{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-mixtral","slug":"mixtral","name":"Mixtral (8x7B)","type":"model","url":"https://ollama.com/library/mixtral","page_url":"https://unfragile.ai/mixtral","categories":["text-writing"],"tags":["ollama","open-source","mistral-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-mixtral__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.text.generation.with.dynamic.expert.routing","name":"sparse-mixture-of-experts text generation with dynamic expert routing","description":"Mixtral implements a Sparse Mixture-of-Experts (SMoE) architecture where 8 expert networks (each 7B parameters) are dynamically routed per token via a learned gating mechanism, activating only 2 experts per forward pass. This reduces computational cost compared to dense models while maintaining quality through selective expert specialization. The model generates text autoregressively using only the active expert parameters, enabling efficient inference on consumer-grade GPUs.","intents":["Run a capable language model locally without enterprise GPU infrastructure","Generate coherent multi-turn conversations with 32K token context","Balance inference speed and model quality for real-time applications","Reduce VRAM requirements compared to dense 56B+ parameter models"],"best_for":["Solo developers building local LLM agents without cloud dependencies","Teams deploying on-premises AI without API costs","Researchers experimenting with mixture-of-experts architectures"],"limitations":["Only ~12.9B parameters active per token (2 of 8 experts), reducing expressiveness vs dense models of equivalent total size","32K token context window is fixed hard limit; cannot process documents longer than ~24,000 words","Expert routing adds ~5-10% computational overhead vs dense models due to gating network evaluation","No documented performance benchmarks against GPT-3.5, Claude, or Llama 2 — claims of 'new standard' are unquantified"],"requires":["Ollama runtime (macOS, Windows, Linux, or Docker)","26GB GPU VRAM minimum for 8x7b variant (actual requirement depends on quantization; unspecified)","Python 3.8+ or Node.js 14+ for SDK integration (optional; CLI works standalone)"],"input_types":["text (plain text, code, markdown, JSON)"],"output_types":["text (streaming or buffered completion)"],"categories":["text-generation-language","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_1","uri":"capability://code.generation.editing.code.generation.with.mathematical.reasoning","name":"code generation with mathematical reasoning","description":"Mixtral is trained with explicit emphasis on code and mathematical problem-solving, enabling it to generate syntactically correct code across multiple languages and solve multi-step mathematical problems. The model leverages its expert routing to specialize certain experts on code patterns and symbolic reasoning, producing output that can be directly executed or used in computational workflows.","intents":["Generate working code snippets in Python, JavaScript, Java, C++, and other languages","Solve algorithmic problems and explain step-by-step mathematical derivations","Debug code by analyzing error messages and suggesting fixes","Translate between programming languages with semantic preservation"],"best_for":["Developers building code-generation features into applications","Data scientists prototyping mathematical models locally","Teams needing offline code completion without cloud API calls"],"limitations":["No explicit verification that generated code is syntactically correct or executable — requires post-generation testing","Mathematical reasoning limited to problems solvable within 32K token context; cannot handle multi-file codebases larger than ~20K lines","No documented accuracy metrics for code generation (e.g., pass@1 on HumanEval benchmark)","Trained on unknown dataset composition — code quality may reflect biases in training data"],"requires":["Ollama runtime with 26GB VRAM","Text input containing code or mathematical problem statement"],"input_types":["text (code snippets, pseudocode, mathematical notation, natural language problem descriptions)"],"output_types":["text (executable code, mathematical proofs, step-by-step solutions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_10","uri":"capability://data.processing.analysis.embedding.generation.for.semantic.search.and.rag","name":"embedding generation for semantic search and rag","description":"Mixtral via Ollama supports embedding generation, converting text into dense vector representations that capture semantic meaning. These embeddings can be stored in vector databases and used for semantic search, retrieval-augmented generation (RAG), or similarity comparisons without requiring a separate embedding model.","intents":["Generate embeddings for documents to enable semantic search","Build RAG systems that retrieve relevant context before generation","Find semantically similar documents or code snippets","Cluster documents or user queries by semantic meaning"],"best_for":["Teams building RAG systems with local inference","Developers needing embeddings without external API calls","Organizations with data residency requirements"],"limitations":["Embedding model architecture and dimensionality are undocumented (unclear if Mixtral generates embeddings natively or via adapter)","No documented