{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"jamba","slug":"jamba","name":"Jamba","type":"model","url":"https://www.ai21.com/jamba","page_url":"https://unfragile.ai/jamba","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"jamba__cap_0","uri":"capability://text.generation.language.hybrid.transformer.mamba.long.context.inference","name":"hybrid-transformer-mamba-long-context-inference","description":"Jamba combines Transformer attention layers with Mamba State Space Model (SSM) layers in a hybrid architecture that enables efficient processing of 256K token context windows. The architecture interleaves attention and SSM layers to balance computational efficiency with semantic understanding, allowing the model to process extended documents (financial records, contracts, knowledge bases) without the quadratic memory scaling of pure Transformer models. This hybrid approach enables 'up to 30% more text per token' efficiency compared to standard tokenizers while maintaining strong performance on reasoning and generation tasks.","intents":["Process financial documents, contracts, or legal briefs exceeding 100K tokens without truncation","Build RAG systems that can ingest entire knowledge bases into context without chunking","Deploy reasoning agents that maintain full conversation history and document context simultaneously","Run long-context inference on resource-constrained hardware (edge devices, on-premises servers)"],"best_for":["Enterprise teams processing long-form documents (finance, legal, healthcare)","Builders creating agentic workflows requiring extended reasoning over full context","Organizations requiring on-device or sovereign AI deployments","Teams optimizing for inference latency and token efficiency"],"limitations":["256K token context window is hard maximum; no documented degradation behavior at maximum length","Hybrid architecture trades some pure attention-based capabilities for efficiency; specific capability gaps not documented","No benchmark data provided comparing performance vs pure Transformer models on standard tasks (MMLU, HellaSwag, etc.)","Mamba SSM layers may have different behavior on tasks requiring strict sequential dependency tracking vs Transformers"],"requires":["API access via AI21 Studio (cloud) OR Hugging Face model download for self-hosting","For cloud API: $10 free trial credits (3 months) or pay-as-you-go pricing ($0.2-$2/1M input tokens depending on variant)","For self-hosted: unknown GPU VRAM/CPU requirements not disclosed in documentation"],"input_types":["text (raw documents, conversation history, prompts)","tokenized input (up to 256K tokens)"],"output_types":["text generation (completions, responses)","structured text (JSON, markdown, code)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_1","uri":"capability://text.generation.language.on.device.compact.model.inference","name":"on-device-compact-model-inference","description":"Jamba2 3B and Jamba Mini variants are optimized for on-device deployment with 3 billion parameters, enabling inference on edge devices, mobile hardware, and resource-constrained environments without cloud API calls. The compact parameter count combined with the hybrid Mamba-Transformer architecture reduces memory footprint and latency compared to larger models, while maintaining performance on agentic workflows and reasoning tasks. Models are available as open-source downloads from Hugging Face in formats suitable for local deployment.","intents":["Deploy AI agents on edge devices or mobile applications with sub-second latency","Run inference on on-premises servers without external API dependencies","Build privacy-preserving applications where data cannot leave the device","Reduce inference costs by eliminating cloud API calls for high-volume applications"],"best_for":["Solo developers and small teams building privacy-first applications","Enterprise teams with data residency or sovereignty requirements","Mobile and edge device developers requiring low-latency inference","Organizations optimizing for cost at scale (high inference volume)"],"limitations":["Exact GPU VRAM and CPU memory requirements not disclosed; requires empirical testing","3B parameter models may have reduced capability on complex reasoning vs larger variants (Jamba Large)","No quantization format documentation (GGUF, int8, fp16); format availability unknown","Self-hosted deployment requires infrastructure management; no managed on-device deployment service provided"],"requires":["Hugging Face model download (open-source access)","Local inference framework (vLLM, Ollama, llama.cpp, or similar)","Hardware with sufficient VRAM (estimated 6-12GB based on 3B parameter count, unconfirmed)","Python 3.9+ or compatible runtime for inference framework"],"input_types":["text prompts","conversation history","document context (up to 256K tokens)"],"output_types":["text generation","agentic action sequences","structured responses"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_10","uri":"capability://automation.workflow.batch.processing.and.cost.optimization.for.high.volume.inference","name":"batch-processing-and-cost-optimization-for-high-volume-inference","description":"Jamba