{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-4-scout","slug":"meta-llama-llama-4-scout","name":"Meta: Llama 4 Scout","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-4-scout","page_url":"https://unfragile.ai/meta-llama-llama-4-scout","categories":["llm-apis"],"tags":["meta-llama","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$8.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-4-scout__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.language.generation.with.dynamic.token.routing","name":"sparse mixture-of-experts language generation with dynamic token routing","description":"Llama 4 Scout implements a sparse MoE architecture that activates only 17B parameters from a 109B parameter pool, routing each token to specialized expert sub-networks based on learned routing weights. This approach reduces computational cost per inference while maintaining model capacity through conditional computation — only the most relevant experts process each token, enabling faster generation on resource-constrained hardware without full model loading.","intents":["Deploy a capable language model with lower latency and memory footprint than dense models","Run inference on edge devices or cost-sensitive cloud infrastructure","Understand how sparse routing decisions affect model behavior for specific domains"],"best_for":["teams building cost-optimized LLM applications with latency constraints","developers deploying models on edge hardware or serverless functions","organizations optimizing inference spend across high-volume API calls"],"limitations":["MoE routing adds non-deterministic latency variance — some tokens may route to slower experts, causing unpredictable per-token generation times","Expert specialization may create knowledge gaps at domain boundaries where no expert specializes, degrading performance on cross-domain reasoning","Requires MoE-aware quantization and optimization; standard dense-model optimization techniques may not apply effectively"],"requires":["OpenRouter API key or compatible inference endpoint supporting MoE model serving","Client library supporting streaming token generation (e.g., OpenAI Python SDK, LangChain)","Minimum 24GB VRAM if self-hosting, or cloud inference service with MoE support"],"input_types":["text prompts","multi-turn conversation history","system instructions"],"output_types":["text generation (streaming or batch)","structured JSON via prompt engineering"],"categories":["text-generation-language","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_1","uri":"capability://image.visual.native.multimodal.input.processing.with.vision.language.fusion","name":"native multimodal input processing with vision-language fusion","description":"Llama 4 Scout accepts both text and image inputs in a single request, processing visual information through an integrated vision encoder that projects image features into the language model's token space. The architecture fuses image embeddings with text tokens in a unified sequence, allowing the model to reason jointly over visual and textual context without separate preprocessing or external vision APIs.","intents":["Analyze images and answer questions about their content in natural language","Extract structured information from documents, screenshots, or diagrams","Build multimodal chatbots that understand both text and visual context"],"best_for":["developers building document understanding or visual QA applications","teams creating multimodal chatbots without managing separate vision models","applications requiring joint reasoning over text and image without pipeline latency"],"limitations":["Image resolution and aspect ratio constraints — very high-resolution images may be downsampled, losing fine detail","No native video support — only static images; video requires frame extraction preprocessing","Vision encoder is frozen (non-trainable) — cannot fine-tune visual understanding for domain-specific image types"],"requires":["OpenRouter API key with multimodal model access","Image input as base64-encoded string or URL (JPEG, PNG, WebP supported)","Client supporting multimodal message format (e.g., OpenAI API with vision_content type)"],"input_types":["text prompts","images (JPEG, PNG, WebP)","mixed text + image sequences"],"output_types":["text descriptions and analysis","structured JSON extracted from images","conversational responses grounded in visual content"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_2","uri":"capability://text.generation.language.instruction.tuned.conversational.generation.with.system.prompt.control","name":"instruction-tuned conversational generation with system prompt control","description":"Llama 4 Scout is fine-tuned on instruction-following data, enabling it to respond to explicit directives, system prompts, and multi-turn conversation context. The model supports role-based system instructions that shape behavior (e.g., 'You are a Python expert'), allowing developers to customize response style, tone, and domain focus without retraining. The architecture maintains conversation history state across turns, enabling coherent multi-step interactions.","intents":["Build chatbots with customizable personality and expertise via system prompts","Generate domain-specific responses (code, technical writing, creative content) through instruction engineering","Maintain context across multi-turn conversations for coherent dialogue"],"best_for":["developers building conversational AI applications with custom behavior","teams creating domain-specific