Mistral
Mistral AI is a family of AI models developed by... Mistral AI, a French AI company founded in 2023 by former DeepMind and Meta researchers. The family's core philosophy centers on efficiency, accessibility, and a hybrid open/commercial approach - releasing select models under Apache 2.0 while offering premium commercial models via API. Mistral models are engineered for maximum compute efficiency, delivering performance comparable to much larger models at significantly lower cost and size.
The family currently spans foundation models, reasoning models, code generation, vision, audio, edge deployment, and specialized utility models. Key strengths include exceptional multilingual support (dozens of languages natively), cost leadership (4-8x cheaper than comparable alternatives), and European data sovereignty with GDPR-compliant infrastructure. Mistral excels in agentic workflows with strong function calling, structured output support, and models specifically designed for autonomous software engineering.
Mistral is the right choice when you need cost-efficient performance, require European data sovereignty, want open-weight models with commercial licenses, need native multilingual capabilities, or are building agentic systems requiring sophisticated tool use. The family's efficiency makes it particularly suitable for production deployments where cost and latency matter.
Platform & Access
Platform Name: AI Studio (console.mistral.ai)
Platform URL: https://console.mistral.ai/
What It Offers:
- API key management and authentication
- Interactive playground for model testing
- Usage monitoring and billing dashboard
- Fine-tuning and custom model deployment
- Document processing and RAG tooling
- Agent orchestration capabilities
- Zero data retention options for sensitive workloads
Access Models:
- API via AI Studio: Primary access method with EU-hosted infrastructure and optional data isolation
- Cloud Partnerships: Available on Azure AI Studio, AWS Bedrock, Google Vertex AI, IBM Watsonx, Snowflake, NVIDIA NIM
- Self-Deployment: Open-weight models available on Hugging Face for on-premises, VPC, or edge deployment
Pricing Model: Token-based, pay-as-you-go (per million tokens). Enterprise custom pricing available.
Free Tier: 1 request/second, 500K tokens/minute, up to 1B tokens/month. No credit card required for initial access.
Foundation Models
Mistral Large 2.1
- Parameters: 123B
- Context Window: 128K tokens
- Multimodal: Text + Vision (handles 30+ high-resolution images)
- License: Mistral Research License (research/non-commercial) + Mistral Commercial License required for commercial use
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- Enterprise RAG systems requiring precise retrieval across massive document sets
- Mathematical and scientific problem-solving with visual data interpretation (69.4% MathVista)
- Multilingual customer support handling dozens of languages with cultural context
- Complex multi-step reasoning tasks requiring extended context retention
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - enhanced parallel and sequential function calling, significant improvements in 2411 version
- Structured Output: Native JSON mode with guaranteed valid JSON outputs
- Notable Features: State-of-the-art vision capabilities (93.3% DocVQA, 88.1% ChartQA), designed for single-node inference, 92.0% HumanEval coding benchmark
Mistral Medium 3.1
- Parameters: Undisclosed (medium-sized, deployable on 4+ GPUs)
- Context Window: 128K tokens
- Multimodal: Text + Vision
- License: Proprietary (API access and custom deployment require licensing)
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- Financial services analytics requiring regulatory compliance and audit trails
- Healthcare diagnostics support with multimodal medical imaging interpretation
- Legal document analysis with domain-specific fine-tuning on proprietary case law
- Enterprise workflows requiring custom post-training on sensitive proprietary data
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - high-precision function calling for enterprise workflows
- Structured Output: Native JSON mode and advanced structured generation
- Notable Features: 8x lower cost than competitors ($0.40 input/$2 output per M tokens), performs at/above 90% of Claude Sonnet 3.7, supports hybrid/on-premises/VPC deployment, custom post-training available
Mistral Small 3.2
- Parameters: 24B
- Context Window: 128K tokens
- Multimodal: Text + Vision
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link:
Hugging Face
Primary Use Cases:
- On-device virtual assistants requiring privacy-first local processing
- Domain-specific expert systems (legal, medical, technical) with fine-tuning
- Visual inspection systems for quality control in manufacturing
