{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemma-4-26b-a4b-it","slug":"google-gemma-4-26b-a4b-it","name":"Google: Gemma 4 26B A4B ","type":"model","url":"https://openrouter.ai/models/google~gemma-4-26b-a4b-it","page_url":"https://unfragile.ai/google-gemma-4-26b-a4b-it","categories":["model-training","testing-quality"],"tags":["google","api-access","text","image","video"],"pricing":{"model":"paid","free":false,"starting_price":"$6.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.token.level.inference","name":"sparse-mixture-of-experts token-level inference","description":"Implements a Mixture-of-Experts (MoE) architecture where only 3.8B parameters activate per token during inference, despite 25.2B total parameters. Uses a learned gating network to route each token to sparse expert subsets, reducing computational cost while maintaining model capacity. This sparse activation pattern is computed dynamically at inference time based on token embeddings, enabling efficient batching across multiple requests.","intents":["Deploy a 26B-parameter model with inference latency and cost comparable to 8-10B dense models","Run long-context inference on resource-constrained infrastructure without quantization","Maximize throughput on shared GPU clusters by reducing per-token compute requirements"],"best_for":["Teams deploying via API (OpenRouter) seeking cost-efficient inference","Builders optimizing for latency-sensitive applications with moderate context windows","Organizations evaluating MoE vs dense model trade-offs for production workloads"],"limitations":["MoE routing adds ~5-15ms per inference step due to gating network computation and expert selection overhead","Load balancing across experts can create uneven GPU utilization if token distribution skews toward specific experts","Fine-tuning on custom tasks may require rebalancing expert specialization, not supported via standard API"],"requires":["OpenRouter API key with Gemma 4 26B A4B model access","Minimum context window of 8K tokens (standard for this model tier)","HTTP/2 capable client for efficient streaming inference"],"input_types":["text (prompts, instructions, multi-turn conversations)"],"output_types":["text (streaming or batch completion tokens)"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_1","uri":"capability://text.generation.language.instruction.tuned.multi.turn.conversation","name":"instruction-tuned multi-turn conversation","description":"Implements instruction-following and conversational reasoning through supervised fine-tuning on high-quality instruction datasets and multi-turn dialogue examples. The model learns to parse structured prompts, follow explicit directives, and maintain coherent context across conversation turns. Supports system prompts, role-playing, and complex task decomposition within a single conversation thread.","intents":["Build chatbot applications that follow user instructions reliably across multiple conversation turns","Implement task-specific agents that parse structured requests and generate appropriate responses","Create conversational interfaces for content generation, analysis, and problem-solving workflows"],"best_for":["Developers building conversational AI products via API without fine-tuning infrastructure","Teams prototyping multi-turn dialogue systems that require instruction-following without custom training","Non-technical founders building chatbot MVPs with minimal ML infrastructure"],"limitations":["Instruction-following quality degrades on out-of-distribution tasks not represented in training data","No built-in memory persistence across separate API calls — conversation state must be managed client-side by replaying full message history","Instruction injection attacks possible if user input is not sanitized before inclusion in system prompts"],"requires":["OpenRouter API key with streaming or batch completion endpoints","Client-side conversation state management (array of message objects with role/content)","Understanding of prompt engineering best practices for reliable instruction-following"],"input_types":["text (system prompts, user messages, assistant responses for context)"],"output_types":["text (assistant responses, structured outputs if prompted)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_2","uri":"capability://text.generation.language.long.context.token.processing.with.efficient.attention","name":"long-context token processing with efficient attention","description":"Processes extended input sequences (8K+ tokens) using optimized attention mechanisms that reduce memory and compute overhead compared to standard dense attention. Likely implements grouped-query attention (GQA) or similar techniques to compress key-value cache requirements. Enables coherent reasoning and information retrieval across long documents, code files, or conversation histories without proportional latency increases.","intents":["Analyze entire source code files or documentation without splitting into chunks","Maintain coherent context across 50+ turn conversations without quality degradation","Retrieve and reason over long documents (research papers, books, legal contracts) in single inference pass"],"best_for":["Developers building code analysis or documentation Q&A systems requiring full-file context","Teams implementing long-context RAG pipelines where document chunking introduces information loss","Builders creating conversational interfaces for knowledge-intensive domains (legal, medical, research)"],"limitations":["Latency