{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-4-maverick","slug":"meta-llama-llama-4-maverick","name":"Meta: Llama 4 Maverick","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-4-maverick","page_url":"https://unfragile.ai/meta-llama-llama-4-maverick","categories":["model-training"],"tags":["meta-llama","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-4-maverick__cap_0","uri":"capability://text.generation.language.multimodal.instruction.following.with.mixture.of.experts.routing","name":"multimodal instruction-following with mixture-of-experts routing","description":"Llama 4 Maverick processes both text and image inputs through a 128-expert mixture-of-experts (MoE) architecture where a learned gating network dynamically routes tokens to specialized expert subnetworks based on input characteristics. Only 17B parameters are active per forward pass despite the larger total model capacity, enabling efficient inference while maintaining high-quality instruction following across modalities. The MoE design allows different experts to specialize in text reasoning, visual understanding, and cross-modal fusion without requiring separate model weights.","intents":["I need a single model that can understand both text prompts and images without separate vision encoders","I want efficient inference with conditional computation that only activates relevant model capacity","I need instruction-following that generalizes across text-only, image-only, and image+text tasks","I want to reduce latency and token costs by using sparse activation instead of dense models"],"best_for":["teams building multimodal AI applications requiring cost-efficient inference","developers deploying on resource-constrained infrastructure who need both vision and language","builders creating instruction-following agents that process mixed-media documents"],"limitations":["MoE routing adds ~50-100ms latency overhead per inference due to gating network computation and expert selection","Load balancing across 128 experts can cause uneven GPU utilization if token distribution is skewed","No fine-tuning support documented — model is inference-only via OpenRouter API","Expert specialization is learned during training and not interpretable or modifiable post-hoc","Requires sufficient context window management for image tokens which can consume 500-2000 tokens per image"],"requires":["OpenRouter API key with access to meta-llama models","HTTP/REST client capability or OpenRouter SDK","Support for multipart/form-data or base64 image encoding for image inputs","Minimum 16GB GPU memory if self-hosting (not applicable via OpenRouter)"],"input_types":["text (natural language instructions, prompts)","image (JPEG, PNG, WebP formats)","mixed text+image (interleaved prompts with visual context)"],"output_types":["text (natural language responses, reasoning chains)","structured text (JSON, markdown, code blocks)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-maverick__cap_1","uri":"capability://image.visual.visual.reasoning.and.scene.understanding.from.images","name":"visual reasoning and scene understanding from images","description":"Llama 4 Maverick processes image inputs through a visual encoder that converts pixel data into token embeddings, which are then routed through the MoE network alongside text tokens. The model performs spatial reasoning, object detection, scene understanding, and visual question answering by jointly attending to visual and textual context. The architecture treats images as sequences of visual tokens, enabling the same transformer attention mechanisms used for text to operate on visual features.","intents":["I need to ask questions about images and get detailed descriptions of visual content","I want to extract structured information from screenshots, diagrams, or documents with visual elements","I need to perform visual reasoning tasks like counting objects, spatial relationships, or scene analysis","I want to understand charts, graphs, and infographics by converting visual data to text descriptions"],"best_for":["document processing pipelines that need to extract meaning from mixed text and image content","accessibility tools converting visual content to natural language descriptions","data extraction from screenshots, forms, and visual documents at scale"],"limitations":["Image resolution is limited by token budget — high-resolution images may be downsampled or cropped","Visual understanding is constrained by training data; performance on domain-specific visuals (medical imaging, scientific diagrams) is not documented","No bounding box output or pixel-level localization — only text descriptions of visual content","Image token consumption (500-2000 tokens per image) significantly reduces available context for text responses","No support for video input — only static images"],"requires":["OpenRouter API key with multimodal model access","Image in JPEG, PNG, or WebP format (typically <20MB)","Base64 encoding or multipart upload capability for image transmission","Sufficient API rate limits for batch image processing"],"input_types":["image (JPEG, PNG, WebP)","text (natural language questions or instructions about the image)"],"output_types":["text (descriptions, answers, extracted information)","structured text (JSON with extracted fields, markdown tables)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-maverick__cap_2","uri":"capability://text.generation.language.instruction.following.with.complex.multi.step.reasoning","name":"instruction-following with complex multi-step reasoning","description":"Llama 4 Maverick is instruction-tuned to follow detailed, multi-step prompts by leveraging