- Local analytics on sensitive data without cloud transmission
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - low-latency function calling optimized for automated workflows
- Structured Output: Native JSON mode with schema validation
- Notable Features: 150 tokens/sec inference, runs on single RTX 4090 or Mac with 32GB RAM when quantized, outperforms GPT-4o Mini and Gemma 3, state-of-the-art vision in 24B class
Reasoning Models
Magistral Medium
- Parameters: Undisclosed (larger than 24B)
- Context Window: 128K tokens
- Multimodal: Text + Vision (v1.2)
- License: Proprietary (API and enterprise only)
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- Multi-step mathematical proofs and formal verification (73.6% AIME-24 pass@1, 90% with majority voting)
- Legal research requiring traceable chains of reasoning with source attribution
- Financial risk modeling where decision transparency is regulatory requirement
- Scientific hypothesis generation with explicit reasoning paths for peer review
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - supports complex function calling with reasoning
- Structured Output: Generates structured JSON with explicit reasoning traces in
<think>tags - Notable Features: Extended chain-of-thought reasoning with transparent thinking process, multilingual reasoning in 8+ languages, "Think mode" and "Flash Answers" modes, vision support for visual reasoning
Magistral Small
- Parameters: 24B
- Context Window: 128K tokens
- Multimodal: Text + Vision (v1.2)
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Educational applications requiring step-by-step problem-solving demonstrations
- Privacy-sensitive reasoning tasks requiring local deployment without cloud access
- Research requiring transparent reasoning processes with reproducible outputs
- Edge deployment of reasoning capabilities on consumer hardware
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - supports function calling with reasoning
- Structured Output: Generates structured outputs with reasoning traces
- Notable Features: Runs locally on single RTX 4090 or Mac with 32GB RAM, transparent step-by-step reasoning showing inner monologue, 70.7% AIME-24 pass@1 (83.3% with majority voting), multilingual reasoning
Code Generation Models
Codestral
- Parameters: Sub-100B (undisclosed)
- Context Window: 256K tokens (largest in Mistral family)
- Multimodal: Text-only
- License: Proprietary (API and on-premise)
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- Production-grade code completion in enterprise IDEs with repository-wide context
- Automated test generation across large codebases with complex dependencies
- SQL query generation from natural language for business intelligence tools
- Real-time fill-in-middle (FIM) tasks requiring sub-second latency (85.9% accuracy)
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - native function calling for development workflows
- Structured Output: Full support for structured code generation
- Notable Features: #1 on LMsys Copilot Arena leaderboard (January 2025), 86.6% HumanEval Python, +30% increase in accepted completions vs prior version, 2x faster generation, works with Devstral for agentic workflows
Codestral Embed
- Parameters: Undisclosed
- Context Window: 8,192 tokens (recommended chunking: 3000 chars with 1000 overlap)
- Multimodal: Text-only (code-specialized)
- License: Proprietary (API only)
- Knowledge Cutoff: N/A (embedding model)
- Link: Documentation
Primary Use Cases:
- RAG systems for code completion and intelligent editing
- Semantic code search across repositories with natural language queries
- Duplicate code detection and architectural pattern analysis
- Code categorization by functionality for automated documentation
Agentic Capabilities:
- Tool Use / Function Calling: N/A (embedding model)
- Structured Output: Matryoshka embeddings (256-3,072 dimensions; default 1,536)
- Notable Features: Outperforms Voyage Code 3, Cohere Embed v4.0, OpenAI Text Embedding 3 Large on SWE-Bench Lite and CodeSearchNet, multiple precision options (binary, int8, float32)
Developer Agent Models
Devstral Small 1.1
- Parameters: 24B
- Context Window: 128K tokens
- Multimodal: Text-only
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Autonomous GitHub issue resolution in open-source projects
- Multi-file refactoring across large codebases with dependency tracking
- Codebase exploration and architectural analysis for onboarding
- Automated bug fixing with repository-level context awareness
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - supports both Mistral function calling and XML formats
- Structured Output: Full support for structured outputs