API supports batch processing for high-volume inference workloads, enabling cost optimization through deferred execution and bulk token pricing. Batch processing allows applications to submit multiple requests for asynchronous processing, reducing per-token costs and enabling cost-effective processing of large document collections or periodic analysis tasks. This is particularly valuable for long-context workloads where per-token costs are significant.","intents":["Process large document collections (thousands of contracts, financial reports, research papers) with optimized costs","Implement periodic batch analysis of customer feedback, support tickets, or market data","Optimize costs for non-real-time workloads by deferring execution to off-peak hours","Reduce per-token costs for high-volume inference through bulk pricing"],"best_for":["Teams with high-volume, non-real-time inference workloads","Organizations processing large document collections periodically","Applications where latency is not critical (analysis, reporting, etc.)","Cost-sensitive deployments requiring maximum efficiency"],"limitations":["Batch processing details not documented; unclear if bulk pricing discounts are available or how batch submission works","No SLA or turnaround time guarantees for batch jobs","Batch processing may not be available for all model variants; unclear which models support batching","No documentation of batch size limits, job queuing, or failure handling"],"requires":["AI21 Studio API account","Batch processing API endpoint (if available; not explicitly documented)","Application logic to submit batch jobs and poll for results","Tolerance for asynchronous processing and delayed results"],"input_types":["multiple text prompts or documents","batch job definitions (JSON format, assumed)"],"output_types":["batch results (text completions for each input)","job status and metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_11","uri":"capability://tool.use.integration.custom.enterprise.plans.with.volume.discounts.and.dedicated.support","name":"custom-enterprise-plans-with-volume-discounts-and-dedicated-support","description":"AI21 offers custom enterprise plans for large-volume deployments, including volume discounts on per-token pricing, premium rate limits, private cloud hosting, and dedicated technical support. Enterprise customers can negotiate custom SLAs, priority access to new models, and domain-specific fine-tuning. This enables organizations to optimize costs at scale and receive dedicated support for production deployments.","intents":["Negotiate volume discounts for large-scale inference deployments (millions of tokens per month)","Secure guaranteed rate limits and SLAs for production applications","Access private cloud hosting for data residency and security requirements","Obtain dedicated support and custom fine-tuning for domain-specific use cases"],"best_for":["Enterprise organizations with large-volume inference requirements","Teams requiring guaranteed SLAs and priority support","Organizations with custom deployment or security requirements","Customers needing domain-specific fine-tuning or optimization"],"limitations":["Custom plan pricing and terms not transparent; requires sales engagement","No published SLA terms or rate limit guarantees","Minimum volume requirements for custom plans not documented","Sales process may be slow; not suitable for rapid prototyping or small teams"],"requires":["Large-scale inference volume (estimated millions of tokens per month; exact threshold unknown)","Sales engagement with AI21 enterprise team","Negotiation of custom terms and SLAs"],"input_types":["volume and usage requirements","custom deployment or security requirements","domain-specific fine-tuning requests"],"output_types":["custom pricing and SLA terms","dedicated support and technical resources"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_2","uri":"capability://text.generation.language.enterprise.reasoning.with.extended.context","name":"enterprise-reasoning-with-extended-context","description":"Jamba Reasoning 3B variant is specifically tuned for complex reasoning tasks while maintaining the 256K context window, enabling multi-step logical inference over extended documents and conversation histories. The model uses chain-of-thought patterns and is optimized for 'record latency' on reasoning workloads, making it suitable for enterprise decision-making systems that require both speed and accuracy. Available via AI21 Studio API with usage-based pricing ($0.2/1M input, $0.4/1M output tokens for Mini variant).","intents":["Analyze multi-document financial reports or legal contracts with reasoning over full context","Build decision-support systems that reason through complex scenarios with extended background information","Create customer support agents that reason through ticket history and knowledge bases simultaneously","Implement compliance checking systems that apply logical rules across long documents"],"best_for":["Enterprise teams in finance, legal, and healthcare requiring reasoning over long documents","Organizations building decision-support