scales sub-linearly but not constantly with context length — 8K token input ~2-3x slower than 2K token input","KV cache memory consumption still grows with context length, limiting batch size on resource-constrained hardware","Long-context quality may degrade on tasks requiring precise recall of information from middle of very long sequences (>16K tokens)"],"requires":["OpenRouter API key with support for 8K+ context windows","Client capable of managing and transmitting large token payloads (typical max ~32K tokens)","Awareness of token counting for accurate cost estimation on long inputs"],"input_types":["text (long documents, code files, conversation histories, concatenated context)"],"output_types":["text (completions with awareness of full input context)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_3","uri":"capability://text.generation.language.streaming.token.generation.with.partial.output.handling","name":"streaming token generation with partial output handling","description":"Generates text tokens sequentially and streams partial outputs to clients in real-time via chunked HTTP responses or server-sent events (SSE). Each token is computed and transmitted immediately rather than buffering the full response, enabling low-latency user feedback and cancellation of long-running generations. Supports both streaming and batch completion modes via OpenRouter API.","intents":["Build real-time chat interfaces where users see text appearing character-by-character","Implement cancellable long-form content generation (articles, code) with early termination","Create interactive applications where partial outputs inform subsequent user actions"],"best_for":["Web application developers building conversational UIs with immediate user feedback","Teams implementing streaming APIs for downstream consumers (mobile apps, web frontends)","Builders optimizing perceived latency in interactive AI applications"],"limitations":["Streaming adds ~50-100ms overhead per chunk due to HTTP framing and network round-trips","Token-by-token streaming prevents certain optimizations (e.g., speculative decoding) that batch inference enables","Client-side buffering and parsing of streaming responses adds complexity; requires SSE or WebSocket handling"],"requires":["OpenRouter API key with streaming endpoint access","HTTP client library supporting streaming responses (e.g., fetch with ReadableStream, axios with responseType: 'stream')","Server-side or client-side buffering logic to handle partial tokens and reassemble complete responses"],"input_types":["text (prompts, instructions)"],"output_types":["text (streamed tokens, typically newline-delimited JSON or SSE format)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_4","uri":"capability://text.generation.language.structured.output.generation.with.schema.constraints","name":"structured output generation with schema constraints","description":"Generates text that conforms to specified JSON schemas or structured formats through prompt engineering or (if supported) constrained decoding. Enables reliable extraction of structured data (entities, relationships, classifications) from unstructured text without post-processing or regex parsing. Supports both explicit schema specification in prompts and implicit schema learning from few-shot examples.","intents":["Extract structured entities (names, dates, amounts) from documents with guaranteed JSON output","Generate API responses in exact schema format without manual parsing or validation","Implement reliable classification and tagging workflows that output structured labels"],"best_for":["Developers building data extraction pipelines that require guaranteed schema compliance","Teams implementing LLM-powered APIs that must return structured responses to downstream systems","Data engineers using LLMs for ETL tasks where schema validation is critical"],"limitations":["Schema constraints via prompt engineering are probabilistic — model may occasionally violate schema despite instructions","No native constrained decoding support confirmed for Gemma 4 26B A4B; requires post-processing validation or retries","Complex nested schemas may confuse the model; flat or moderately nested structures work most reliably"],"requires":["OpenRouter API key","Clear schema definition (JSON Schema, TypeScript interface, or natural language specification)","Client-side validation and retry logic to handle schema violations"],"input_types":["text (unstructured input, schema specification in prompt)"],"output_types":["text (JSON or structured format matching specified schema)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_5","uri":"capability://text.generation.language.multi.language.text.generation.and.understanding","name":"multi-language text generation and understanding","description":"Processes and generates text in multiple languages (English, Spanish, French, German, Chinese, Japanese, etc.) with comparable quality across languages. Trained on multilingual corpora, enabling translation, cross-lingual reasoning, and code-switching within single responses. Supports both monolingual and code-mixed inputs without explicit language specification.","intents":["Build multilingual chatbots that serve users in their preferred language without separate models","Implement translation workflows that preserve context and nuance across language pairs","Create global applications where users interact in mixed languages (e.g., English + Spanish)"],"best_for":["Teams