assistants (coding, writing, analysis) without fine-tuning","applications requiring role-based or persona-driven responses"],"limitations":["System prompt injection risk — untrusted user input in system prompts can override intended behavior","Instruction following degrades on adversarial or conflicting instructions — no built-in conflict resolution","Context window limitations (likely 8K-16K tokens) — long conversations require summarization or context pruning"],"requires":["OpenRouter API key","Client library supporting system message format (OpenAI API compatible)","Understanding of prompt engineering best practices for consistent behavior"],"input_types":["system prompts (role/behavior definition)","user messages (single or multi-turn)","conversation history"],"output_types":["natural language responses","code snippets","structured text (lists, tables, JSON via prompting)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_3","uri":"capability://tool.use.integration.api.based.inference.with.streaming.token.generation","name":"api-based inference with streaming token generation","description":"Llama 4 Scout is accessed exclusively through OpenRouter's API, supporting both streaming and batch inference modes. Streaming mode returns tokens incrementally as they are generated, enabling real-time response display in user interfaces. The API abstracts away model serving complexity, handling load balancing, hardware allocation, and multi-user concurrency automatically.","intents":["Integrate a capable language model into applications without managing inference infrastructure","Stream generated text to users in real-time for responsive chat interfaces","Scale inference across variable load without provisioning dedicated hardware"],"best_for":["startups and small teams without ML infrastructure expertise","applications requiring dynamic scaling without capacity planning","developers building prototypes or MVPs with minimal DevOps overhead"],"limitations":["API latency adds 100-500ms per request due to network round-trip and queue wait times","Pricing per token — high-volume applications may be more cost-effective with self-hosted inference","Vendor lock-in to OpenRouter — switching providers requires API client changes","No local model caching — every inference request hits the remote API"],"requires":["OpenRouter API key (paid account)","HTTP client library (curl, Python requests, JavaScript fetch)","Network connectivity and acceptable latency tolerance (100ms+)"],"input_types":["JSON request bodies with messages, system prompts, and generation parameters"],"output_types":["streaming token chunks (Server-Sent Events format)","complete response JSON (batch mode)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_4","uri":"capability://code.generation.editing.parameter.efficient.inference.with.quantization.friendly.architecture","name":"parameter-efficient inference with quantization-friendly architecture","description":"Llama 4 Scout's sparse MoE design is inherently quantization-friendly — because only 17B of 109B parameters activate per forward pass, quantization (8-bit, 4-bit) has less impact on quality compared to dense models. The routing mechanism remains in full precision while expert weights can be aggressively quantized, enabling deployment on consumer GPUs or edge devices with minimal quality degradation.","intents":["Deploy Llama 4 Scout on consumer-grade GPUs (RTX 4090, A100) with 8-bit or 4-bit quantization","Run inference on edge devices (mobile, IoT) with extreme memory constraints","Reduce inference cost by combining sparse activation with aggressive quantization"],"best_for":["teams deploying models on resource-constrained hardware","edge AI applications requiring on-device inference","organizations optimizing inference cost through hardware-efficient techniques"],"limitations":["Quantization reduces numerical precision — may degrade performance on tasks requiring exact arithmetic or fine-grained reasoning","Routing precision loss — quantizing routing weights can cause suboptimal expert selection, reducing MoE benefits","Limited quantization tooling — not all quantization frameworks (GPTQ, AWQ) have optimized MoE support; may require custom implementation"],"requires":["Quantization framework (bitsandbytes, GPTQ, AWQ, or similar) with MoE support","GPU with sufficient VRAM for 17B active parameters + routing overhead (typically 24GB+)","Knowledge of quantization trade-offs and testing methodology"],"input_types":["full-precision model weights (109B)","quantization configuration (bit-width, calibration data)"],"output_types":["quantized model checkpoint (8-bit or 4-bit)","inference performance metrics (latency, memory, quality)"],"categories":["code-generation-editing","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_5","uri":"capability://planning.reasoning.context.aware.reasoning.with.chain.of.thought.prompting.support","name":"context-aware reasoning with chain-of-thought prompting support","description":"Llama 4 Scout supports explicit chain-of-thought (CoT) prompting patterns, where the model generates intermediate reasoning steps before producing final answers. The instruction-tuned architecture recognizes CoT patterns (e.g., 'Let me think step by step...') and allocates expert routing to reasoning-specialized experts, improving performance on complex multi-step problems. This enables developers to trade generation speed for reasoning quality by requesting explicit reasoning traces.","intents":["Improve model accuracy on complex reasoning tasks