embedding quality metrics or comparison to specialized embedding models (e.g., OpenAI text-embedding-3-large)","Embedding generation latency is unquantified; unclear if it's faster/slower than dedicated embedding models","No documented vector database integrations; users must implement their own storage and retrieval"],"requires":["Ollama runtime with embedding support enabled","Text input (documents, queries, code snippets)","Vector database or similarity search library (e.g., Pinecone, Weaviate, FAISS)"],"input_types":["text (documents, queries, code)"],"output_types":["JSON (embedding vector with float array)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_11","uri":"capability://automation.workflow.quantization.and.model.size.optimization.for.consumer.gpus","name":"quantization and model size optimization for consumer gpus","description":"Mixtral weights are distributed in 'native' format via Ollama, with quantization options applied at runtime to fit models into consumer GPU VRAM. The Ollama runtime selects quantization levels (e.g., 4-bit, 8-bit) based on available VRAM, trading off model quality for memory efficiency without requiring manual quantization or retraining.","intents":["Run 26GB/80GB models on consumer GPUs with 8GB-24GB VRAM via quantization","Automatically select optimal quantization level based on available hardware","Reduce inference latency by using lower-precision arithmetic (e.g., int4 vs float32)"],"best_for":["Developers with limited GPU VRAM (RTX 3060, RTX 4060, etc.)","Teams optimizing for inference speed over maximum quality","Organizations reducing power consumption and cooling requirements"],"limitations":["Quantization levels and formats are undocumented; unclear what quantization schemes are available (4-bit, 8-bit, mixed-precision?)","No documented quality degradation metrics for different quantization levels (e.g., BLEU score loss for 4-bit vs float32)","Quantization is applied automatically by Ollama; users have no control over quantization strategy or parameters","No documented support for advanced quantization techniques (e.g., GPTQ, AWQ, or dynamic quantization)"],"requires":["Ollama runtime","GPU with sufficient VRAM for quantized model (exact requirements depend on quantization level, undocumented)"],"input_types":["none (quantization is applied automatically at model load time)"],"output_types":["quantized model weights in Ollama format"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_12","uri":"capability://tool.use.integration.pre.built.integrations.with.ai.development.frameworks","name":"pre-built integrations with ai development frameworks","description":"Mixtral is integrated into popular AI development frameworks and applications (Claude Code, Codex, OpenCode, OpenClaw, Hermes Agent) via Ollama's API, allowing developers to use Mixtral as a backend without writing integration code. These integrations expose Mixtral through framework-specific abstractions (e.g., LangChain, LlamaIndex).","intents":["Use Mixtral as a drop-in replacement for OpenAI/Anthropic APIs in LangChain applications","Build agents with Mixtral using existing agent frameworks without custom code","Leverage Mixtral in IDE plugins or code editors that support Ollama"],"best_for":["Developers already using LangChain, LlamaIndex, or other frameworks","Teams migrating from cloud APIs to local inference","Developers building IDE extensions or code editors"],"limitations":["Integration list is incomplete and undocumented; unclear which frameworks fully support Mixtral vs partial support","No documented API compatibility matrix (e.g., which LangChain features work with Mixtral?)","Integration maintenance is unclear; no SLA or update schedule documented","Custom integrations require implementing Ollama REST API calls manually"],"requires":["Ollama runtime","Supported framework (LangChain, LlamaIndex, etc.) with Mixtral integration"],"input_types":["framework-specific (e.g., LangChain PromptTemplate, LlamaIndex QueryEngine)"],"output_types":["framework-specific (e.g., LangChain AIMessage, LlamaIndex Response)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_2","uri":"capability://tool.use.integration.native.function.calling.with.schema.based.routing","name":"native function calling with schema-based routing","description":"Mixtral 8x22b variant natively supports function calling by generating structured JSON that conforms to provided function schemas, enabling the model to invoke external tools without additional fine-tuning. The model learns to map user intents to function calls by understanding schema constraints, allowing integration with APIs, databases, and custom tools through a standardized calling convention.","intents":["Build AI agents that call external APIs (weather, search, payment processing) based on user requests","Create chatbots that query databases or knowledge bases in response to questions","Orchestrate multi-step workflows where the model decides which tools to invoke","Integrate Mixtral into existing tool-use frameworks without custom adapters"],"best_for":["Developers building agentic systems with local inference","Teams integrating Mixtral into LangChain or LlamaIndex workflows","Organizations requiring tool calling without cloud API dependencies"],"limitations":["Only documented for 8x22b variant (80GB model size); 8x7b capability unknown","Requires