{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-bytedance-seed-seed-1.6-flash","slug":"bytedance-seed-seed-1.6-flash","name":"ByteDance Seed: Seed 1.6 Flash","type":"model","url":"https://openrouter.ai/models/bytedance-seed~seed-1.6-flash","page_url":"https://unfragile.ai/bytedance-seed-seed-1.6-flash","categories":["image-generation"],"tags":["bytedance-seed","api-access","text","image","video"],"pricing":{"model":"paid","free":false,"starting_price":"$7.50e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_0","uri":"capability://planning.reasoning.multimodal.deep.thinking.inference.with.extended.context","name":"multimodal deep thinking inference with extended context","description":"Processes text and visual inputs (images, video frames) through a unified transformer architecture optimized for reasoning tasks, leveraging a 256k token context window to maintain coherence across long documents, multi-turn conversations, and complex visual scenes. The model uses a deep thinking approach that allocates computational budget to reasoning steps before generating outputs, enabling more accurate analysis of nuanced queries.","intents":["I need to analyze a complex document with embedded images and get detailed reasoning about relationships between text and visual elements","I want to process long video transcripts with frame-by-frame visual context to understand narrative flow and visual-semantic alignment","I need to reason through multi-step problems that require both textual analysis and visual pattern recognition across extended contexts"],"best_for":["AI researchers and engineers building reasoning-heavy applications requiring visual grounding","Document analysis teams processing PDFs with mixed text and image content at scale","Video understanding platforms needing frame-accurate semantic analysis with long-form context"],"limitations":["Deep thinking approach adds latency compared to standard inference — suitable for batch/async workflows, not real-time chat","256k context window still insufficient for full-length feature films or massive document collections; requires chunking strategies","Visual input resolution and format constraints not publicly documented — may require preprocessing for non-standard image dimensions","Reasoning depth is fixed per model version; cannot dynamically adjust compute allocation per query"],"requires":["API key for ByteDance Seed or OpenRouter proxy","HTTP/2 client supporting streaming responses for long-form outputs","Image preprocessing pipeline for format normalization (JPEG, PNG, WebP support assumed)"],"input_types":["text (UTF-8, up to 256k tokens)","image (JPEG, PNG, WebP — specific resolution limits unknown)","video frames (as sequential image inputs)"],"output_types":["text (reasoning chains + final answers)","structured reasoning traces (if requested via prompt engineering)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_1","uri":"capability://text.generation.language.ultra.low.latency.text.generation.for.streaming.applications","name":"ultra-low-latency text generation for streaming applications","description":"Optimized inference serving with 'Flash' variant tuning for minimal time-to-first-token and per-token latency, enabling real-time streaming responses suitable for conversational interfaces. Uses quantization, KV-cache optimization, and likely batching strategies to reduce memory footprint while maintaining reasoning quality, making it deployable on resource-constrained inference infrastructure.","intents":["I need a reasoning model that can power interactive chat without noticeable delays between user input and first response token","I want to stream complex reasoning outputs to users in real-time without buffering entire responses","I need to run high-throughput inference on limited GPU memory while preserving model quality"],"best_for":["Startups building consumer-facing AI chat products with strict latency budgets (<500ms TTFT)","Teams deploying reasoning models on edge devices or cost-constrained cloud infrastructure","Platforms requiring high concurrent user throughput with per-user reasoning capabilities"],"limitations":["Flash optimization may reduce reasoning depth compared to full Seed 1.6 — trade-off between speed and accuracy not publicly quantified","Streaming output requires client-side buffering and token reassembly; no built-in retry logic for dropped connections","Latency improvements are relative to baseline; absolute numbers depend on inference hardware and batch size","Quantization may impact performance on edge-case reasoning tasks requiring high numerical precision"],"requires":["OpenRouter API key or direct ByteDance Seed API access","Client library supporting Server-Sent Events (SSE) or WebSocket streaming","Network connection with <100ms latency for optimal streaming experience"],"input_types":["text (streaming or batch)","image (single or multi-frame)"],"output_types":["text tokens (streamed via SSE or WebSocket)","partial reasoning traces (if model exposes intermediate steps)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_2","uri":"capability://image.visual.visual.question.answering.with.reasoning.chains","name":"visual question answering with reasoning chains","description":"Analyzes images and video frames by combining visual feature extraction with language understanding to answer complex questions about visual content, generating