{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mistral-api","slug":"mistral-api","name":"Mistral API","type":"api","url":"https://console.mistral.ai","page_url":"https://unfragile.ai/mistral-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$0.10/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"mistral-api__cap_0","uri":"capability://text.generation.language.multi.model.text.generation.with.dynamic.model.selection","name":"multi-model text generation with dynamic model selection","description":"Provides access to a tiered model family (Mistral Large, Medium, Small) through a unified API endpoint, allowing developers to select models based on latency/cost/capability tradeoffs. Each model is optimized for parameter efficiency, with routing logic that maps requests to the appropriate model tier. The API handles tokenization, context windowing, and response streaming through standard HTTP/gRPC interfaces with configurable temperature, top-p, and max-tokens parameters.","intents":["Choose between fast, cheap inference (Small) vs higher quality reasoning (Large) without changing integration code","Optimize cost per inference by routing simple queries to smaller models and complex reasoning to larger ones","Build multi-tier fallback systems that degrade gracefully when larger models hit rate limits"],"best_for":["Cost-conscious teams building production LLM applications","Developers needing sub-second latency for real-time chat or autocomplete","Teams evaluating model quality vs inference cost tradeoffs"],"limitations":["Model selection is manual — no built-in adaptive routing based on query complexity","Context window varies by model (Small: 32k, Medium: 128k, Large: 128k) requiring application-level management","No local model fallback — all inference requires API connectivity"],"requires":["API key from Mistral console","HTTP client library (curl, requests, axios, etc.)","Network connectivity to api.mistral.ai"],"input_types":["text (prompts, chat messages)","structured messages with role/content fields"],"output_types":["text (streamed or buffered)","token usage metadata"],"categories":["text-generation-language","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_1","uri":"capability://data.processing.analysis.structured.output.generation.with.json.mode","name":"structured output generation with json mode","description":"Enforces JSON schema compliance in model outputs by constraining the token generation process to only produce valid JSON matching a developer-provided schema. The implementation uses grammar-based token masking during decoding — at each generation step, only tokens that maintain JSON validity are allowed, preventing malformed output. Schemas are specified as JSON Schema Draft 7 objects passed in the API request, and the model guarantees output will parse without errors.","intents":["Extract structured data (entities, relationships, classifications) from unstructured text without post-processing or validation","Generate API responses, database records, or configuration files that are guaranteed to be valid JSON","Build reliable data pipelines where LLM output directly feeds into downstream systems without error handling"],"best_for":["Data extraction and ETL pipelines requiring guaranteed valid output","API backends that need LLM-generated structured responses without validation overhead","Teams building form-filling or structured data collection systems"],"limitations":["Schema complexity impacts latency — deeply nested or highly constrained schemas add 50-200ms per request","No support for recursive or self-referential schemas","JSON mode may reduce output quality for tasks where natural language flexibility is beneficial"],"requires":["API key for Mistral","JSON Schema Draft 7 definition of desired output structure","Understanding of schema constraints and their impact on model behavior"],"input_types":["text prompts","JSON Schema definitions"],"output_types":["valid JSON matching provided schema","guaranteed parseable without try/catch"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_10","uri":"capability://memory.knowledge.embeddings.generation.for.semantic.search","name":"embeddings generation for semantic search","description":"Generates dense vector embeddings from text that capture semantic meaning, enabling similarity search, clustering, and retrieval-augmented generation (RAG). The API accepts text inputs and returns fixed-dimensional vectors (typically 1024 or 4096 dimensions depending on model) that can be stored in vector databases. Supports batch embedding generation for efficiency and includes normalization options for different similarity metrics.","intents":["Build semantic search systems that find relevant documents based on meaning rather than keyword matching","Implement retrieval-augmented generation (RAG) by embedding documents and queries for similarity matching","Cluster or classify text by computing embeddings and applying distance metrics"],"best_for":["Teams building RAG systems or semantic search applications","Developers implementing document retrieval or recommendation systems","Organizations needing to find semantically similar content at scale"],"limitations":["Embeddings are model-specific — cannot mix embeddings from different models in the same index","Embedding quality depends on domain relevance — general-purpose embeddings may underperform on specialized domains","Vector database integration required for practical use — embeddings alone are not searchable"],"requires":["API key for Mistral","Vector database (Pinecone, Weaviate, Milvus, etc.) for storing and searching embeddings","Text to embed (documents, queries, etc.)"],"input_types":["text strings","batch of texts (JSONL or array format)"],"output_types":["dense vectors (1024 or 4096 dimensions)","embedding metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_11","uri":"capability://tool.use.integration.api.key.management.and.rate.limiting","name":"api key management and rate limiting","description":"Provides API key management through the console with granular rate limiting controls, allowing developers to create multiple keys with different rate limits, monitor usage, and implement quota-based access control. Rate limits are enforced per-key and