{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-x-ai-grok-4-fast","slug":"x-ai-grok-4-fast","name":"xAI: Grok 4 Fast","type":"model","url":"https://openrouter.ai/models/x-ai~grok-4-fast","page_url":"https://unfragile.ai/x-ai-grok-4-fast","categories":["llm-apis"],"tags":["x-ai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-x-ai-grok-4-fast__cap_0","uri":"capability://text.generation.language.multimodal.text.and.image.understanding.with.2m.token.context","name":"multimodal text and image understanding with 2m token context","description":"Processes both text and image inputs simultaneously within a 2M token context window, enabling analysis of long documents, multiple images, and extended conversations without context truncation. The model uses a unified transformer architecture that interleaves vision and language tokens, allowing it to maintain coherence across extended sequences while performing joint reasoning over heterogeneous input modalities.","intents":["Analyze multi-page documents with embedded images without losing context","Process image-heavy conversations that span thousands of turns","Extract and reason over structured data from scanned documents with visual context","Build applications requiring long-context multimodal understanding without chunking strategies"],"best_for":["Enterprise document processing teams handling PDFs with mixed text and images","Developers building long-context RAG systems with visual content","Teams processing video transcripts with frame analysis"],"limitations":["2M token window is effective but not infinite — very large datasets still require batching or hierarchical processing","Image resolution and quality affect token consumption; high-resolution images consume more tokens within the window","Multimodal reasoning latency increases with context length; typical inference at 2M tokens is slower than shorter-context models"],"requires":["API access via OpenRouter or xAI endpoints","Valid authentication credentials (API key)","Support for multipart/form-data or base64-encoded image payloads"],"input_types":["text (UTF-8, any language)","images (JPEG, PNG, WebP, GIF)","mixed sequences of text and images"],"output_types":["text (natural language response)","structured analysis (JSON, markdown)","reasoning traces (if reasoning variant used)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4-fast__cap_1","uri":"capability://text.generation.language.cost.optimized.inference.with.sota.efficiency.metrics","name":"cost-optimized inference with sota efficiency metrics","description":"Delivers state-of-the-art cost-per-token pricing while maintaining competitive performance on standard benchmarks, achieved through architectural optimizations including quantization-aware training, efficient attention mechanisms, and parameter sharing. The model is designed to minimize computational overhead during inference without sacrificing output quality, making it suitable for high-volume production workloads where cost per inference is a primary constraint.","intents":["Deploy large-scale inference pipelines where per-token costs directly impact unit economics","Build cost-sensitive applications serving thousands of concurrent users","Run continuous batch processing jobs with tight budget constraints","Optimize inference costs in multi-model routing systems"],"best_for":["Startups and scale-ups optimizing for unit economics in LLM-powered products","Teams running high-volume batch processing with limited inference budgets","Enterprises consolidating multiple model deployments to reduce operational costs"],"limitations":["Cost efficiency may come at the expense of peak performance on specialized tasks — not guaranteed to outperform larger models on all benchmarks","Pricing advantage diminishes for very short prompts where fixed overhead dominates","Cost benefits are relative to inference volume; single-request use cases see minimal savings"],"requires":["API access via OpenRouter or xAI endpoints","Valid authentication credentials","Monitoring infrastructure to track token consumption and costs"],"input_types":["text prompts of any length","images (for multimodal variant)","mixed text and image sequences"],"output_types":["text completions","structured data (JSON, CSV)","reasoning traces (reasoning variant)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4-fast__cap_2","uri":"capability://text.generation.language.non.reasoning.fast.inference.mode","name":"non-reasoning fast inference mode","description":"Provides rapid text and image understanding without explicit chain-of-thought reasoning, optimized for latency-sensitive applications where response time is critical. This variant skips intermediate reasoning steps and directly generates outputs, reducing token generation overhead and wall-clock inference time while maintaining quality for straightforward tasks that don't require deep multi-step