or compliance automation systems","Teams prioritizing inference latency for real-time reasoning workflows","Builders creating multi-step agentic reasoning systems"],"limitations":["No benchmark data comparing reasoning performance vs GPT-4o, Claude 3.5, or other reasoning-optimized models","'Record latency' claim is unverified; no actual latency numbers provided","Reasoning capability improvements over base Jamba variants not documented","Tuning methodology for reasoning optimization not disclosed (RLHF, synthetic data, etc.)"],"requires":["AI21 Studio API account with active credits or pay-as-you-go billing","API key for authentication","Pricing: $0.2-$2/1M input tokens, $0.4-$8/1M output tokens depending on model variant"],"input_types":["text prompts with reasoning requirements","multi-document context (up to 256K tokens)","structured reasoning queries (JSON, markdown)"],"output_types":["reasoning chains (step-by-step logic)","structured decisions (JSON, markdown)","text explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_3","uri":"capability://text.generation.language.api.based.text.generation.with.usage.based.pricing","name":"api-based-text-generation-with-usage-based-pricing","description":"Jamba models are accessible via AI21 Studio cloud API with usage-based pay-as-you-go pricing, supporting multiple model variants (Mini, Large, Reasoning 3B) with transparent per-token costs. The API provides REST endpoints for text generation with configurable parameters (temperature, max tokens, top-p sampling) and supports batch processing for cost optimization. Pricing ranges from $0.2/1M input tokens (Mini) to $2/1M input tokens (Large), with output token pricing 2-4x higher than input.","intents":["Integrate Jamba into applications without managing infrastructure or model weights","Scale inference from prototyping to production with transparent, predictable costs","Experiment with multiple model variants (Mini, Large, Reasoning) without redeployment","Build applications requiring high availability and automatic failover (managed by AI21)"],"best_for":["Startups and small teams without ML infrastructure expertise","Applications with variable inference load (pay-per-use model preferred)","Teams requiring rapid prototyping and model switching","Organizations seeking managed cloud inference without self-hosting complexity"],"limitations":["Cloud API introduces network latency (~100-500ms round-trip) vs on-device inference","Data sent to AI21 servers; not suitable for applications with strict data residency requirements","Pricing scales linearly with token volume; high-volume applications may be cost-prohibitive vs self-hosted","Rate limits and quota management required for production deployments; custom plans needed for guaranteed throughput"],"requires":["AI21 Studio account (free signup)","API key for authentication","Free trial: $10 credits valid for 3 months","Production use: pay-as-you-go billing or custom enterprise plan","HTTP client library (curl, requests, axios, etc.)"],"input_types":["text prompts (JSON payload)","system messages and conversation history","structured parameters (temperature, max_tokens, top_p)"],"output_types":["text completions","token usage metadata (input_tokens, output_tokens)","finish_reason (stop, length, etc.)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_4","uri":"capability://text.generation.language.open.source.model.download.and.self.hosting","name":"open-source-model-download-and-self-hosting","description":"Jamba models are available as open-source downloads from Hugging Face, enabling self-hosted deployment without API dependencies or cloud costs. Models are distributed in standard formats compatible with inference frameworks (vLLM, Ollama, llama.cpp, etc.) and support both CPU and GPU inference. The open-source availability enables fine-tuning, quantization, and custom optimization for specific use cases, with no licensing restrictions documented for commercial use.","intents":["Download and deploy Jamba models on private infrastructure for data sovereignty","Fine-tune Jamba models on proprietary datasets without exposing data to cloud APIs","Quantize and optimize models for specific hardware (mobile, edge, GPU clusters)","Integrate Jamba into existing ML pipelines and frameworks without vendor lock-in"],"best_for":["Enterprise teams with data residency or sovereignty requirements","Researchers and ML engineers requiring model customization and fine-tuning","Organizations building proprietary applications requiring model ownership","Teams with existing ML infrastructure (Kubernetes, GPU clusters, etc.)"],"limitations":["Self-hosting requires infrastructure management, monitoring, and scaling (no managed service)","GPU VRAM and CPU memory requirements not documented; requires empirical testing and capacity planning","No quantization format documentation (GGUF, int8, fp16); format availability and performance trade-offs unknown","Fine-tuning methodology, training data requirements, and convergence behavior not documented","License terms not explicitly stated in source material; assumed permissive based on 'open foundation