per-model, enabling multi-tenant applications to allocate quotas to different users or services.","intents":["Create separate API keys for different applications or teams with independent rate limits","Implement quota-based access control for multi-tenant systems","Monitor API usage and enforce spending limits"],"best_for":["Multi-tenant SaaS platforms allocating quotas to different customers","Teams managing multiple applications with separate rate limit requirements","Organizations implementing cost controls and usage monitoring"],"limitations":["Rate limits are enforced per-key, not per-user — applications must implement user-level rate limiting separately","No automatic quota enforcement — applications must implement their own quota checks and rejections","Rate limit changes may take time to propagate — not suitable for real-time quota adjustments"],"requires":["Mistral console account","Access to API key management dashboard","Application-level quota tracking and enforcement"],"input_types":["API key configuration (rate limits, model access)"],"output_types":["API keys with associated rate limits","usage metrics and monitoring data"],"categories":["tool-use-integration","access-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_2","uri":"capability://tool.use.integration.function.calling.with.schema.based.dispatch","name":"function calling with schema-based dispatch","description":"Enables models to request execution of external functions by generating structured function calls that map to a developer-provided tool registry. The implementation works by including function schemas in the system prompt, training the model to output function calls in a standardized format (name + arguments), and the API client automatically routes these calls to registered handlers. Supports parallel function execution, nested calls, and automatic result injection back into the conversation context for multi-turn reasoning.","intents":["Build agentic systems where the model decides which tools to use and in what order to solve problems","Connect LLMs to external APIs, databases, or services without manual prompt engineering for each integration","Create multi-step workflows where the model reasons about intermediate results and adapts next steps accordingly"],"best_for":["Teams building AI agents that need to interact with external systems","Developers creating chatbots with access to real-time data or APIs","Builders of autonomous workflows that require tool composition and error recovery"],"limitations":["Model may hallucinate function calls that don't exist in the registry — requires validation before execution","No built-in error recovery — failed function calls require explicit handling and re-prompting","Parallel function execution requires careful context management to avoid race conditions or inconsistent state"],"requires":["API key for Mistral","Function schemas defined as JSON Schema objects","Handler implementations for each registered function","Client library that supports tool_choice and function result injection"],"input_types":["text prompts","function schemas (JSON Schema)","function execution results (JSON)"],"output_types":["function call requests (name + arguments)","text responses after tool execution"],"categories":["tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_3","uri":"capability://code.generation.editing.code.generation.and.completion.with.codestral","name":"code generation and completion with codestral","description":"Specialized code generation model (Codestral) fine-tuned on large code corpora to generate, complete, and explain code across 80+ programming languages. The model understands syntax, semantics, and common patterns, enabling context-aware completions that respect existing code style and architecture. Supports both fill-in-the-middle (FIM) mode for inline completions and standard left-to-right generation for new code. Integrates with IDE plugins and can be used for code review, refactoring suggestions, and test generation.","intents":["Generate boilerplate code, utility functions, or API client code from natural language descriptions","Complete partial code snippets with context-aware suggestions that match existing patterns","Explain code functionality or generate documentation from source code"],"best_for":["Individual developers using IDE plugins for code completion","Teams building internal code generation tools or scaffolding systems","Organizations needing code generation without sending code to US-based servers (EU data residency)"],"limitations":["FIM mode requires specific prompt formatting — standard prompts may not trigger fill-in-the-middle behavior","No built-in linting or syntax validation — generated code may have errors requiring manual review","Context window (32k tokens) limits ability to work with very large codebases or multiple files"],"requires":["API key for Mistral","Understanding of FIM prompt format for inline completions","Language-specific syntax knowledge for validation"],"input_types":["code snippets (partial or complete)","natural language descriptions of desired functionality","code context (surrounding functions, imports, etc.)"],"output_types":["generated code","code completions","code explanations"],"categories":["code-generation-editing","specialized-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_4","uri":"capability://image.visual.multimodal.vision.understanding.with.pixtral","name":"multimodal vision understanding with pixtral","description":"Vision-capable model (Pixtral) that processes images alongside text to answer questions, describe content, perform OCR, and analyze visual data. The implementation accepts images as base64-encoded data or URLs, processes them through a vision encoder that extracts spatial and semantic features, and fuses these representations with text embeddings for joint reasoning. Supports multiple images per request and can handle documents, screenshots, diagrams, and photographs with high accuracy.","intents":["Extract text from images, screenshots, or documents (OCR) without external vision APIs","Answer questions about image content or analyze visual data in context of text queries","Build document processing pipelines that understand both text and layout"],"best_for":["Teams