explicit schema definition in JSON Schema format; no automatic schema inference","No built-in error handling for invalid function calls or missing parameters — application must validate and retry","No documented accuracy for complex multi-step tool sequences or nested function calls"],"requires":["Mixtral 8x22b variant (not 8x7b)","80GB GPU VRAM minimum","Function schemas defined in JSON Schema format","Ollama runtime with function-calling support enabled"],"input_types":["text (natural language user request) + JSON Schema (function definitions)"],"output_types":["JSON (function call with parameters) + text (reasoning or explanation)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_3","uri":"capability://text.generation.language.multi.language.text.generation.with.language.specific.expert.routing","name":"multi-language text generation with language-specific expert routing","description":"Mixtral 8x22b is trained on English, French, Italian, German, and Spanish, with expert routing potentially specializing certain experts on language-specific patterns (morphology, syntax, idioms). The model generates fluent text in any of these languages and can perform code-switching or translation tasks by leveraging shared semantic understanding across experts.","intents":["Generate customer support responses in multiple European languages from a single model","Translate between supported languages while preserving meaning and tone","Build multilingual chatbots without maintaining separate models per language","Process and respond to user input in the user's native language"],"best_for":["European SaaS companies serving multilingual user bases","Teams deploying single models across multiple language markets","Developers building translation or localization features"],"limitations":["Only 5 languages supported (English, French, Italian, German, Spanish); no Asian, African, or other language families","No documented translation quality metrics or comparison to specialized translation models","Language detection is implicit (model infers from input); no explicit language tagging mechanism","8x7b variant language support is undocumented — may not support all 5 languages"],"requires":["Mixtral 8x22b variant (8x7b support unknown)","80GB GPU VRAM","Text input in one of the 5 supported languages"],"input_types":["text (in English, French, Italian, German, or Spanish)"],"output_types":["text (in any of the 5 supported languages)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_4","uri":"capability://text.generation.language.long.context.document.analysis.with.64k.token.window","name":"long-context document analysis with 64k token window","description":"Mixtral 8x22b supports a 64K token context window (approximately 48,000 words), enabling the model to ingest entire documents, codebases, or conversation histories in a single prompt and perform analysis, summarization, or question-answering without chunking or retrieval. The model maintains coherence across the full context by using standard transformer attention mechanisms scaled to 64K positions.","intents":["Analyze entire research papers or technical documentation in one pass","Answer questions about large codebases by loading all files into context","Summarize long documents or meeting transcripts without losing detail","Maintain multi-turn conversations with full history for context-aware responses"],"best_for":["Researchers analyzing academic papers or technical specifications","Code review teams analyzing large pull requests or entire modules","Customer support teams handling complex multi-turn conversations","Legal or compliance teams reviewing lengthy documents"],"limitations":["64K token limit is hard ceiling; documents longer than ~48,000 words must be chunked or summarized externally","Attention computation scales quadratically with context length, causing inference latency to increase significantly for full 64K windows (unquantified)","No documented 'lost in the middle' analysis — model may struggle to retrieve information from middle of very long contexts","8x7b variant limited to 32K tokens; long-context capability only available on 8x22b (80GB VRAM requirement)"],"requires":["Mixtral 8x22b variant (8x7b limited to 32K)","80GB GPU VRAM","Document or context smaller than 64K tokens (~48,000 words)"],"input_types":["text (documents, code, conversation history, markdown, JSON)"],"output_types":["text (analysis, summary, answers, code review comments)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_5","uri":"capability://tool.use.integration.local.inference.via.ollama.runtime.with.rest.api","name":"local inference via ollama runtime with rest api","description":"Mixtral is distributed exclusively through Ollama, a runtime that packages the model weights and inference engine, exposing a REST API on localhost:11434 for chat completions, embeddings, and model management. The Ollama runtime handles model loading, quantization selection, GPU memory management, and request batching, abstracting away low-level inference details while providing CLI and SDK interfaces.","intents":["Run Mixtral locally without writing CUDA/inference code","Integrate Mixtral into applications via standard HTTP REST API","Switch between different models (Llama, Mistral, etc.) without code changes","Deploy Mixtral in Docker containers for reproducible