building global applications serving non-English-speaking users","Developers implementing translation or localization pipelines","Builders creating multilingual customer support or content generation systems"],"limitations":["Quality varies by language — English and major European languages (Spanish, French, German) are strongest; lower-resource languages may have degraded performance","No explicit language detection; model infers language from context, which may fail on ambiguous inputs","Multilingual training may reduce English-specific performance compared to English-only models of equivalent size"],"requires":["OpenRouter API key","UTF-8 encoding support for non-Latin scripts","Awareness of language-specific prompt engineering (e.g., explicit language specification for ambiguous inputs)"],"input_types":["text (any language or code-mixed input)"],"output_types":["text (output in same language as input or specified target language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_6","uri":"capability://code.generation.editing.code.generation.and.technical.reasoning","name":"code generation and technical reasoning","description":"Generates syntactically correct code across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) with understanding of language-specific idioms, libraries, and best practices. Supports code completion, function generation, algorithm implementation, and debugging assistance. Trained on large code corpora, enabling context-aware suggestions that respect existing code style and patterns.","intents":["Auto-complete code functions or entire implementations from natural language descriptions","Generate boilerplate code, test cases, or documentation from existing code context","Debug code by analyzing error messages and suggesting fixes with explanations"],"best_for":["Developers using API-based code generation without IDE integration","Teams building code-to-code transformation tools or automated refactoring systems","Builders implementing AI-powered code review or documentation generation"],"limitations":["Code generation quality degrades for domain-specific languages or proprietary frameworks not well-represented in training data","No built-in syntax validation — generated code may have subtle bugs or style violations requiring human review","Context window limitations (8K tokens) constrain ability to generate code for very large files or complex multi-file refactoring"],"requires":["OpenRouter API key","Code context (existing code, error messages, or natural language specifications)","Client-side syntax validation and testing infrastructure"],"input_types":["text (code snippets, error messages, natural language specifications, comments)"],"output_types":["text (code in specified language, explanations, debugging suggestions)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_7","uri":"capability://text.generation.language.few.shot.learning.and.in.context.adaptation","name":"few-shot learning and in-context adaptation","description":"Learns task-specific behaviors from examples provided in the prompt (few-shot learning) without requiring model fine-tuning or retraining. Analyzes patterns in provided examples and applies them to new inputs, enabling rapid task adaptation. Supports 1-shot, 5-shot, and 10-shot learning scenarios within a single inference call, with quality improving as more examples are provided.","intents":["Adapt the model to custom tasks (classification, extraction, formatting) by providing 3-5 examples in the prompt","Implement zero-shot fallback with few-shot enhancement for improved accuracy without model retraining","Create task-specific behaviors dynamically at runtime based on user-provided examples"],"best_for":["Developers building flexible AI systems that adapt to user-defined tasks without retraining","Teams prototyping new use cases quickly using prompt-based adaptation","Builders implementing dynamic classification or extraction pipelines with user-customizable rules"],"limitations":["Few-shot learning quality plateaus after ~10 examples; more examples don't proportionally improve performance","Examples consume tokens from the context window, reducing space for actual input or output","Task complexity matters — few-shot works well for classification/formatting but poorly for complex reasoning tasks requiring deep domain knowledge"],"requires":["OpenRouter API key","Carefully curated examples that represent the task distribution","Understanding of prompt engineering to structure examples effectively"],"input_types":["text (examples in prompt, new input to apply examples to)"],"output_types":["text (output following patterns demonstrated in examples)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_8","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.decomposition","name":"reasoning and chain-of-thought decomposition","description":"Generates step-by-step reasoning chains that decompose complex problems into intermediate steps, improving accuracy on tasks requiring multi-step logic. Supports explicit chain-of-thought (CoT) prompting where the model generates reasoning before final answers, as well as implicit reasoning learned during instruction-tuning. Enables transparent problem-solving where intermediate steps are visible to users or downstream systems.","intents":["Solve math problems, logic puzzles, or complex reasoning tasks by generating step-by-step solutions","Improve accuracy on multi-step tasks by prompting for reasoning before final