{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mistral-nemo","slug":"mistral-nemo","name":"Mistral Nemo","type":"model","url":"https://mistral.ai/news/mistral-nemo","page_url":"https://unfragile.ai/mistral-nemo","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mistral-nemo__cap_0","uri":"capability://text.generation.language.multilingual.text.generation.with.128k.context.window","name":"multilingual text generation with 128k context window","description":"Generates coherent text across 100+ languages using a Transformer architecture with a 128K token context window, trained on multilingual corpora with a custom Tekken tokenizer that achieves 30% better compression efficiency than SentencePiece on code and non-English languages. The model maintains context awareness across extended conversations and documents through standard causal self-attention mechanisms scaled to handle 128K tokens without architectural modifications.","intents":["Generate multi-turn conversations in non-English languages without losing context","Process long documents (research papers, books, code files) in a single inference pass","Build chatbots that understand and respond in 100+ languages with consistent quality","Create content in multiple languages while maintaining semantic coherence across long sequences"],"best_for":["teams building multilingual AI applications targeting global audiences","developers needing long-context understanding for document processing in non-English languages","organizations deploying language models in resource-constrained environments requiring compact models"],"limitations":["Context window hard-capped at 128K tokens (~96KB of text) — cannot process documents longer than this","Multilingual performance varies by language; benchmark data not provided for all 100+ supported languages","No explicit performance guarantees for low-resource languages or specialized technical domains","Tokenizer efficiency gains do not translate to proportional inference speedup — compression is preprocessing only"],"requires":["API key for Mistral's la Plateforme or NVIDIA ai.nvidia.com endpoint","Minimum 8GB GPU VRAM for FP8 quantized inference (exact requirements not specified)","Python 3.8+ for local inference via mistral-inference library"],"input_types":["text (UTF-8 encoded, any of 100+ supported languages)","code (treated as text, no special syntax awareness)"],"output_types":["text (variable length, language matches input or specified target language)"],"categories":["text-generation-language","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.function.calling","name":"code generation and completion with function calling","description":"Generates and completes code across multiple programming languages using a Transformer trained with code-specific data and explicit function-calling capabilities. The model supports structured function invocation through a schema-based registry, enabling it to call external APIs and tools directly from generated code without requiring post-processing or manual parsing of function signatures.","intents":["Generate syntactically correct code snippets in response to natural language descriptions","Complete partial code with context-aware suggestions maintaining code style and patterns","Invoke external APIs and tools directly by generating properly-formatted function calls","Build AI agents that can call multiple functions in sequence to solve complex tasks"],"best_for":["developers building code-generation features into IDEs or development tools","teams creating AI agents that need to interact with external APIs and services","organizations automating code completion and refactoring workflows"],"limitations":["No explicit list of supported programming languages provided — claim is general 'code generation' capability","Function calling format and schema specification not documented in source material","No benchmarks provided for code generation accuracy, syntax correctness, or function call success rates","Cannot guarantee generated code is production-ready or follows security best practices"],"requires":["API key for Mistral's la Plateforme (model: open-mistral-nemo-2407)","Function schema definitions in JSON format (exact format specification unknown)","Python 3.8+ for local inference or REST client for API calls"],"input_types":["text (natural language code descriptions or partial code snippets)","code (incomplete code for completion tasks)"],"output_types":["code (generated or completed code in target language)","structured function calls (JSON-formatted function invocations with parameters)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_10","uri":"capability://planning.reasoning.reasoning.and.complex.task.decomposition","name":"reasoning and complex task decomposition","description":"Trained to handle reasoning tasks and decompose complex problems into steps through Transformer architecture with extended context window enabling multi-step reasoning chains. The model can maintain reasoning state across multiple turns and generate intermediate reasoning steps, though specific reasoning techniques (chain-of-thought, tree-of-thought, etc.) are not documented.","intents":["Solve multi-step math and logic problems by generating reasoning chains","Break down complex tasks into subtasks and solve them sequentially","Generate explanations for decisions