environments"],"best_for":["Developers unfamiliar with CUDA or inference optimization","Teams building applications that need model flexibility","Organizations deploying via Docker/Kubernetes","Researchers prototyping with multiple models"],"limitations":["Ollama is proprietary and closed-source; no visibility into quantization, optimization, or inference implementation","Model weights are packaged in Ollama's format; direct access to raw weights is undocumented","REST API adds ~50-100ms latency per request compared to direct library calls","No built-in request queuing or load balancing for concurrent requests beyond Ollama Cloud tiers","Ollama Cloud free tier limited to 1 concurrent model; Pro tier ($20/mo) limited to 3 concurrent models"],"requires":["Ollama runtime (macOS, Windows, Linux, or Docker)","26GB GPU VRAM for 8x7b or 80GB for 8x22b","HTTP client library (curl, Python requests, Node.js fetch, etc.) for REST API"],"input_types":["JSON (chat completion request with messages array)"],"output_types":["JSON (chat completion response with text content) or streaming newline-delimited JSON"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_6","uri":"capability://text.generation.language.streaming.text.generation.with.token.by.token.output","name":"streaming text generation with token-by-token output","description":"Mixtral supports streaming inference via Ollama's REST API, returning tokens incrementally as they are generated rather than buffering the complete response. The client receives newline-delimited JSON objects, each containing a single token or partial token, enabling real-time display of model output and early termination if needed.","intents":["Display model output in real-time as it generates (chat UI, terminal)","Reduce perceived latency by showing first token quickly","Cancel generation mid-stream if output is incorrect or unwanted","Implement token-counting or cost estimation during generation"],"best_for":["Frontend developers building chat interfaces","Teams building interactive AI applications","Developers needing to display output before full generation completes"],"limitations":["Streaming adds complexity to client code (must handle partial JSON, concatenate tokens)","No documented token-level timing data; cannot measure time-to-first-token or inter-token latency","Streaming requests cannot be easily retried mid-stream without losing partial output","No built-in support for streaming function calls (tool use) — unclear if tool calls stream or buffer"],"requires":["Ollama runtime","HTTP client supporting streaming responses (most modern libraries support this)","JSON parsing for newline-delimited format"],"input_types":["JSON (chat completion request with stream: true)"],"output_types":["newline-delimited JSON (each line contains {model, created_at, message: {role, content}, done})"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_7","uri":"capability://tool.use.integration.multi.platform.local.deployment.with.cli.and.sdk.bindings","name":"multi-platform local deployment with cli and sdk bindings","description":"Mixtral via Ollama is available as a single binary for macOS, Windows, and Linux, with native CLI commands and SDK bindings for Python and JavaScript. The deployment model eliminates dependency management by bundling the runtime and model weights, allowing one-command installation and execution across platforms.","intents":["Deploy Mixtral on developer laptops without Docker or cloud infrastructure","Integrate Mixtral into Python scripts or Node.js applications with minimal setup","Run the same model across macOS, Windows, and Linux without code changes","Distribute AI-powered applications to end users without requiring them to manage dependencies"],"best_for":["Individual developers building local AI tools","Teams distributing desktop applications with embedded AI","Organizations with strict data residency requirements (on-premises only)"],"limitations":["Ollama binary is large (~500MB+) due to bundled runtime; distribution adds significant size to applications","GPU support limited to NVIDIA (CUDA) and Apple Silicon (Metal); AMD/Intel GPU support unclear or unsupported","CLI and SDK are Ollama-specific; switching to another inference framework requires rewriting integration code","No documented performance optimization for specific hardware (e.g., RTX 4090 vs RTX 3060 Ti)"],"requires":["macOS 11+, Windows 10+, or Linux (Ubuntu 20.04+)","NVIDIA GPU with CUDA 11.8+ or Apple Silicon Mac","26GB free disk space for 8x7b model download"],"input_types":["CLI: text input via stdin or command-line arguments; SDK: Python/JavaScript objects"],"output_types":["CLI: text output to stdout; SDK: Python/JavaScript objects or streaming iterators"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_8","uri":"capability://automation.workflow.cloud.deployment.with.usage.based.pricing.and.concurrency.tiers","name":"cloud deployment with usage-based pricing and concurrency tiers","description":"Mixtral is available via Ollama Cloud, a managed service that runs the model on Ollama's infrastructure and meters usage by GPU compute time (not tokens). Users select a tier (Free, Pro, Max) that determines concurrent model capacity and usage allowance, with requests queued if concurrency limits are exceeded.","intents":["Deploy Mixtral without managing GPU infrastructure or scaling","Pay only for actual