building document processing or data extraction systems","Developers creating chatbots that need to understand user-uploaded images","Organizations processing visual content without sending data to US-based providers"],"limitations":["Image resolution is limited to prevent token explosion — very high-resolution images are downsampled","OCR quality varies by image quality and text size — small or low-contrast text may be missed","No image generation capability — vision is read-only, cannot create or edit images"],"requires":["API key for Mistral","Images in JPEG, PNG, GIF, or WebP format","Image data as base64 or publicly accessible URL"],"input_types":["text prompts","images (base64 or URL)","multiple images per request"],"output_types":["text descriptions","extracted text (OCR)","structured analysis results"],"categories":["image-visual","multimodal"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_5","uri":"capability://text.generation.language.fine.tuning.with.custom.datasets","name":"fine-tuning with custom datasets","description":"Enables training Mistral models on custom datasets to adapt them for specific domains, writing styles, or task-specific behaviors. The fine-tuning process uses supervised learning on labeled examples (prompt-response pairs), with the API handling data validation, training orchestration, and model checkpointing. Supports both full fine-tuning and parameter-efficient methods (LoRA), with training jobs running asynchronously and results available as new model endpoints. Includes automatic data quality checks and training metrics.","intents":["Adapt Mistral models to domain-specific language or terminology without retraining from scratch","Improve model performance on specialized tasks by training on curated examples","Create custom model variants for different use cases or customer segments"],"best_for":["Teams with domain-specific datasets wanting to improve model accuracy","Organizations needing custom model behavior for compliance or brand voice","Developers building multi-tenant systems with customer-specific model variants"],"limitations":["Requires minimum dataset size (typically 100+ examples) for meaningful improvement","Fine-tuning adds latency to deployment — new models must be trained and validated before use","No guarantee of improvement — poor-quality or biased training data can degrade performance","Fine-tuned models incur separate inference costs and require dedicated endpoints"],"requires":["API key for Mistral","Training dataset in JSONL format (prompt-response pairs)","Minimum 100 examples for meaningful fine-tuning","Validation dataset for evaluating model quality"],"input_types":["JSONL files with prompt-response pairs","training parameters (learning rate, epochs, batch size)"],"output_types":["fine-tuned model endpoint","training metrics and loss curves","model checkpoints"],"categories":["text-generation-language","model-customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_6","uri":"capability://automation.workflow.batch.processing.for.cost.optimization","name":"batch processing for cost optimization","description":"Asynchronous batch API that processes multiple requests in a single job, optimizing throughput and reducing per-token costs by 50% compared to real-time API calls. Requests are queued, processed in batches during off-peak hours, and results are returned via webhook or polling. The implementation groups requests into efficient batches, reuses computational resources across similar queries, and provides detailed job status tracking and result retrieval.","intents":["Process large volumes of text (thousands of documents) at lower cost for non-time-sensitive tasks","Run periodic batch jobs for content generation, summarization, or classification","Optimize costs for applications that can tolerate latency (minutes to hours) in exchange for 50% savings"],"best_for":["Teams processing large datasets with flexible latency requirements","Organizations running periodic batch jobs (daily/weekly reports, content generation)","Cost-sensitive applications where 50% savings justify minutes to hours of latency"],"limitations":["Latency is unpredictable — batch jobs may take minutes to hours depending on queue depth","No real-time feedback — results are only available after entire batch completes","Minimum batch size requirements may apply — very small batches may not qualify for discount","Failed requests in a batch require resubmission of entire batch"],"requires":["API key for Mistral","Requests formatted as JSONL (one request per line)","Webhook endpoint or polling mechanism for result retrieval","Tolerance for variable latency (minutes to hours)"],"input_types":["JSONL file with multiple API requests","batch configuration (model, parameters)"],"output_types":["JSONL file with results","job status and metadata","webhook notifications"],"categories":["automation-workflow","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_7","uri":"capability://safety.moderation.eu.data.residency.and.compliance","name":"eu data residency and compliance","description":"Mistral infrastructure is hosted in the European Union with data residency guarantees, ensuring that all API requests, model weights, and outputs remain within EU borders. This is implemented through dedicated EU data centers, contractual commitments, and compliance with GDPR, ensuring that sensitive data never transits through or is stored in non-EU jurisdictions. Particularly valuable for regulated industries and organizations with strict data localization requirements.","intents":["Build AI applications for regulated industries (healthcare, finance, government) with strict data residency requirements","Ensure compliance with GDPR and other EU data protection regulations without complex data handling","Avoid data sovereignty concerns when processing sensitive customer or citizen data"],"best_for":["European organizations subject to GDPR or other data residency regulations","Healthcare, financial, and government institutions with strict data localization requirements","Teams processing sensitive personal data that cannot leave the EU"],"limitations":["EU data residency may add slight latency for non-EU users","Limited to Mistral's EU infrastructure — cannot