by requesting step-by-step explanations","Debug model reasoning by examining intermediate steps and identifying error sources","Build applications requiring explainable AI where reasoning traces are user-facing"],"best_for":["applications requiring high-accuracy reasoning (math, logic, code generation)","teams building explainable AI systems where reasoning transparency is critical","developers debugging model failures by analyzing intermediate reasoning steps"],"limitations":["CoT increases token generation by 2-5x — longer responses mean higher latency and API costs","Reasoning quality depends on prompt engineering — poorly formatted CoT prompts may not trigger reasoning experts","No guarantee of correct reasoning — model can generate plausible-sounding but incorrect intermediate steps"],"requires":["Prompt engineering knowledge to structure CoT requests effectively","Acceptance of longer generation times and higher token costs","Validation logic to verify reasoning correctness (not built-in)"],"input_types":["text prompts with explicit CoT instructions (e.g., 'Think step by step')","complex reasoning tasks (math, logic, code generation)"],"output_types":["intermediate reasoning steps (text)","final answer or solution","structured reasoning traces (via prompt engineering)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-scout__cap_6","uri":"capability://automation.workflow.batch.inference.with.asynchronous.processing","name":"batch inference with asynchronous processing","description":"Llama 4 Scout supports batch inference mode through OpenRouter, accepting multiple requests in a single API call and returning results asynchronously. This mode optimizes throughput by amortizing API overhead and enabling the inference backend to schedule requests efficiently across available hardware. Batch mode is ideal for non-latency-sensitive workloads like document processing, content generation, or overnight analysis jobs.","intents":["Process large volumes of text (100s-1000s of documents) with lower per-request cost","Generate content in bulk (e.g., product descriptions, email templates) without real-time latency requirements","Analyze datasets by running inference on all samples in a single batch"],"best_for":["data processing pipelines with flexible latency requirements","content generation workflows (bulk writing, summarization)","teams optimizing inference cost by batching requests"],"limitations":["Batch processing introduces latency — results may not be available for minutes to hours depending on queue depth","No streaming support in batch mode — must wait for complete response before processing","Batch size limits — OpenRouter may cap batch sizes to prevent resource exhaustion"],"requires":["OpenRouter API key with batch processing support","Asynchronous processing infrastructure to handle delayed results","Polling or webhook mechanism to retrieve batch results"],"input_types":["array of JSON request objects (messages, prompts, parameters)"],"output_types":["array of complete response objects (no streaming)","batch job ID for status tracking"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or compatible inference endpoint supporting MoE model serving","Client library supporting streaming token generation (e.g., OpenAI Python SDK, LangChain)","Minimum 24GB VRAM if self-hosting, or cloud inference service with MoE support","OpenRouter API key with multimodal model access","Image input as base64-encoded string or URL (JPEG, PNG, WebP supported)","Client supporting multimodal message format (e.g., OpenAI API with vision_content type)","OpenRouter API key","Client library supporting system message format (OpenAI API compatible)","Understanding of prompt engineering best practices for consistent behavior","OpenRouter API key (paid account)"],"failure_modes":["MoE routing adds non-deterministic latency variance — some tokens may route to slower experts, causing unpredictable per-token generation times","Expert specialization may create knowledge gaps at domain boundaries where no expert specializes, degrading performance on cross-domain reasoning","Requires MoE-aware quantization and optimization; standard dense-model optimization techniques may not apply effectively","Image resolution and aspect ratio constraints — very high-resolution images may be downsampled, losing fine detail","No native video support — only static images; video requires frame extraction preprocessing","Vision encoder is frozen (non-trainable) — cannot fine-tune visual understanding for domain-specific image types","System prompt injection risk — untrusted user input in system prompts can override intended behavior","Instruction following degrades on adversarial or conflicting instructions — no built-in conflict resolution","Context window limitations (likely 8K-16K tokens) — long conversations require summarization or context pruning","API latency adds 100-500ms per request due to network round-trip and queue wait times","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.27,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=meta-llama-llama-4-scout","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-4-scout"}},"signature":"yS1+lUZ9F1js5oZ2uYrWPf6DENL2ldTB3aF/Ly3YB6i6/gDyZtlS/0vcqQoOdgkgrKl94fwKwEF/dP8+YQhnAQ==","signedAt":"2026-06-16T04:51:24.005Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-4-scout","artifact":"https://unfragile.ai/meta-llama-llama-4-scout","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-4-scout","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"}}