GPU compute time used, not reserved capacity","Scale from prototype to production without code changes","Access Mixtral from applications without local GPU requirements"],"best_for":["Startups and small teams without GPU infrastructure","Applications with variable load (bursty traffic)","Organizations preferring managed services over self-hosted inference"],"limitations":["Free tier limited to 1 concurrent model and 'light usage' (undefined quota); Pro tier ($20/mo) limited to 3 concurrent models; Max tier ($100/mo) limited to 10 concurrent models","Pricing is metered by GPU time, not tokens — cost per request varies based on input/output length and model size (unquantified)","Queue behavior for requests exceeding concurrency limits is undocumented (timeout, FIFO, priority?)","No documented SLA, uptime guarantee, or rate limiting policy","Cache-aware pricing promised 'soon' but not yet available"],"requires":["Ollama Cloud account (free signup)","API key for authentication","Internet connectivity (cloud-dependent, not local)"],"input_types":["JSON (chat completion request via HTTPS)"],"output_types":["JSON (chat completion response) or streaming newline-delimited JSON"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mixtral__cap_9","uri":"capability://automation.workflow.model.switching.and.version.management.via.ollama.library","name":"model switching and version management via ollama library","description":"Ollama maintains a library of pre-packaged models (Mixtral, Llama, Mistral, etc.) with versioning, allowing users to pull, run, and switch between models via CLI or API. The runtime handles model downloading, caching, and memory management, enabling seamless switching without manual weight management or version conflicts.","intents":["Experiment with multiple models (Mixtral 8x7b vs 8x22b, Llama 2, Mistral) without manual setup","Pin specific model versions for reproducible results","Automatically download and cache models on first use","Compare model outputs on the same task without rewriting code"],"best_for":["Researchers comparing model architectures or sizes","Developers building applications that support multiple model backends","Teams evaluating models before production deployment"],"limitations":["Model library is curated by Ollama; custom models or weights require manual integration (undocumented process)","No version pinning in API calls — version must be specified in model name (e.g., 'mixtral:8x7b' vs 'mixtral:8x22b')","Model switching requires unloading previous model from VRAM; no documented multi-model serving or model batching","No documented model update mechanism — unclear how new versions are released or deprecated"],"requires":["Ollama runtime","Internet connectivity for initial model download","Sufficient disk space for all models (26GB per 8x7b, 80GB per 8x22b)"],"input_types":["CLI: model name (e.g., 'ollama run mixtral:8x7b'); API: model field in request JSON"],"output_types":["Model output (text, embeddings, etc.) from selected model"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (macOS, Windows, Linux, or Docker)","26GB GPU VRAM minimum for 8x7b variant (actual requirement depends on quantization; unspecified)","Python 3.8+ or Node.js 14+ for SDK integration (optional; CLI works standalone)","Ollama runtime with 26GB VRAM","Text input containing code or mathematical problem statement","Ollama runtime with embedding support enabled","Text input (documents, queries, code snippets)","Vector database or similarity search library (e.g., Pinecone, Weaviate, FAISS)","Ollama runtime","GPU with sufficient VRAM for quantized model (exact requirements depend on quantization level, undocumented)"],"failure_modes":["Only ~12.9B parameters active per token (2 of 8 experts), reducing expressiveness vs dense models of equivalent total size","32K token context window is fixed hard limit; cannot process documents longer than ~24,000 words","Expert routing adds ~5-10% computational overhead vs dense models due to gating network evaluation","No documented performance benchmarks against GPT-3.5, Claude, or Llama 2 — claims of 'new standard' are unquantified","No explicit verification that generated code is syntactically correct or executable — requires post-generation testing","Mathematical reasoning limited to problems solvable within 32K token context; cannot handle multi-file codebases larger than ~20K lines","No documented accuracy metrics for code generation (e.g., pass@1 on HumanEval benchmark)","Trained on unknown dataset composition — code quality may reflect biases in training data","Embedding model architecture and dimensionality are undocumented (unclear if Mixtral generates embeddings natively or via adapter)","No documented embedding quality metrics or comparison to specialized embedding models (e.g., OpenAI text-embedding-3-large)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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=mixtral","compare_url":"https://unfragile.ai/compare?artifact=mixtral"}},"signature":"ZtfDHdDCGxbh3GBTTxFRquPox34rK1rblFnoHzibSUOBnqdSCCiHiSaPuNSAMk67R37z6uiiP/HvkXrLBQkNBg==","signedAt":"2026-06-20T17:25:35.295Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mixtral","artifact":"https://unfragile.ai/mixtral","verify":"https://unfragile.ai/api/v1/verify?slug=mixtral","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"}}