and reasoning steps","Build AI agents that can plan and reason through complex workflows"],"best_for":["applications requiring step-by-step problem solving and explanation","AI agents that need to decompose complex tasks into subtasks","educational applications teaching reasoning and problem-solving","systems requiring interpretable AI decisions with reasoning chains"],"limitations":["No specific benchmarks provided for reasoning accuracy or task decomposition quality","Reasoning capability boundaries not documented — unknown failure modes","No explicit support for structured reasoning formats (JSON reasoning chains, etc.)","Reasoning quality depends heavily on prompt engineering — no built-in reasoning framework"],"requires":["API key for Mistral's la Plateforme or local inference setup","Well-crafted prompts that encourage step-by-step reasoning","Python 3.8+ for integration"],"input_types":["text (problem statements, questions, or task descriptions)"],"output_types":["text (reasoning chains, step-by-step solutions, explanations)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_11","uri":"capability://automation.workflow.collaborative.development.with.nvidia.optimization","name":"collaborative development with nvidia optimization","description":"Developed in collaboration with NVIDIA with native optimization for NVIDIA GPU hardware and inference frameworks. The model includes NVIDIA NIM containerization, FP8 quantization support optimized for NVIDIA GPUs, and integration with NVIDIA's inference optimization tools, ensuring optimal performance on NVIDIA infrastructure without requiring manual tuning.","intents":["Deploy on NVIDIA GPUs with pre-optimized inference kernels","Access NVIDIA-specific optimizations without manual configuration","Use NVIDIA NIM for containerized inference with built-in monitoring","Leverage NVIDIA's inference acceleration tools for production deployments"],"best_for":["organizations with existing NVIDIA GPU infrastructure","teams using NVIDIA cloud services (NGC, NVIDIA AI Enterprise)","enterprises requiring NVIDIA-optimized inference for compliance or performance"],"limitations":["Optimization is NVIDIA-specific — may not perform optimally on non-NVIDIA hardware","Specific NVIDIA GPU models and architectures not listed in documentation","No performance comparisons provided for NVIDIA vs other hardware platforms","NVIDIA-specific features may not be available in open-weight model variant"],"requires":["NVIDIA GPU with CUDA compute capability 7.0+ (A100, H100, L40S, or compatible)","NVIDIA CUDA 12.0+ and cuDNN","NVIDIA Container Toolkit for containerized deployment","NVIDIA NIM runtime (optional but recommended)"],"input_types":["text (any supported language)"],"output_types":["text (generated output)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_2","uri":"capability://text.generation.language.instruction.following.and.multi.turn.conversation","name":"instruction-following and multi-turn conversation","description":"Processes natural language instructions and maintains coherent multi-turn conversations through an instruction-tuned variant trained with advanced fine-tuning and alignment techniques. The model uses standard Transformer decoder architecture with causal masking to track conversation history and respond contextually, evaluated against GPT-4o as a reference judge for instruction adherence and reasoning quality.","intents":["Build chatbots that follow complex multi-step instructions accurately","Create conversational AI that maintains context across 10+ turns without losing coherence","Implement AI assistants that can reason through problems and explain their thinking","Deploy instruction-following models that align with user intent and preferences"],"best_for":["teams building conversational AI products and chatbot applications","developers creating AI assistants that need to follow detailed user instructions","organizations deploying customer-facing AI agents requiring high instruction adherence"],"limitations":["Evaluation methodology uses GPT-4o as judge, potentially introducing bias toward GPT-4o-aligned outputs","No independent benchmarks provided for instruction-following accuracy or reasoning quality","Specific fine-tuning dataset and alignment methodology not disclosed","No quantitative metrics for multi-turn conversation coherence or context retention"],"requires":["API key for Mistral's la Plateforme (model: open-mistral-nemo-2407 for instruction-tuned variant)","Conversation history management in application layer (model does not persist state)","Python 3.8+ or REST client for API integration"],"input_types":["text (natural language instructions, questions, or conversation turns)"],"output_types":["text (instruction-following responses, reasoning chains, conversational replies)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_3","uri":"capability://code.generation.editing.quantization.aware.inference.with.fp8.support","name":"quantization-aware inference with fp8 support","description":"Supports FP8 (8-bit floating point) quantized inference without claimed performance degradation through quantization-aware training during model development. The model weights are pre-optimized for low-precision computation, enabling deployment on hardware with limited memory and reduced inference latency through native FP8 support in NVIDIA GPUs and