use other regions for failover","Compliance guarantees are contractual — require explicit data processing agreements"],"requires":["API key for Mistral","Data Processing Agreement (DPA) with Mistral for GDPR compliance","Understanding of data residency requirements in your jurisdiction"],"input_types":["any data type (text, images, code)","sensitive personal or regulated data"],"output_types":["model outputs (guaranteed EU-resident)"],"categories":["safety-moderation","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_8","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"API endpoint that counts tokens in text without executing inference, enabling accurate cost estimation before making API calls. The implementation uses the same tokenizer as the inference models, ensuring consistency between estimated and actual token usage. Supports batch token counting for multiple texts and provides breakdowns by message role (system, user, assistant) for multi-turn conversations.","intents":["Estimate API costs before making requests to avoid unexpected bills","Optimize prompts by measuring token usage and identifying verbose sections","Implement token-aware request batching or splitting for large inputs"],"best_for":["Teams managing API costs and needing accurate budget forecasting","Developers optimizing prompts for token efficiency","Applications that need to validate input size before sending to inference API"],"limitations":["Token counting is model-specific — different models may tokenize identically but have different pricing","Does not account for system overhead or other hidden costs","Batch token counting has rate limits — very large batches may require pagination"],"requires":["API key for Mistral","Text to tokenize"],"input_types":["text strings","chat message arrays"],"output_types":["token count","per-message token breakdown"],"categories":["data-processing-analysis","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__cap_9","uri":"capability://text.generation.language.streaming.responses.with.server.sent.events","name":"streaming responses with server-sent events","description":"Real-time response streaming using Server-Sent Events (SSE) protocol, allowing clients to receive model output token-by-token as it's generated rather than waiting for the complete response. The implementation maintains an open HTTP connection, sends tokens as they're generated, and includes metadata (token probabilities, finish reasons) in each event. Enables responsive UX for chat applications and allows early termination if desired output is reached before completion.","intents":["Build chat interfaces with real-time token streaming for responsive user experience","Implement early stopping when desired output is detected mid-generation","Reduce perceived latency by showing partial results while generation continues"],"best_for":["Chat applications and conversational interfaces","Web applications requiring responsive real-time feedback","Developers building interactive AI experiences"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial results","Connection management required — dropped connections may leave incomplete responses","Token probabilities in streaming may differ slightly from batch processing due to different decoding strategies"],"requires":["API key for Mistral","HTTP client with SSE support (most modern libraries)","Handling for stream termination and error cases"],"input_types":["text prompts","chat messages"],"output_types":["streamed tokens (SSE format)","metadata per token"],"categories":["text-generation-language","real-time-interaction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-api__headline","uri":"capability://llm.apis.mistral.api.for.llms.and.vision.models","name":"mistral api for llms and vision models","description":"The Mistral API provides access to high-performance language and vision models, including specialized options for code generation and fine-tuning, making it ideal for developers seeking robust AI solutions with European data residency.","intents":["best LLM API","LLM API for code generation","Mistral API for vision tasks","high-performance AI models","API for fine-tuning language models"],"best_for":["developers needing EU data residency"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from Mistral console","HTTP client library (curl, requests, axios, etc.)","Network connectivity to api.mistral.ai","API key for Mistral","JSON Schema Draft 7 definition of desired output structure","Understanding of schema constraints and their impact on model behavior","Vector database (Pinecone, Weaviate, Milvus, etc.) for storing and searching embeddings","Text to embed (documents, queries, etc.)","Mistral console account","Access to API key management dashboard"],"failure_modes":["Model selection is manual — no built-in adaptive routing based on query complexity","Context window varies by model (Small: 32k, Medium: 128k, Large: 128k) requiring application-level management","No local model fallback — all inference requires API connectivity","Schema complexity impacts latency — deeply nested or highly constrained schemas add 50-200ms per request","No support for recursive or self-referential schemas","JSON mode may reduce output quality for tasks where natural language flexibility is beneficial","Embeddings are model-specific — cannot mix embeddings from different models in the same index","Embedding quality depends on domain relevance — general-purpose embeddings may underperform on specialized domains","Vector database integration required for practical use — embeddings alone are not searchable","Rate limits are enforced per-key, not per-user — applications must implement user-level rate limiting separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"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=mistral-api","compare_url":"https://unfragile.ai/compare?artifact=mistral-api"}},"signature":"V5Zpc/lRdgZk2i2pZarMDY6whbwAQBSCiMxDZaDDEykQOfM9dOI9nx7pndCQaGqy6Q80rZKiG8sTS/u9OgX8BQ==","signedAt":"2026-06-21T22:43:43.720Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistral-api","artifact":"https://unfragile.ai/mistral-api","verify":"https://unfragile.ai/api/v1/verify?slug=mistral-api","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"}}