models' description"],"requires":["Hugging Face account (free signup) to download models","Local inference framework (vLLM, Ollama, llama.cpp, Text Generation WebUI, or similar)","GPU with sufficient VRAM (estimated 6-24GB depending on variant; unconfirmed)","Python 3.9+ or compatible runtime","Storage for model weights (estimated 6-50GB depending on variant)"],"input_types":["text prompts","conversation history","document context (up to 256K tokens)"],"output_types":["text generation","token logits and probabilities (for custom sampling)","structured outputs (JSON, markdown)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_5","uri":"capability://text.generation.language.multi.variant.model.selection.for.cost.performance.tradeoff","name":"multi-variant-model-selection-for-cost-performance-tradeoff","description":"Jamba offers multiple model variants (Mini, Large, Reasoning 3B, 2 3B) optimized for different cost-performance tradeoffs, enabling builders to select the appropriate model for their use case without over-provisioning. Mini variants prioritize efficiency and cost ($0.2/1M input tokens), while Large variants provide maximum capability ($2/1M input tokens), and Reasoning 3B targets reasoning workloads. All variants share the 256K context window and hybrid architecture, allowing seamless switching based on workload requirements.","intents":["Select the most cost-effective model variant for a given task (e.g., Mini for simple classification, Large for complex reasoning)","Implement dynamic model routing based on request complexity or user tier","Prototype with Mini variant and upgrade to Large only for production workloads requiring higher quality","Optimize inference costs by matching model capability to task requirements"],"best_for":["Teams with variable workload complexity requiring cost optimization","Builders implementing tiered service offerings (free tier with Mini, premium with Large)","Organizations scaling from prototype to production with cost constraints","ML engineers optimizing inference cost-per-quality metrics"],"limitations":["Performance differences between variants not documented; no benchmark comparisons provided","No guidance on which variant to use for specific task types (classification, summarization, reasoning, etc.)","Switching between variants requires code changes or dynamic routing logic; no automatic fallback mechanism","Pricing differences (10x between Mini and Large) may create incentive misalignment if model quality varies significantly"],"requires":["AI21 Studio API account with understanding of pricing structure","Application logic to select appropriate variant based on task or user tier","Monitoring and cost tracking to validate cost-performance tradeoffs"],"input_types":["text prompts","task metadata (complexity, user tier, etc.)","context documents (up to 256K tokens)"],"output_types":["text generation","model selection metadata (which variant was used)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_6","uri":"capability://text.generation.language.domain.specific.optimization.for.enterprise.verticals","name":"domain-specific-optimization-for-enterprise-verticals","description":"Jamba is optimized for enterprise verticals including finance, healthcare, defense, technology, and manufacturing, with specific tuning for domain-specific tasks like financial analysis, contract review, and compliance checking. The 256K context window and reasoning capabilities enable processing of domain-specific documents (financial reports, medical records, contracts) without truncation. AI21 offers custom enterprise plans with domain-specific fine-tuning and dedicated support for vertical-specific deployments.","intents":["Analyze financial documents (earnings reports, SEC filings, contracts) with full context and domain-specific reasoning","Build healthcare applications processing medical records, clinical notes, and research papers with privacy controls","Implement compliance and regulatory systems checking documents against domain-specific rules","Deploy defense/sovereign AI applications with on-premises deployment and data residency guarantees"],"best_for":["Enterprise teams in regulated verticals (finance, healthcare, defense) requiring domain-specific optimization","Organizations with high-volume document processing in specific domains","Teams requiring custom fine-tuning on proprietary domain datasets","Builders implementing compliance automation or regulatory systems"],"limitations":["Domain-specific optimization details not documented; unclear what tuning was applied to each vertical","No benchmark data comparing domain-specific performance vs general-purpose models","Custom enterprise plans require sales engagement; pricing and terms not transparent","No published case studies or examples of domain-specific deployments"],"requires":["For standard deployment: AI21 Studio API account","For custom domain optimization: enterprise plan negotiation with AI21 sales team","Domain-specific knowledge to validate model outputs and define success metrics"],"input_types":["domain-specific documents (financial reports, medical records, contracts, etc.)","domain-specific