compatible inference engines.","intents":["Deploy language models on edge devices or resource-constrained servers with limited GPU VRAM","Reduce inference latency and memory footprint for high-throughput production deployments","Run the model locally without cloud API calls while maintaining quality","Optimize inference cost by reducing GPU memory requirements and enabling smaller instance types"],"best_for":["teams deploying models on edge devices, mobile servers, or cost-optimized cloud instances","organizations requiring low-latency local inference without cloud dependencies","developers building resource-constrained AI applications with strict memory budgets"],"limitations":["Claim of 'no performance loss' in FP8 is unverified by independent benchmarks","No quantitative metrics provided for inference speedup or memory reduction vs FP16/FP32","FP8 support depends on hardware capabilities — not all GPUs support native FP8 operations","Quantization-aware training details not disclosed — reproducibility and generalization unknown"],"requires":["NVIDIA GPU with native FP8 support (A100, H100, or newer architectures)","NVIDIA NIM container runtime or mistral-inference library with FP8 backend","Minimum 8GB GPU VRAM (exact requirement not specified in documentation)","CUDA 12.0+ for optimal FP8 performance"],"input_types":["text (any language supported by base model)"],"output_types":["text (same output quality as FP16/FP32 variants, claimed)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_4","uri":"capability://data.processing.analysis.efficient.tokenization.across.100.languages","name":"efficient tokenization across 100+ languages","description":"Uses a custom Tekken tokenizer (based on Tiktoken architecture) trained on 100+ languages to achieve significantly better compression efficiency than standard tokenizers like SentencePiece or Llama 3's tokenizer. The tokenizer reduces token overhead by 30% on code and non-Latin languages, 2x on Korean, and 3x on Arabic, directly reducing inference cost and context window consumption for multilingual workloads.","intents":["Reduce token consumption and API costs for multilingual text processing","Process longer documents in the same context window by using fewer tokens","Improve inference latency by reducing token count per request","Build cost-efficient multilingual applications with better token-to-character ratios"],"best_for":["teams processing high volumes of multilingual text with per-token billing models","organizations optimizing inference cost for non-English language workloads","developers building applications targeting languages with poor tokenizer efficiency (Arabic, Korean, CJK)"],"limitations":["Tokenizer efficiency gains apply only to preprocessing — inference speed improvement is indirect","Compression efficiency varies significantly by language; no comprehensive benchmark table provided","Tekken tokenizer not available as standalone tool — only accessible through Mistral models","Switching tokenizers may break compatibility with existing fine-tuned models or prompts"],"requires":["Mistral Nemo model deployment (tokenizer is bundled, not separately installable)","API key for Mistral's la Plateforme or local inference setup","No additional dependencies beyond base model requirements"],"input_types":["text (UTF-8 encoded, any of 100+ supported languages)"],"output_types":["token sequence (integer token IDs with reduced count vs standard tokenizers)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_5","uri":"capability://text.generation.language.drop.in.replacement.compatibility.with.mistral.7b","name":"drop-in replacement compatibility with mistral 7b","description":"Designed as a drop-in replacement for Mistral 7B with compatible API signatures and model interface, enabling existing applications built on Mistral 7B to switch to Nemo without code changes. The model maintains API compatibility while offering improved performance through larger parameter count (12B vs 7B) and extended context window (128K vs 32K), using identical Transformer architecture patterns.","intents":["Upgrade existing Mistral 7B deployments to better performance without refactoring application code","A/B test Mistral Nemo against Mistral 7B with minimal integration effort","Migrate from Mistral 7B to Nemo while preserving existing prompt engineering and fine-tuning work","Evaluate performance improvements by swapping model identifiers in API calls"],"best_for":["teams already using Mistral 7B seeking incremental performance improvements","organizations with existing Mistral 7B deployments wanting to upgrade without refactoring","developers evaluating Mistral model family with minimal switching costs"],"limitations":["Drop-in compatibility claim not independently verified — may require minor prompt adjustments","Fine-tuned Mistral 7B models may not transfer directly to Nemo without retraining","Larger model size (12B vs 7B) requires more GPU VRAM — may not fit on same hardware","API compatibility does not guarantee identical output for same prompts — model behavior will differ"],"requires":["Existing Mistral 7B integration or API client code","API key for Mistral's la Plateforme (same credentials as Mistral 7B)","Sufficient GPU VRAM for 12B model (vs 7B) if running locally"],"input_types":["text (same format as Mistral 7B)"],"output_types":["text (same format