step-by-step reasoning that explains how visual elements support the answer. The model integrates visual grounding (identifying regions relevant to the question) with semantic reasoning, enabling accurate responses to questions requiring both object detection and contextual understanding.","intents":["I need to extract detailed information from screenshots, diagrams, or charts with natural language questions","I want to verify visual content authenticity or detect inconsistencies by asking the model to reason about what it sees","I need to caption or describe video frames with context-aware reasoning about temporal relationships"],"best_for":["Content moderation teams analyzing images and videos for policy violations with reasoning transparency","Accessibility teams generating detailed alt-text and descriptions for visual content","Research teams analyzing scientific figures, charts, and experimental imagery with interpretable reasoning"],"limitations":["Visual reasoning quality degrades on low-resolution or heavily compressed images; minimum resolution requirements not specified","Cannot perform pixel-level editing or manipulation — analysis-only capability","Reasoning chains may be verbose for simple questions; no built-in brevity control","Hallucination risk remains for ambiguous or adversarially-crafted images; no confidence scoring provided"],"requires":["Image in supported format (JPEG, PNG, WebP)","Structured prompt engineering to elicit reasoning chains (e.g., 'Explain step-by-step...')","API access via OpenRouter or ByteDance Seed"],"input_types":["image (JPEG, PNG, WebP)","text (natural language question or instruction)"],"output_types":["text (answer + reasoning chain)","structured JSON (if prompt-engineered for extraction)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_3","uri":"capability://data.processing.analysis.long.document.semantic.understanding.with.visual.references","name":"long-document semantic understanding with visual references","description":"Processes documents up to 256k tokens that mix text and embedded images (PDFs, scanned documents, multi-page reports) by maintaining coherent semantic understanding across the entire document while grounding analysis in visual elements. Uses hierarchical attention and cross-modal fusion to track concepts across pages and correlate textual references with visual illustrations, enabling accurate extraction and reasoning over complex, lengthy documents.","intents":["I need to extract key information from a 100+ page PDF with charts, tables, and diagrams without losing context","I want to answer questions about relationships between text sections and visual elements across a long document","I need to summarize a multi-page report while preserving visual context and cross-references"],"best_for":["Legal and compliance teams analyzing lengthy contracts and regulatory documents with embedded exhibits","Academic researchers processing full research papers with figures and supplementary materials","Enterprise document processing teams handling annual reports, technical specifications, and multi-page proposals"],"limitations":["256k token limit still constrains very large documents (e.g., full books); requires intelligent chunking or summarization pre-processing","PDF parsing must be handled externally — model expects pre-extracted text + images, not raw PDF files","Visual element positioning information (page numbers, coordinates) not automatically preserved in output","Reasoning latency scales with document length; batch processing recommended for high-volume workflows"],"requires":["PDF extraction pipeline (e.g., PyPDF2, pdfplumber) to convert PDFs to text + image sequences","Token counting utility to ensure document fits within 256k limit","Structured prompts for document-level tasks (e.g., 'Summarize the key findings from this report')"],"input_types":["text (extracted from PDF, up to 256k tokens)","image (extracted from PDF pages, in sequence)"],"output_types":["text (summaries, extracted information, answers)","structured data (JSON with extracted fields, if prompt-engineered)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_4","uri":"capability://automation.workflow.batch.inference.with.cost.optimization","name":"batch inference with cost optimization","description":"Supports asynchronous batch processing of multiple requests through OpenRouter's batch API, enabling cost-per-token reductions (typically 50% discount) by deferring execution to off-peak hours and consolidating inference across requests. Batching is transparent to the application layer — requests are queued and processed in groups, with results returned via callback or polling.","intents":["I need to process thousands of documents or images for analysis but can tolerate 1-24 hour latency","I want to reduce API costs for non-real-time reasoning tasks by leveraging batch discounts","I need to analyze large datasets with reasoning without overwhelming my inference budget"],"best_for":["Data science teams processing large corpora for research or analytics","Content platforms analyzing user-generated content in bulk","Enterprise teams running nightly/weekly analysis jobs with flexible deadlines"],"limitations":["Batch processing introduces 1-24 hour latency — unsuitable for real-time applications","Batch API requires explicit request formatting and polling/callback handling; adds complexity vs. synchronous