reasoning.","intents":["Build real-time chat applications requiring sub-second response latency","Deploy customer support systems where response speed impacts user experience","Create interactive tools requiring immediate feedback (code completion, content suggestions)","Run high-throughput batch jobs where latency per request is a bottleneck"],"best_for":["Teams building real-time conversational interfaces with strict latency budgets (<1s)","Developers creating interactive IDE plugins or browser extensions","High-throughput batch processing systems optimizing for throughput over reasoning depth"],"limitations":["Non-reasoning mode sacrifices explicit chain-of-thought transparency — outputs lack visible reasoning traces","Performance degrades on complex multi-step reasoning tasks compared to reasoning variant","Not suitable for tasks requiring verification or explanation of reasoning steps"],"requires":["API access via OpenRouter or xAI endpoints","Valid authentication credentials","Latency monitoring to validate sub-second response times in production"],"input_types":["text prompts","images","mixed text and image sequences"],"output_types":["text completions","structured data (JSON, markdown)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4-fast__cap_3","uri":"capability://planning.reasoning.extended.reasoning.mode.with.explicit.chain.of.thought","name":"extended reasoning mode with explicit chain-of-thought","description":"Generates explicit, step-by-step reasoning traces before producing final outputs, enabling transparent multi-step problem solving and verification of model reasoning. This variant allocates additional tokens to intermediate reasoning steps, allowing the model to decompose complex problems, explore multiple solution paths, and provide auditable reasoning chains that can be inspected and validated by downstream systems or human reviewers.","intents":["Build systems requiring explainable AI where reasoning steps must be auditable","Solve complex multi-step problems (math, logic, code debugging) where intermediate steps matter","Create educational tools that teach problem-solving methodology through visible reasoning","Implement verification systems that validate reasoning correctness before accepting outputs"],"best_for":["Teams building enterprise systems with explainability requirements (finance, healthcare, legal)","Developers creating educational or tutoring applications","Researchers studying model reasoning and failure modes","Systems requiring human-in-the-loop verification of model outputs"],"limitations":["Reasoning mode increases token consumption by 2-5x compared to non-reasoning variant, raising inference costs","Longer response latency due to additional token generation for reasoning steps","Reasoning traces are not guaranteed to be correct — model can produce plausible but invalid reasoning","Increased token usage makes this variant unsuitable for high-volume, cost-sensitive deployments"],"requires":["API access via OpenRouter or xAI endpoints","Valid authentication credentials","Higher token budget or cost allocation for reasoning-heavy workloads","Parsing infrastructure to extract and validate reasoning traces from outputs"],"input_types":["text prompts","images","mixed text and image sequences"],"output_types":["text with embedded reasoning traces","structured reasoning (JSON with reasoning and answer fields)","markdown with formatted reasoning steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4-fast__cap_4","uri":"capability://tool.use.integration.api.based.model.access.with.streaming.support","name":"api-based model access with streaming support","description":"Exposes Grok 4 Fast through REST API endpoints (via OpenRouter or xAI) with support for streaming responses, enabling real-time token-by-token output delivery. The API implements standard OpenAI-compatible interfaces, allowing developers to integrate the model using existing client libraries and middleware without custom integration code. Streaming support enables progressive rendering of responses in user-facing applications, improving perceived latency and enabling cancellation of long-running requests.","intents":["Integrate Grok 4 Fast into existing applications using OpenAI-compatible client libraries","Build streaming chat interfaces that render responses token-by-token","Implement request cancellation for long-running inference tasks","Create applications that process model outputs progressively without waiting for full completion"],"best_for":["Teams with existing OpenAI integrations looking to switch models without code changes","Developers building real-time chat and streaming applications","Startups using OpenRouter for multi-model orchestration"],"limitations":["API-based access introduces network latency compared to local inference","Streaming adds overhead for token-by-token transmission; batch requests may be