as Mistral 7B, but with improved quality)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_6","uri":"capability://automation.workflow.containerized.inference.via.nvidia.nim","name":"containerized inference via nvidia nim","description":"Deployable as a containerized microservice through NVIDIA NIM (NVIDIA Inference Microservice) runtime, providing a standardized inference endpoint with built-in optimizations for NVIDIA GPUs. The container includes pre-optimized inference kernels, automatic batching, and monitoring capabilities, abstracting away low-level inference complexity while maintaining high throughput and low latency.","intents":["Deploy Mistral Nemo as a production inference service with minimal infrastructure setup","Run the model on NVIDIA GPUs with automatic optimization and batching","Expose the model as a REST API endpoint for distributed applications","Monitor inference performance and resource utilization in containerized environments"],"best_for":["teams deploying models in Kubernetes or Docker-based infrastructure","organizations using NVIDIA GPUs and wanting optimized inference without custom tuning","developers building microservices that need standardized inference endpoints"],"limitations":["NVIDIA NIM requires NVIDIA GPU hardware — no CPU-only inference support","Container image size and startup time not specified in documentation","Automatic batching behavior and configuration options not documented","Monitoring and observability features not detailed in source material"],"requires":["NVIDIA GPU with CUDA compute capability 7.0+ (A100, H100, L40S, or compatible)","Docker or container runtime (Docker 20.10+, Podman, or equivalent)","NVIDIA Container Toolkit for GPU access in containers","NVIDIA NIM container image from NVIDIA NGC registry"],"input_types":["text (via REST API or gRPC)"],"output_types":["text (via REST API or gRPC response)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_7","uri":"capability://text.generation.language.base.and.instruction.tuned.model.variants","name":"base and instruction-tuned model variants","description":"Released as two distinct checkpoint variants: a base pre-trained model for general text generation and an instruction-tuned variant optimized for following user instructions and multi-turn conversations. The instruction-tuned variant undergoes additional fine-tuning and alignment phases beyond base pre-training, enabling better instruction adherence and reasoning without requiring downstream fine-tuning.","intents":["Choose between base model for custom fine-tuning vs instruction-tuned for immediate deployment","Use base model for domain-specific fine-tuning with minimal downstream training cost","Deploy instruction-tuned variant directly for chatbots and assistants without additional alignment","Compare base vs instruction-tuned performance to understand fine-tuning impact"],"best_for":["teams planning to fine-tune models on domain-specific data (use base variant)","organizations deploying chatbots and assistants immediately (use instruction-tuned variant)","researchers studying fine-tuning and alignment techniques"],"limitations":["Base model may require significant fine-tuning to match instruction-tuned performance","Fine-tuning dataset and methodology for instruction-tuned variant not disclosed","No quantitative comparison between base and instruction-tuned variants provided","Instruction-tuned variant may not be optimal for specialized domains requiring custom fine-tuning"],"requires":["API key for Mistral's la Plateforme (separate model IDs for base and instruction-tuned)","For fine-tuning: mistral-finetune library and training data","Python 3.8+ for local inference or API client"],"input_types":["text (base model: any text; instruction-tuned: natural language instructions)"],"output_types":["text (base model: continuation; instruction-tuned: instruction-following response)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_8","uri":"capability://automation.workflow.open.weight.model.with.apache.2.0.license","name":"open-weight model with apache 2.0 license","description":"Distributed as open-source weights under Apache 2.0 license, enabling unrestricted commercial use, redistribution, and modification without licensing fees or usage restrictions. The model weights are publicly available on HuggingFace, allowing local deployment, fine-tuning, and integration into proprietary applications without vendor lock-in or API dependencies.","intents":["Deploy the model locally without cloud API dependencies or per-token costs","Fine-tune the model on proprietary data without licensing restrictions","Integrate the model into commercial products without vendor lock-in concerns","Modify and redistribute the model as part of open-source or proprietary projects"],"best_for":["organizations requiring vendor-independent AI infrastructure","teams building proprietary products with open-source model foundations","researchers and developers needing full model access for experimentation","companies with data privacy requirements preventing cloud API usage"],"limitations":["Open-source weights require local infrastructure for deployment — no managed service","Responsibility for model updates, security patches, and maintenance falls on deployer","No official support or SLA guarantees from Mistral AI for self-hosted deployments","Requires