API","Cost savings are provider-dependent (OpenRouter may offer different discounts than direct ByteDance API)","No priority queuing — all batch requests processed in FIFO order"],"requires":["OpenRouter API key with batch processing enabled","Batch request formatting (JSONL format with specific schema)","Polling mechanism or webhook endpoint for result retrieval"],"input_types":["text (up to 256k tokens per request)","image (per request)"],"output_types":["text (reasoning outputs, answers)","structured data (JSON, if requested)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-bytedance-seed-seed-1.6-flash__cap_5","uri":"capability://image.visual.video.frame.by.frame.semantic.analysis.with.temporal.reasoning","name":"video frame-by-frame semantic analysis with temporal reasoning","description":"Processes video by extracting and analyzing individual frames sequentially while maintaining temporal context across frames, enabling the model to reason about motion, scene transitions, and narrative progression. The 256k context window allows processing dozens of frames with full reasoning chains, tracking object states and relationships across time without losing coherence.","intents":["I need to analyze a video clip and describe what happens across multiple scenes with temporal reasoning","I want to detect anomalies or changes in video content by comparing frame states across time","I need to generate detailed captions or summaries of video content that account for temporal relationships"],"best_for":["Video content moderation teams analyzing user-generated videos for policy violations with temporal context","Security teams analyzing surveillance footage for anomaly detection","Media companies generating video summaries and metadata with temporal accuracy"],"limitations":["Video must be pre-processed into frames externally — model does not accept raw video files","Frame sampling strategy (every Nth frame) must be chosen by application; no built-in adaptive sampling","Temporal reasoning quality degrades with sparse frame sampling; dense sampling quickly exhausts 256k token budget","No built-in motion estimation or optical flow — reasoning is based on visual appearance changes only"],"requires":["Video extraction pipeline (e.g., OpenCV, ffmpeg) to extract frames at chosen sampling rate","Frame preprocessing (resizing, format conversion) to match model input specifications","Temporal context prompting (e.g., 'Analyze these frames in sequence and describe what happens')"],"input_types":["image (video frames in sequence, JPEG/PNG/WebP)","text (temporal context instructions)"],"output_types":["text (temporal analysis, scene descriptions, anomaly reports)","structured data (frame-level annotations, if prompt-engineered)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["API key for ByteDance Seed or OpenRouter proxy","HTTP/2 client supporting streaming responses for long-form outputs","Image preprocessing pipeline for format normalization (JPEG, PNG, WebP support assumed)","OpenRouter API key or direct ByteDance Seed API access","Client library supporting Server-Sent Events (SSE) or WebSocket streaming","Network connection with <100ms latency for optimal streaming experience","Image in supported format (JPEG, PNG, WebP)","Structured prompt engineering to elicit reasoning chains (e.g., 'Explain step-by-step...')","API access via OpenRouter or ByteDance Seed","PDF extraction pipeline (e.g., PyPDF2, pdfplumber) to convert PDFs to text + image sequences"],"failure_modes":["Deep thinking approach adds latency compared to standard inference — suitable for batch/async workflows, not real-time chat","256k context window still insufficient for full-length feature films or massive document collections; requires chunking strategies","Visual input resolution and format constraints not publicly documented — may require preprocessing for non-standard image dimensions","Reasoning depth is fixed per model version; cannot dynamically adjust compute allocation per query","Flash optimization may reduce reasoning depth compared to full Seed 1.6 — trade-off between speed and accuracy not publicly quantified","Streaming output requires client-side buffering and token reassembly; no built-in retry logic for dropped connections","Latency improvements are relative to baseline; absolute numbers depend on inference hardware and batch size","Quantization may impact performance on edge-case reasoning tasks requiring high numerical precision","Visual reasoning quality degrades on low-resolution or heavily compressed images; minimum resolution requirements not specified","Cannot perform pixel-level editing or manipulation — analysis-only capability","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"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":"2026-05-24T12:16:24.484Z","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=bytedance-seed-seed-1.6-flash","compare_url":"https://unfragile.ai/compare?artifact=bytedance-seed-seed-1.6-flash"}},"signature":"DLDCuFk/KZ9EK5r17aMBtaInvJMPf5h7KB/vsCkIt/VxELr4Jut7HXVEvBjkmbu2Io9EX6W8D6/DDBYBO98HBQ==","signedAt":"2026-06-22T08:33:50.661Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bytedance-seed-seed-1.6-flash","artifact":"https://unfragile.ai/bytedance-seed-seed-1.6-flash","verify":"https://unfragile.ai/api/v1/verify?slug=bytedance-seed-seed-1.6-flash","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"}}