- Notable Features: 53.6% SWE-Bench Verified (state-of-the-art for open models without test-time scaling, +11.4% better than second-best open model), works with OpenHands and SWE-Agent, runs on single RTX 4090 or Mac with 32GB RAM
Devstral Medium
- Parameters: Undisclosed (larger than 24B)
- Context Window: 128K tokens
- Multimodal: Text-only
- License: Proprietary (API access; custom licensing for on-premise)
- Knowledge Cutoff: Not publicly disclosed
- Link: Announcement
Primary Use Cases:
- Enterprise software engineering with complex multi-repository modifications
- Custom codebase-specific workflows requiring proprietary knowledge
- Automated refactoring of legacy systems with business logic preservation
- Advanced architectural planning with cross-system dependency analysis
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - advanced tool use for software engineering workflows
- Structured Output: Full support for structured outputs
- Notable Features: 61.6% SWE-Bench Verified (surpasses Gemini 2.5 Pro and GPT-4.1 at 1/4 the price), custom fine-tuning available, on-premise deployment option, can be post-trained on proprietary codebases
Vision Models
Pixtral Large
- Parameters: 124B total (123B decoder + 1B vision encoder)
- Context Window: 128K tokens (fits 30+ high-resolution images)
- Multimodal: Text + Vision
- License: Mistral Research License + Mistral Commercial License
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- Scientific paper analysis with chart, equation, and figure interpretation (69.4% MathVista)
- Enterprise document processing with complex layouts and embedded visuals (93.3% DocVQA)
- Multilingual OCR with contextual reasoning beyond pure text extraction
- Visual knowledge exploration requiring high-resolution image understanding
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - enhanced function calling with parallel execution support
- Structured Output: Native JSON mode with custom structured outputs
- Notable Features: Best open-weights model on LMSys Vision Leaderboard by ~50 ELO, processes images at natural aspect ratio and high resolution, outperforms Claude 3.5 Sonnet, Gemini 1.5 Pro, GPT-4o on MM-MT-Bench
Pixtral 12B
- Parameters: 12.4B total (12B decoder + 400M vision encoder)
- Context Window: 128K tokens (arbitrary number of images)
- Multimodal: Text + Vision
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Automated visual inspection systems requiring real-time processing
- Document QA where speed and cost matter more than absolute accuracy
- Multimodal applications requiring on-device inference without cloud access
- Visual instruction following in resource-constrained environments
Agentic Capabilities:
- Tool Use / Function Calling: Good - supports function calling
- Structured Output: JSON output supported
- Notable Features: Native variable image size support (processes at natural resolution), handles high-resolution diagrams/charts, fast inference on small images, 52.5% MMMU reasoning, leading performance in 12B class
Audio Models
Voxtral Small
- Parameters: 24.3B (24B multimodal decoder based on Mistral Small 3.1)
- Context Window: 32K tokens (up to 30 min transcription, 40 min understanding)
- Multimodal: Audio input + Text input/output
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Voice-based agent workflows requiring function calling directly from audio input
- Multilingual meeting transcription and summarization (8+ languages)
- Voice-powered technical support systems with multi-turn conversations
- Audio-based data extraction with structured output generation
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - native function calling directly from voice without intermediate transcription
- Structured Output: Native JSON output with structured extraction
- Notable Features: Function calling straight from voice, automatic language detection across English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian+, drop-in replacement for Mistral Small 3.1 for text tasks
Voxtral Mini
- Parameters: 4.7B (3B multimodal decoder based on Ministral-3B)
- Context Window: 32K tokens (up to 30 min transcription, 40 min understanding)
- Multimodal: Audio input + Text input/output
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Edge device audio processing requiring low power consumption
- Real-time voice interfaces on mobile devices without network connectivity
- Cost-sensitive transcription at scale for high-volume applications
- Local voice assistants with privacy-first architecture
Agentic Capabilities:
- Tool Use / Function Calling: Good - native function calling from voice
- Structured Output: JSON and document-as-prompt with structured extraction
- Notable Features: Optimized for local and edge deployments, ~9.5 GB GPU RAM in bf16/fp16, multiple audio inputs per message, automatic language detection across 8+ languages