technical expertise to optimize inference on local hardware"],"requires":["HuggingFace account to download model weights (free)","GPU hardware with 8GB+ VRAM for FP8 inference or 16GB+ for FP16","mistral-inference library or compatible inference engine (vLLM, TensorRT-LLM, etc.)","Python 3.8+ and CUDA 12.0+ for local inference"],"input_types":["text (any language supported by model)"],"output_types":["text (generated output)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__cap_9","uri":"capability://tool.use.integration.api.access.via.mistral.s.la.plateforme","name":"api access via mistral's la plateforme","description":"Available through Mistral's managed API platform (la Plateforme) under model identifier 'open-mistral-nemo-2407', providing REST API access without requiring local infrastructure or GPU hardware. The API handles inference, batching, and scaling transparently, with per-token billing and automatic load balancing across Mistral's infrastructure.","intents":["Access Mistral Nemo without managing local GPU infrastructure","Build applications with variable load patterns using managed API scaling","Prototype and test the model quickly without infrastructure setup","Pay only for inference usage without upfront hardware investment"],"best_for":["startups and small teams without GPU infrastructure","applications with variable or unpredictable load patterns","developers prototyping AI features before committing to infrastructure","organizations preferring managed services over self-hosted deployments"],"limitations":["Per-token pricing model increases costs for high-volume inference","API rate limits and throughput not specified in documentation","Data sent to Mistral's servers — not suitable for sensitive/proprietary data","Dependency on Mistral's service availability and API stability"],"requires":["API key from Mistral's la Plateforme (requires account creation and payment method)","HTTP client library (curl, Python requests, etc.) or Mistral SDK","Internet connectivity to Mistral's API endpoints"],"input_types":["text (JSON-formatted API requests)"],"output_types":["text (JSON-formatted API responses with generated text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-nemo__headline","uri":"capability://code.generation.editing.multilingual.code.generation.and.reasoning.model","name":"multilingual code generation and reasoning model","description":"Mistral Nemo is a 12B parameter open-weight model designed for multilingual understanding, code generation, and reasoning tasks, featuring a 128K context window for efficient performance.","intents":["best multilingual code generation model","code generation model for reasoning tasks","open-source model for multilingual understanding","AI model with large context window for code generation","best model for multilingual tasks"],"best_for":["developers needing multilingual support","projects requiring extensive context handling"],"limitations":["limited to 128,000 tokens"],"requires":["GPU for inference"],"input_types":["natural language text","source code"],"output_types":["code","text responses"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["API key for Mistral's la Plateforme or NVIDIA ai.nvidia.com endpoint","Minimum 8GB GPU VRAM for FP8 quantized inference (exact requirements not specified)","Python 3.8+ for local inference via mistral-inference library","API key for Mistral's la Plateforme (model: open-mistral-nemo-2407)","Function schema definitions in JSON format (exact format specification unknown)","Python 3.8+ for local inference or REST client for API calls","API key for Mistral's la Plateforme or local inference setup","Well-crafted prompts that encourage step-by-step reasoning","Python 3.8+ for integration","NVIDIA GPU with CUDA compute capability 7.0+ (A100, H100, L40S, or compatible)"],"failure_modes":["Context window hard-capped at 128K tokens (~96KB of text) — cannot process documents longer than this","Multilingual performance varies by language; benchmark data not provided for all 100+ supported languages","No explicit performance guarantees for low-resource languages or specialized technical domains","Tokenizer efficiency gains do not translate to proportional inference speedup — compression is preprocessing only","No explicit list of supported programming languages provided — claim is general 'code generation' capability","Function calling format and schema specification not documented in source material","No benchmarks provided for code generation accuracy, syntax correctness, or function call success rates","Cannot guarantee generated code is production-ready or follows security best practices","No specific benchmarks provided for reasoning accuracy or task decomposition quality","Reasoning capability boundaries not documented — unknown failure modes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:23.328Z","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-nemo","compare_url":"https://unfragile.ai/compare?artifact=mistral-nemo"}},"signature":"8btN2DqYwRWyNwPcw+6WLNU5GtqfSP9H87m2636qVFzMJ2eHvbjaKduXSgkXQSM0exnjAzTSPyxoBDDp1zJECg==","signedAt":"2026-06-20T21:19:45.694Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistral-nemo","artifact":"https://unfragile.ai/mistral-nemo","verify":"https://unfragile.ai/api/v1/verify?slug=mistral-nemo","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"}}