prompts and queries","structured domain data (JSON, CSV)"],"output_types":["domain-specific analysis and insights","structured domain outputs (compliance reports, financial summaries, etc.)","domain-specific reasoning chains"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_7","uri":"capability://text.generation.language.sovereign.ai.and.on.premises.deployment","name":"sovereign-ai-and-on-premises-deployment","description":"Jamba supports on-premises and sovereign AI deployment for organizations with data residency, security, or geopolitical requirements. Models are available as open-source downloads for self-hosting, and AI21 offers custom enterprise plans with private cloud hosting, dedicated infrastructure, and compliance certifications. This enables organizations to maintain full data control and meet regulatory requirements (GDPR, HIPAA, national security) without sending data to external cloud providers.","intents":["Deploy AI systems in regulated environments (healthcare, finance, defense) with data residency guarantees","Build sovereign AI applications for government or defense contractors with classified data handling","Implement GDPR-compliant systems that process EU citizen data without cross-border transfer","Maintain full data control and audit trails for compliance and security requirements"],"best_for":["Government agencies and defense contractors requiring sovereign AI capabilities","Healthcare and financial institutions with strict data residency requirements","Organizations in regulated jurisdictions (EU, China, Russia) with data localization laws","Enterprise teams prioritizing data control and security over cloud convenience"],"limitations":["On-premises deployment requires significant infrastructure investment and operational overhead","Private cloud hosting and custom enterprise plans require sales engagement; pricing not transparent","No documentation of compliance certifications (SOC 2, ISO 27001, FedRAMP, etc.)","Self-hosted deployment requires security hardening, monitoring, and incident response capabilities","No SLA or uptime guarantees for self-hosted deployments"],"requires":["For self-hosted: on-premises infrastructure (GPU cluster, storage, networking)","For private cloud: enterprise plan with AI21 (requires sales engagement)","Security infrastructure: firewalls, VPNs, access controls, audit logging","Compliance expertise to validate deployment meets regulatory requirements"],"input_types":["sensitive data (healthcare records, financial data, classified information)","prompts and queries","context documents"],"output_types":["analysis and insights","audit logs and compliance records","structured outputs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_8","uri":"capability://data.processing.analysis.efficient.tokenization.with.30.percent.text.density.improvement","name":"efficient-tokenization-with-30-percent-text-density-improvement","description":"Jamba achieves 'up to 30% more text per token' efficiency compared to standard tokenizers through optimized tokenization, reducing the number of tokens required to represent the same text. This efficiency gain directly reduces API costs (fewer tokens billed) and increases effective context window capacity (more text fits within 256K token limit). The tokenization improvement applies across all model variants and deployment methods (API and self-hosted).","intents":["Reduce API costs by 20-30% for long-document processing through improved tokenization efficiency","Fit more text into the 256K context window (effective context becomes ~330K tokens worth of text)","Process longer documents without truncation or chunking","Optimize token usage for cost-sensitive applications with high inference volume"],"best_for":["Teams processing long documents with high inference volume (cost-sensitive)","Applications requiring maximum effective context window (RAG systems, document analysis)","Organizations optimizing for token efficiency and cost-per-query metrics","Builders implementing document chunking strategies that can now use larger chunks"],"limitations":["30% efficiency improvement is a marketing claim; no independent verification or benchmark data provided","Tokenization efficiency may vary by language, domain, or text type; no breakdown provided","Efficiency gain only applies to Jamba; switching from other models requires re-evaluation of token counts","No documentation of tokenization algorithm or comparison methodology vs 'standard tokenizers'"],"requires":["Use of Jamba models (any variant, any deployment method)","Understanding that token counts will differ from other models; requires cost recalculation"],"input_types":["text documents of any length (up to 256K tokens)","conversation history","code snippets"],"output_types":["token count estimates (fewer tokens than competing models)","cost estimates (lower per-document costs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__cap_9","uri":"capability://planning.reasoning.agentic.workflow.support.with.tool.integration","name":"agentic-workflow-support-with-tool-integration","description":"Jamba2 3B is specifically optimized for agentic workflows, enabling