Voxtral Mini Transcribe
- Parameters: Based on 3B Voxtral Mini (transcription-optimized)
- Context Window: 32K tokens (up to 30 min audio)
- Multimodal: Audio input only (transcription service)
- License: Apache 2.0 (through base model)
- Knowledge Cutoff: N/A (transcription service)
- Link: Documentation
Primary Use Cases:
- High-volume transcription pipelines requiring cost efficiency at scale
- Automated captioning systems with segment-level timestamps
- Multilingual transcription requiring language detection (25+ languages)
- Batch processing of audio archives for searchable text generation
Agentic Capabilities:
- Tool Use / Function Calling: No (transcription-only endpoint)
- Structured Output: Text transcription with segment-level timestamps, language detection, JSON response format
- Notable Features: Outperforms GPT-4o mini Transcribe and Gemini 2.5 Flash, matches/exceeds Whisper large-v3, ~1,000+ pages per dollar (~2x with batch), approximately half the cost of OpenAI Whisper
Document Processing Models
Mistral OCR
- Parameters: Undisclosed (lightweight)
- Context Window: N/A (processing model)
- Multimodal: OCR/Document understanding (PDFs, images)
- License: Proprietary (API only)
- Knowledge Cutoff: N/A (OCR service)
- Link: Documentation
Primary Use Cases:
- Scientific paper digitization with chart, graph, and equation extraction
- Historical document preservation requiring high-accuracy multilingual OCR
- Enterprise document processing at scale (2,000 pages/min on single node)
- Complex layout understanding for forms, invoices, and structured documents
Agentic Capabilities:
- Tool Use / Function Calling: No (specialized OCR service)
- Structured Output: Markdown format, JSON, extracts images as base64/URLs, bounding boxes
- Notable Features: 99%+ text accuracy multilingual, 94.89% overall (vs Azure 89.52%, Google 83.42%, GPT-4o 89.77%), supports 25+ languages and thousands of scripts, extracts embedded images (unique capability), processes up to 2,000 pages/min
Edge/Mobile-Optimized Models
Ministral 3B
- Parameters: 3B
- Context Window: 128K tokens (32K on vLLM currently)
- Multimodal: Text-only
- License: Mistral Commercial License (proprietary, weights not public)
- Knowledge Cutoff: Not publicly disclosed
- Link: Documentation
Primary Use Cases:
- On-device translation without internet connectivity for mobile apps
- Battery-powered IoT devices requiring local NLP capabilities
- Privacy-first personal assistants running entirely on smartphone
- Autonomous robotics requiring real-time language understanding with low power draw
Agentic Capabilities:
- Tool Use / Function Calling: Good - native function calling for external APIs and multi-step workflows
- Structured Output: Custom structured outputs via API
- Notable Features: Outperforms Mistral 7B despite being <50% the size (60.9 MMLU), lowest power consumption in family, API pricing $0.04/M tokens (most affordable), GGUF format available
Ministral 8B
- Parameters: 8.02B
- Context Window: 128K tokens (32K on vLLM currently)
- Multimodal: Text-only
- License: Mistral Research License + Mistral Commercial License (weights available)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Edge computing on laptops/workstations without GPU acceleration
- Developer/hobbyist projects requiring high performance on consumer hardware
- Multi-step agentic workflows on resource-constrained edge devices
- Local smart assistants with function calling requiring single-GPU deployment
Agentic Capabilities:
- Tool Use / Function Calling: Excellent - superior function calling (31.6 internal benchmark vs Mistral 7B 6.9)
- Structured Output: Full support with JSON mode and prefix generation
- Notable Features: Interleaved sliding-window attention for memory-efficient inference, outperforms Mistral 7B and competes with Llama 3.1 8B (65.0 MMLU), 76.8% HumanEval, 24GB GPU RAM for single-GPU deployment
Specialized Utility Models
Mistral Embed
- Parameters: Undisclosed
- Context Window: 8,000 tokens
- Multimodal: Text-only
- License: Proprietary (API only)
- Knowledge Cutoff: N/A (embedding model)
- Link: Documentation
Primary Use Cases:
- RAG systems requiring semantic document retrieval
- Text classification pipelines for content categorization
- Duplicate detection in large document corpora
- Clustering unorganized documents by semantic similarity
Agentic Capabilities:
- Tool Use / Function Calling: N/A (embedding model)
- Structured Output: Fixed 1,024-dimensional vectors
- Notable Features: MTEB retrieval score 55.26, optimized for semantic similarity via Euclidean distance/cosine similarity/dot product
Mistral Moderation
- Parameters: Based on Ministral 8B (8B)
- Context Window: N/A (classification model)
- Multimodal: Text-only