more efficient for non-interactive workloads","Rate limiting and quota management required for high-volume deployments","Dependency on external API availability — no offline fallback"],"requires":["API key for OpenRouter or xAI","Network connectivity to API endpoints","OpenAI-compatible client library (e.g., openai-python, langchain, llama-index)","HTTP/2 or WebSocket support for streaming"],"input_types":["text prompts (JSON payload)","images (base64-encoded or URL references)","mixed text and image sequences"],"output_types":["streaming text tokens (Server-Sent Events)","complete text response (non-streaming)","structured JSON (with usage metadata)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4-fast__cap_5","uri":"capability://image.visual.image.input.processing.with.vision.understanding","name":"image input processing with vision understanding","description":"Processes images as native inputs alongside text, enabling joint reasoning over visual and textual content. The model uses a vision encoder that converts images into token sequences, which are interleaved with text tokens in the transformer, allowing it to answer questions about images, extract information from visual content, and perform cross-modal reasoning. Supports multiple image formats and resolutions with automatic scaling to fit within the context window.","intents":["Extract text and structured data from images (OCR, form processing, document analysis)","Answer questions about image content (visual question answering)","Analyze charts, diagrams, and infographics","Process screenshots and UI mockups for accessibility or design analysis"],"best_for":["Document processing teams handling scanned PDFs and images","Developers building visual question-answering systems","Teams automating data extraction from images and screenshots"],"limitations":["Image resolution affects token consumption — high-resolution images consume more tokens within the 2M window","Vision understanding quality varies with image clarity and content complexity","No native image generation capability — vision is input-only, not output","Image processing adds latency compared to text-only inference"],"requires":["API access via OpenRouter or xAI","Valid authentication credentials","Images in supported formats (JPEG, PNG, WebP, GIF)","Base64 encoding or URL references for image transmission"],"input_types":["images (JPEG, PNG, WebP, GIF)","text prompts describing image analysis tasks","mixed text and image sequences"],"output_types":["text descriptions of image content","extracted structured data (JSON, CSV)","answers to visual questions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or xAI endpoints","Valid authentication credentials (API key)","Support for multipart/form-data or base64-encoded image payloads","Valid authentication credentials","Monitoring infrastructure to track token consumption and costs","Latency monitoring to validate sub-second response times in production","Higher token budget or cost allocation for reasoning-heavy workloads","Parsing infrastructure to extract and validate reasoning traces from outputs","API key for OpenRouter or xAI","Network connectivity to API endpoints"],"failure_modes":["2M token window is effective but not infinite — very large datasets still require batching or hierarchical processing","Image resolution and quality affect token consumption; high-resolution images consume more tokens within the window","Multimodal reasoning latency increases with context length; typical inference at 2M tokens is slower than shorter-context models","Cost efficiency may come at the expense of peak performance on specialized tasks — not guaranteed to outperform larger models on all benchmarks","Pricing advantage diminishes for very short prompts where fixed overhead dominates","Cost benefits are relative to inference volume; single-request use cases see minimal savings","Non-reasoning mode sacrifices explicit chain-of-thought transparency — outputs lack visible reasoning traces","Performance degrades on complex multi-step reasoning tasks compared to reasoning variant","Not suitable for tasks requiring verification or explanation of reasoning steps","Reasoning mode increases token consumption by 2-5x compared to non-reasoning variant, raising inference costs","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":"2026-05-24T12:16:25.059Z","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=x-ai-grok-4-fast","compare_url":"https://unfragile.ai/compare?artifact=x-ai-grok-4-fast"}},"signature":"YGW1GhhQJ68DnGt4OKkzMlwOmbKA+4sluALRVFv0ktJdYEAOqwKxacRzHQ8rmToOQQ5M+vl+WIBfKMxTviQ6Bg==","signedAt":"2026-06-20T11:06:39.350Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/x-ai-grok-4-fast","artifact":"https://unfragile.ai/x-ai-grok-4-fast","verify":"https://unfragile.ai/api/v1/verify?slug=x-ai-grok-4-fast","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"}}