the model to plan multi-step tasks, call external tools, and maintain state across interactions. The model supports function calling and tool integration patterns required for autonomous agents, with the compact 3B parameter size enabling on-device agent deployment. The 256K context window allows agents to maintain full conversation history and tool execution logs without truncation.","intents":["Build autonomous agents that plan multi-step tasks and call external tools (APIs, databases, search engines)","Deploy agents on edge devices or mobile applications with full autonomy","Create conversational agents that maintain full interaction history and tool execution context","Implement agents that reason over extended tool output and documentation"],"best_for":["Builders creating autonomous agents for customer support, research, or task automation","Teams deploying agents on edge devices or mobile platforms","Organizations requiring agents with full context awareness and reasoning","Developers implementing multi-step workflows with tool orchestration"],"limitations":["Agentic optimization details not documented; unclear what tuning enables agentic capabilities","No benchmark data comparing agentic performance vs other models optimized for agents (e.g., GPT-4 with function calling)","Tool integration methodology not specified; unclear if native function calling or prompt-based tool use","No examples or documentation of agentic workflows or tool schemas"],"requires":["Jamba2 3B model (via API or self-hosted)","Agent framework (LangChain, AutoGen, CrewAI, or custom implementation)","Tool definitions and integration logic (function schemas, API endpoints, etc.)","State management for multi-turn interactions"],"input_types":["user queries and instructions","tool definitions (function schemas, descriptions)","tool execution results and feedback","conversation history (up to 256K tokens)"],"output_types":["tool calls (function names, arguments)","reasoning chains (step-by-step planning)","final responses to user queries"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jamba__headline","uri":"capability://model.training.ai.model.for.long.context.document.processing","name":"ai model for long-context document processing","description":"Jamba is an advanced AI model that combines Transformer and Mamba SSM architectures to efficiently process documents with a massive 256K context window, ideal for enterprise applications.","intents":["best AI model for long documents","AI model for enterprise document processing","top AI model with large context window","AI model for finance and healthcare","best AI model for extended reasoning tasks"],"best_for":["enterprise applications","long-context processing"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["API access via AI21 Studio (cloud) OR Hugging Face model download for self-hosting","For cloud API: $10 free trial credits (3 months) or pay-as-you-go pricing ($0.2-$2/1M input tokens depending on variant)","For self-hosted: unknown GPU VRAM/CPU requirements not disclosed in documentation","Hugging Face model download (open-source access)","Local inference framework (vLLM, Ollama, llama.cpp, or similar)","Hardware with sufficient VRAM (estimated 6-12GB based on 3B parameter count, unconfirmed)","Python 3.9+ or compatible runtime for inference framework","AI21 Studio API account","Batch processing API endpoint (if available; not explicitly documented)","Application logic to submit batch jobs and poll for results"],"failure_modes":["256K token context window is hard maximum; no documented degradation behavior at maximum length","Hybrid architecture trades some pure attention-based capabilities for efficiency; specific capability gaps not documented","No benchmark data provided comparing performance vs pure Transformer models on standard tasks (MMLU, HellaSwag, etc.)","Mamba SSM layers may have different behavior on tasks requiring strict sequential dependency tracking vs Transformers","Exact GPU VRAM and CPU memory requirements not disclosed; requires empirical testing","3B parameter models may have reduced capability on complex reasoning vs larger variants (Jamba Large)","No quantization format documentation (GGUF, int8, fp16); format availability unknown","Self-hosted deployment requires infrastructure management; no managed on-device deployment service provided","Batch processing details not documented; unclear if bulk pricing discounts are available or how batch submission works","No SLA or turnaround time guarantees for batch jobs","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":0.9,"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":"2026-05-24T12:16:23.327Z","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=jamba","compare_url":"https://unfragile.ai/compare?artifact=jamba"}},"signature":"VhwOD04s6AYJ4TTVDfdNksyYmXy3nwH0c3g+Rt02Gx0eEjyZzHn8gh+zZA9K5fizu7tJ4UZyOwOcE8wngSJJBg==","signedAt":"2026-06-15T06:29:54.493Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jamba","artifact":"https://unfragile.ai/jamba","verify":"https://unfragile.ai/api/v1/verify?slug=jamba","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"}}