its 128-expert architecture to allocate specialized experts for different reasoning phases. The model can decompose complex instructions into sub-tasks, maintain context across multiple reasoning steps, and generate coherent responses that follow specified formats or constraints. The MoE routing allows different experts to specialize in instruction parsing, reasoning, and output formatting without model capacity waste.","intents":["I need the model to follow complex, multi-part instructions with specific output formatting requirements","I want to use chain-of-thought prompting to get step-by-step reasoning before final answers","I need the model to handle conditional logic in prompts (if-then instructions, branching tasks)","I want to enforce output structure (JSON, XML, markdown) through instruction-following"],"best_for":["developers building structured data extraction pipelines with natural language instructions","teams using prompt engineering for complex reasoning tasks without fine-tuning","builders creating multi-step AI workflows that rely on instruction adherence"],"limitations":["Instruction-following quality degrades with very long or ambiguous instructions (>2000 tokens)","No guaranteed output format compliance — model may deviate from JSON/XML structure despite instructions","Reasoning steps are not separately scored or validated — no confidence metrics for intermediate steps","Context window limitations mean complex multi-step tasks may lose earlier instructions as context fills","No built-in constraint satisfaction or validation — requires post-processing to enforce hard constraints"],"requires":["OpenRouter API key","Well-structured, clear prompts (instruction quality directly impacts output quality)","Post-processing logic to validate output format and structure","Understanding of prompt engineering best practices for instruction-tuned models"],"input_types":["text (natural language instructions, prompts with formatting requirements)"],"output_types":["text (formatted responses following instruction specifications)","structured text (JSON, XML, markdown, code blocks)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-maverick__cap_3","uri":"capability://text.generation.language.context.aware.text.generation.with.long.range.dependencies","name":"context-aware text generation with long-range dependencies","description":"Llama 4 Maverick generates coherent text by maintaining attention over long context windows, with the MoE architecture enabling selective expert activation based on context characteristics. The model can track long-range dependencies, maintain narrative consistency across multiple paragraphs, and generate contextually appropriate responses that reference earlier parts of the conversation or document. The sparse activation pattern allows different experts to specialize in local coherence, long-range dependency tracking, and semantic consistency.","intents":["I need to generate multi-paragraph responses that maintain narrative consistency and coherence","I want the model to reference and build upon earlier context in a conversation","I need to generate text that follows specific stylistic or tonal guidelines established in the prompt","I want to create content that maintains semantic consistency across long documents"],"best_for":["content creators generating long-form articles, stories, or documentation","chatbot developers building conversational agents with multi-turn context","teams creating summarization or paraphrasing pipelines that preserve meaning"],"limitations":["Context window size limits long-range dependency tracking — very long documents may lose early context","No explicit memory mechanism — context is limited to the current conversation window","Repetition and hallucination can occur in very long generations (>2000 tokens)","No built-in fact-checking or grounding — generated text may contain plausible-sounding but false information","Coherence quality degrades when context is highly technical or domain-specific without training data"],"requires":["OpenRouter API key","Clear, well-structured prompts that establish context and tone","Post-generation review for factual accuracy and coherence in critical applications"],"input_types":["text (prompts, context, style guidelines)"],"output_types":["text (generated content, responses, continuations)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-maverick__cap_4","uri":"capability://image.visual.cross.modal.reasoning.between.text.and.image.inputs","name":"cross-modal reasoning between text and image inputs","description":"Llama 4 Maverick performs joint reasoning over text and image inputs by routing both text tokens and visual tokens through the same MoE network, enabling the model to answer questions that require understanding relationships between visual and textual information. The architecture treats visual and textual tokens uniformly in the transformer, allowing attention mechanisms to naturally fuse information across modalities. Experts can specialize in text-to-image grounding, image-to-text translation, and cross-modal semantic alignment.","intents":["I need to answer questions that require understanding both text descriptions and accompanying images","I want to verify if text claims match visual content in images","I need to generate text descriptions that reference specific visual elements in images","I want to perform visual search or matching tasks where text queries are matched against image content"],"best_for":["document understanding systems that process mixed text-image documents","fact-checking tools that verify text against visual evidence","accessibility tools that