answers","Create explainable AI systems where reasoning steps justify final outputs to users"],"best_for":["Developers building educational AI systems that explain reasoning to students","Teams implementing fact-checking or verification systems that require transparent reasoning","Builders creating AI agents that must justify decisions to users or other systems"],"limitations":["Chain-of-thought reasoning increases token consumption (2-5x more tokens for reasoning + answer vs. direct answer)","Reasoning quality degrades on tasks outside the model's training distribution; hallucinated reasoning steps are common","No guarantee that reasoning steps are logically sound — model may generate plausible-sounding but incorrect reasoning"],"requires":["OpenRouter API key","Prompt engineering to explicitly request reasoning (e.g., 'Let's think step by step')","Client-side parsing of reasoning steps if they need to be extracted or validated"],"input_types":["text (problems, questions, prompts requesting reasoning)"],"output_types":["text (reasoning steps followed by final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-26b-a4b-it__cap_9","uri":"capability://tool.use.integration.api.based.inference.with.usage.tracking.and.cost.optimization","name":"api-based inference with usage tracking and cost optimization","description":"Provides access to Gemma 4 26B A4B via OpenRouter's unified API, which handles model selection, load balancing, and billing. Tracks token usage (input and output tokens separately), supports batch and streaming inference modes, and enables cost optimization through model selection and parameter tuning. Abstracts away infrastructure management, allowing developers to focus on application logic.","intents":["Access Gemma 4 26B A4B without managing GPU infrastructure or model deployment","Monitor and optimize inference costs by tracking token usage and comparing model pricing","Implement fallback logic that switches between models based on cost, latency, or availability"],"best_for":["Startups and small teams without ML infrastructure expertise or budget","Developers prototyping AI applications who need rapid iteration without deployment overhead","Teams building multi-model applications that require unified API abstraction"],"limitations":["API latency adds 100-500ms overhead compared to local inference, depending on network conditions and OpenRouter load","Pricing is per-token; high-volume applications may find local deployment more cost-effective","API rate limits and quota management required for production applications; no SLA guarantees unless on premium tier"],"requires":["OpenRouter API key (free tier available with limited usage)","HTTP client library (any language)","Understanding of token counting for accurate cost estimation"],"input_types":["text (prompts, instructions)"],"output_types":["text (completions, usage metadata including token counts and cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key with Gemma 4 26B A4B model access","Minimum context window of 8K tokens (standard for this model tier)","HTTP/2 capable client for efficient streaming inference","OpenRouter API key with streaming or batch completion endpoints","Client-side conversation state management (array of message objects with role/content)","Understanding of prompt engineering best practices for reliable instruction-following","OpenRouter API key with support for 8K+ context windows","Client capable of managing and transmitting large token payloads (typical max ~32K tokens)","Awareness of token counting for accurate cost estimation on long inputs","OpenRouter API key with streaming endpoint access"],"failure_modes":["MoE routing adds ~5-15ms per inference step due to gating network computation and expert selection overhead","Load balancing across experts can create uneven GPU utilization if token distribution skews toward specific experts","Fine-tuning on custom tasks may require rebalancing expert specialization, not supported via standard API","Instruction-following quality degrades on out-of-distribution tasks not represented in training data","No built-in memory persistence across separate API calls — conversation state must be managed client-side by replaying full message history","Instruction injection attacks possible if user input is not sanitized before inclusion in system prompts","Latency scales sub-linearly but not constantly with context length — 8K token input ~2-3x slower than 2K token input","KV cache memory consumption still grows with context length, limiting batch size on resource-constrained hardware","Long-context quality may degrade on tasks requiring precise recall of information from middle of very long sequences (>16K tokens)","Streaming adds ~50-100ms overhead per chunk due to HTTP framing and network round-trips","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"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":"2026-05-03T15:20:45.775Z","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=google-gemma-4-26b-a4b-it","compare_url":"https://unfragile.ai/compare?artifact=google-gemma-4-26b-a4b-it"}},"signature":"CfKNyrwQABK5r4CzVNIm6YATr2PtdRLpjpKALtKdE+nk/FlLvOH03W/69wFUimbCu3ghX/Rr2uiLgdDJAvrQDA==","signedAt":"2026-06-21T21:29:13.587Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemma-4-26b-a4b-it","artifact":"https://unfragile.ai/google-gemma-4-26b-a4b-it","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemma-4-26b-a4b-it","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"}}