expected output formats. The model learns to select appropriate tools, format parameters correctly, and chain multiple tool calls through training on tool-use examples, without native function-calling APIs or schema validation.","intents":["I need a model that can decide when to call external tools and generate properly formatted function calls","I want to build an agent that can use APIs, databases, or custom functions based on user requests","I need a model that can chain multiple tool calls to solve complex tasks"],"best_for":["developers building LLM agents with external tool integration","teams creating autonomous systems that interact with APIs or databases","builders implementing tool-use capabilities without native function-calling support"],"limitations":["No native schema validation — generated function calls may have incorrect parameter types or missing required fields","Tool selection accuracy depends on prompt quality and training data coverage; novel tools may fail","No built-in error handling for tool execution failures; requires external error recovery logic","Function call parsing requires custom logic; no standardized output format like OpenAI's function calling"],"requires":["Detailed tool specifications in prompts with parameter descriptions and examples","JSON/YAML parser to extract function calls from model output","Tool execution framework to invoke functions and handle results","Error handling and retry logic for failed tool calls"],"input_types":["text (user requests, tool specifications, tool descriptions)"],"output_types":["text (function calls in specified format, tool invocation chains, results)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mixtral-8x7b-instruct__cap_8","uri":"capability://safety.moderation.content.moderation.and.safety.aware.response.generation","name":"content moderation and safety-aware response generation","description":"Mixtral 8x7B Instruct is instruction-tuned to decline harmful requests, avoid generating toxic content, and provide safety-aware responses through alignment training. The model learns to recognize unsafe requests, explain why it cannot fulfill them, and suggest safe alternatives, without explicit content filtering or external moderation APIs.","intents":["I need a model that refuses harmful requests and explains why it cannot help with unsafe tasks","I want to deploy an instruction-following model that minimizes toxic or harmful outputs","I need a model that can provide safety-aware responses while remaining helpful for legitimate requests"],"best_for":["teams building public-facing applications requiring content safety","developers creating customer-facing chatbots with safety requirements","builders implementing responsible AI practices without external moderation"],"limitations":["Safety alignment is not perfect; adversarial prompts or jailbreaks may bypass safety mechanisms","Safety decisions are learned heuristics; edge cases may be misclassified as safe or unsafe","No fine-grained control over safety thresholds; cannot customize safety levels per use case","Safety mechanisms may over-refuse legitimate requests, reducing helpfulness"],"requires":["Understanding of model safety limitations and potential jailbreak vectors","Monitoring and logging of model outputs for safety violations","User feedback mechanisms to identify and address safety failures"],"input_types":["text (user requests, potentially harmful prompts)"],"output_types":["text (safe responses, refusals with explanations, alternative suggestions)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or direct Mistral API with valid authentication token","HTTP/REST client capable of streaming responses","Support for 32k token context window in calling application","API client capable of formatting multi-turn messages in OpenAI-compatible chat format","Application-level conversation history management to track and format prior turns","Token counting utility to stay within 32k context window limits","API access via OpenRouter or Mistral API","External code execution or validation environment to verify generated code correctness","Language-specific syntax highlighting or parsing for post-processing if strict validation needed","Prompt engineering expertise to specify format clearly with examples"],"failure_modes":["Expert load balancing can be uneven during inference, causing some experts to be underutilized or overloaded depending on input distribution","Sparse routing adds ~5-10% latency overhead compared to dense forward passes due to gating computation and expert selection","No fine-grained control over expert routing at inference time — routing is entirely learned and deterministic per input","Requires sufficient batch size or sequence length to amortize expert computation; single-token inference may not see full SMoE benefits","Context window is fixed at 32k tokens — conversations exceeding this length require truncation or summarization strategies","No explicit memory mechanism — all context must be included in each API call, increasing latency and token costs for long conversations","Attention computation scales quadratically with context length, causing noticeable latency increases beyond 20k tokens","No built-in conversation state persistence — external storage required to maintain conversation history across sessions","No AST-based validation — generated code may have subtle logical errors or inefficiencies despite syntactic correctness","Limited to code patterns seen in training data; novel or domain-specific languages may produce lower-quality output","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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-8x7b-instruct","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mixtral-8x7b-instruct"}},"signature":"uUFbesRAmxmiNAUy7POGr1naGt14NsDAUiPOFzcGpTuanSGUQLdxHLYBvcDmf/HJBynYxBmzTfXT9LcfOZJnCA==","signedAt":"2026-06-20T08:25:33.220Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mixtral-8x7b-instruct","artifact":"https://unfragile.ai/mistralai-mixtral-8x7b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mixtral-8x7b-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"}}