generate detailed descriptions of images with text context","multimodal search and retrieval systems"],"limitations":["Cross-modal alignment quality depends on training data — may struggle with uncommon visual-text combinations","No explicit grounding mechanism — cannot point to specific image regions when referencing visual elements","Image token consumption reduces available context for text, limiting the amount of text that can be processed alongside images","No support for multiple images in a single query (or limited support not documented)","Reasoning about abstract relationships between text and images may be limited compared to specialized vision-language models"],"requires":["OpenRouter API key with multimodal access","Both text prompt and image input in supported formats","Clear instructions that specify how text and image should be related or analyzed"],"input_types":["text (questions, prompts, descriptions)","image (JPEG, PNG, WebP)"],"output_types":["text (answers, descriptions, verification results)","structured text (JSON with cross-modal analysis)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-4-maverick__cap_5","uri":"capability://text.generation.language.efficient.inference.via.sparse.mixture.of.experts.activation","name":"efficient inference via sparse mixture-of-experts activation","description":"Llama 4 Maverick uses a 128-expert mixture-of-experts architecture where a learned gating network routes each token to a subset of experts based on token characteristics, resulting in only 17B active parameters per forward pass despite larger total capacity. This sparse activation pattern reduces computational cost and latency compared to dense models while maintaining model capacity for diverse tasks. The routing is learned end-to-end during training and is non-differentiable at inference time, enabling deterministic expert selection.","intents":["I need to reduce inference latency and API costs compared to dense models of similar capability","I want to serve a high-capacity model on resource-constrained infrastructure","I need to balance model capacity with inference efficiency for production deployments","I want to understand how much computation is actually used per inference"],"best_for":["teams deploying models in cost-sensitive environments (high-volume inference)","builders creating latency-sensitive applications that need high model capacity","organizations optimizing API costs for large-scale inference workloads"],"limitations":["Expert load balancing can be uneven if token distribution is skewed, causing some experts to be underutilized","Gating network overhead adds ~50-100ms per inference compared to direct token processing","No visibility into expert utilization or routing decisions — black box routing mechanism","Expert specialization is not interpretable or controllable post-hoc","Sparse activation may reduce model robustness on out-of-distribution inputs that don't match training token distributions"],"requires":["OpenRouter API key (no self-hosting documentation provided)","Understanding that 17B active parameters ≠ 17B total parameters — total model size is larger","Acceptance of non-deterministic expert routing behavior across different hardware/batch sizes"],"input_types":["text (any input that would work with dense models)"],"output_types":["text (any output that would work with dense models)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key with access to meta-llama models","HTTP/REST client capability or OpenRouter SDK","Support for multipart/form-data or base64 image encoding for image inputs","Minimum 16GB GPU memory if self-hosting (not applicable via OpenRouter)","OpenRouter API key with multimodal model access","Image in JPEG, PNG, or WebP format (typically <20MB)","Base64 encoding or multipart upload capability for image transmission","Sufficient API rate limits for batch image processing","OpenRouter API key","Well-structured, clear prompts (instruction quality directly impacts output quality)"],"failure_modes":["MoE routing adds ~50-100ms latency overhead per inference due to gating network computation and expert selection","Load balancing across 128 experts can cause uneven GPU utilization if token distribution is skewed","No fine-tuning support documented — model is inference-only via OpenRouter API","Expert specialization is learned during training and not interpretable or modifiable post-hoc","Requires sufficient context window management for image tokens which can consume 500-2000 tokens per image","Image resolution is limited by token budget — high-resolution images may be downsampled or cropped","Visual understanding is constrained by training data; performance on domain-specific visuals (medical imaging, scientific diagrams) is not documented","No bounding box output or pixel-level localization — only text descriptions of visual content","Image token consumption (500-2000 tokens per image) significantly reduces available context for text responses","No support for video input — only static images","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.27,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","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-maverick","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-4-maverick"}},"signature":"6E1rMhL5/Jz5X34egY+JgXZC1RyBBMT/omfU3+CjHPwn084tuUQzUTPLSBbJvH+ijZ90rsTYx+a8u7eZDhk5Bw==","signedAt":"2026-06-20T08:41:03.264Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-4-maverick","artifact":"https://unfragile.ai/meta-llama-llama-4-maverick","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-4-maverick","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"}}