- License: Proprietary (API only)
- Knowledge Cutoff: N/A (moderation model)
- Link: Documentation
Primary Use Cases:
- System-level guardrails for production LLM deployments
- Content moderation in user-generated content platforms
- Compliance monitoring for regulatory requirements
- PII detection in sensitive document processing pipelines
Agentic Capabilities:
- Tool Use / Function Calling: N/A (classification model)
- Structured Output: Returns structured classification with category scores and binary flags
- Notable Features: 9 detection categories (Sexual, Hate/Discrimination, Violence/Threats, Dangerous/Criminal, Self-Harm, Health, Financial, Law, PII), supports 11 languages natively, customizable thresholds
Mistral Nemo
- Parameters: 12B
- Context Window: 128K tokens
- Multimodal: Text-only
- License: Apache 2.0 (open source)
- Knowledge Cutoff: Not publicly disclosed
- Link: Hugging Face
Primary Use Cases:
- Multi-turn conversational AI requiring extended context retention
- RAG applications with long-context document retrieval
- Multilingual applications requiring 11+ language support
- Edge deployment requiring balance of performance and resource efficiency
Agentic Capabilities:
- Tool Use / Function Calling: Good - trained on function calling
- Structured Output: JSON mode and structured outputs
- Notable Features: Built with NVIDIA, Tekken tokenizer (100+ languages, 30% more efficient on source code), trained with quantization awareness for FP8 inference without loss, drop-in replacement for Mistral 7B
Model Comparison Table
| Model | Context | Parameters | License | Agentic | Best For |
|---|---|---|---|---|---|
| Large 2.1 | 128K | 123B | Research/Commercial | ⭐⭐⭐⭐⭐ | Flagship multimodal reasoning, vision |
| Medium 3.1 | 128K | Undisclosed | Proprietary | ⭐⭐⭐⭐⭐ | Enterprise, custom deployment |
| Small 3.2 | 128K | 24B | Apache 2.0 | ⭐⭐⭐⭐ | Open-weight multimodal, cost-efficient |
| Magistral Medium | 128K | Undisclosed | Proprietary | ⭐⭐⭐⭐⭐ | Complex reasoning with transparency |
| Magistral Small | 128K | 24B | Apache 2.0 | ⭐⭐⭐⭐ | Open reasoning, educational |
| Codestral 25.08 | 256K | Sub-100B | Proprietary | ⭐⭐⭐⭐⭐ | Production code generation |
| Devstral Small | 128K | 24B | Apache 2.0 | ⭐⭐⭐⭐⭐ | Open agentic software engineering |
| Devstral Medium | 128K | Undisclosed | Proprietary | ⭐⭐⭐⭐⭐ | Enterprise agentic coding |
| Pixtral Large | 128K | 124B | Research/Commercial | ⭐⭐⭐⭐⭐ | State-of-the-art vision understanding |
| Pixtral 12B | 128K | 12.4B | Apache 2.0 | ⭐⭐⭐ | Open vision, cost-efficient |
| Voxtral Small | 32K | 24.3B | Apache 2.0 | ⭐⭐⭐⭐ | Production audio understanding |
| Voxtral Mini | 32K | 4.7B | Apache 2.0 | ⭐⭐⭐ | Edge audio processing |
| Ministral 3B | 128K | 3B | Commercial | ⭐⭐⭐ | Mobile/IoT, battery-powered |
| Ministral 8B | 128K | 8.02B | Research/Commercial | ⭐⭐⭐⭐ | Edge deployment, single GPU |
| Nemo | 128K | 12B | Apache 2.0 | ⭐⭐⭐ | General-purpose, multilingual |
Key Considerations
Aggressive Model Deprecation
Mistral deprecates models every 6-12 months. Always use dated API endpoints (e.g., mistral-large-2411) not -latest endpoints in production. Recent deprecations include Mistral 7B, Mixtral 8x7B, and Mixtral 8x22B (March 2025). Monitor https://docs.mistral.ai for deprecation notices and plan migrations accordingly.
Rate Limits and Quotas Organization-level limits (not per-user) scale with billing tier. Free tier: 1 RPS, 500K tokens/min, 1B tokens/month - not suitable for production. Upgrade to commercial tier for production workloads. HTTP 429 errors indicate exceeded limits. Implement exponential backoff and consider batch processing (60% cheaper) for non-real-time workloads.
Data Privacy and Sovereignty EU-hosted infrastructure with GDPR compliance. Data isolation and zero retention options available for sensitive workloads. Free tier data may be used for model improvement unless opted out. Pro/Enterprise tiers offer hard opt-out with no data retention. Critical for European data sovereignty requirements.
Licensing Complexity Apache 2.0 models (Small, Pixtral 12B, Nemo, Devstral Small, Magistral Small, Voxtral, Ministral 8B) allow free commercial use. Mistral Research License models (Large, Pixtral Large, Magistral Medium) require separate commercial licensing. Proprietary models (Codestral, Devstral Medium, Medium 3, Ministral 3B) require API or custom licensing. Verify licensing before deployment.
Resources
Official Documentation: https://docs.mistral.ai/
Platform/Console: https://console.mistral.ai/
Model Weights: https://huggingface.co/mistralai (for open-source models)
API Reference: https://docs.mistral.ai/api/
Pricing Information: https://mistral.ai/technology/#pricing
Cloud Partnerships: Azure AI Studio, AWS Bedrock, Google Vertex AI, IBM Watsonx