{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-baidu-ernie-4.5-300b-a47b","slug":"baidu-ernie-4.5-300b-a47b","name":"Baidu: ERNIE 4.5 300B A47B ","type":"model","url":"https://openrouter.ai/models/baidu~ernie-4.5-300b-a47b","page_url":"https://unfragile.ai/baidu-ernie-4.5-300b-a47b","categories":["llm-apis"],"tags":["baidu","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.80e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_0","uri":"capability://text.generation.language.mixture.of.experts.text.generation.with.selective.parameter.activation","name":"mixture-of-experts text generation with selective parameter activation","description":"ERNIE-4.5-300B-A47B implements a Mixture-of-Experts (MoE) architecture where only 47B out of 300B total parameters are activated per token, reducing computational overhead while maintaining model capacity. The model uses a gating network to route tokens to specialized expert modules, enabling efficient inference through sparse activation patterns rather than dense forward passes through all parameters.","intents":["Generate coherent multi-turn conversations with reduced latency compared to dense 300B models","Process long-context documents while maintaining reasonable token throughput and cost efficiency","Build production chatbots that require high-quality reasoning without proportional compute scaling"],"best_for":["Teams deploying conversational AI at scale seeking cost-efficiency without quality degradation","Developers building multi-turn dialogue systems requiring sub-second response times","Organizations migrating from smaller models (70B-100B) needing capability uplift with controlled inference costs"],"limitations":["MoE routing adds ~15-25ms latency overhead per token due to gating network computation","Expert imbalance during training can cause load skew — some experts may be underutilized, reducing effective parameter efficiency","Sparse activation patterns may produce inconsistent outputs for edge-case prompts where expert selection diverges across runs","No native support for dynamic expert pruning or fine-tuning individual experts without full model retraining"],"requires":["OpenRouter API key or direct Baidu API credentials","HTTP/2 capable client library (async recommended for production throughput)","Context window management for prompts exceeding 4K-8K tokens (exact limit not specified in artifact)"],"input_types":["text (UTF-8 encoded natural language)","code snippets (for instruction-following tasks)","structured prompts with system/user/assistant roles"],"output_types":["text (streaming or batch completion)","structured JSON (via prompt engineering or function calling if supported)","multi-turn conversation continuations"],"categories":["text-generation-language","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management.with.role.based.message.handling","name":"multi-turn conversational context management with role-based message handling","description":"ERNIE-4.5-300B-A47B processes conversation history through explicit system/user/assistant message roles, maintaining coherent context across multiple exchanges without requiring manual context window management. The model implements sliding-window attention or similar context compression to handle extended dialogues while respecting token limits, enabling stateless API calls where conversation state is passed in each request.","intents":["Build chatbot applications that maintain conversation coherence across 10+ user turns without losing context","Implement role-based prompt injection safeguards by separating system instructions from user input","Create multi-agent dialogue systems where different roles (user, assistant, system) have distinct behavioral constraints"],"best_for":["Developers building conversational interfaces (Discord bots, Slack integrations, web chat widgets)","Teams implementing customer support automation requiring context retention across sessions","Researchers prototyping dialogue systems with explicit role separation for bias/safety analysis"],"limitations":["Context window is finite — conversations exceeding ~4K-8K tokens require manual summarization or truncation","No native conversation persistence — each API call must include full history, increasing payload size and latency for long conversations","Role-based message handling may not generalize to non-English languages with different grammatical role structures","No built-in conversation branching or hypothetical reasoning ('what if' scenarios require explicit prompt engineering)"],"requires":["OpenRouter API key or Baidu API credentials","Client-side conversation state management (array of message objects with role/content fields)","Understanding of token counting for the model to estimate context window usage"],"input_types":["JSON message objects with 'role' (system/user/assistant) and 'content' (text) fields","Streaming or batch conversation histories"],"output_types":["text completion (next assistant message)","streaming token-by-token output for real-time UI updates"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_2","uri":"capability://text.generation.language.instruction.following.and.task.specific.prompt.adaptation","name":"instruction-following and task-specific prompt adaptation","description":"ERNIE-4.5-300B-A47B is trained on instruction-following datasets enabling it to interpret natural language task descriptions and adapt behavior accordingly. The model uses in-context learning to follow complex multi-step instructions, system prompts for behavioral constraints, and few-shot examples to guide output format — all without fine-tuning, leveraging the model's learned ability to parse and execute arbitrary instructions.","intents":["Execute complex reasoning tasks (math, logic, code generation) by providing step-by-step instructions in natural language","Adapt model behavior for domain-specific tasks (legal document analysis, medical summarization) via system prompts without retraining","Generate structured outputs (JSON, CSV, code) by instructing the model on desired format in the prompt"],"best_for":["Developers building general-purpose AI assistants requiring flexible task adaptation","Non-technical users creating custom workflows via prompt engineering without ML expertise","Teams prototyping new use cases rapidly before committing to fine-tuning or specialized models"],"limitations":["Instruction-following quality degrades with ambiguous or contradictory prompts — no built-in conflict resolution","Complex multi-step instructions may require explicit chain-of-thought prompting to achieve reliable execution","Output format adherence is probabilistic — structured output (JSON, code) may be malformed without strict parsing constraints","No native support for instruction-based fine-tuning — all adaptation occurs via prompt engineering, limiting specialization depth"],"requires":["Skill in prompt engineering to craft clear, unambiguous instructions","Understanding of few-shot learning patterns to provide effective examples","Output validation/parsing logic on the client side to handle format inconsistencies"],"input_types":["natural language instructions (system prompts, task descriptions)","few-shot examples (input-output pairs demonstrating desired behavior)","structured task specifications (JSON, YAML describing task parameters)"],"output_types":["text (task-specific responses)","code (Python, JavaScript, SQL, etc. via code generation instructions)","structured data (JSON, CSV, markdown tables via format instructions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.with.language.agnostic.token.routing","name":"multilingual text generation with language-agnostic token routing","description":"ERNIE-4.5-300B-A47B supports text generation across multiple languages (Chinese, English, and others) through language-agnostic MoE routing where the gating network treats tokens uniformly regardless of language, allowing the model to leverage shared expert knowledge across linguistic boundaries. The model was trained on multilingual corpora, enabling code-switching and cross-lingual reasoning without language-specific model variants.","intents":["Generate coherent responses in non-English languages (Chinese, Spanish, etc.) with quality comparable to English","Handle code-switching scenarios where users mix languages within a single prompt","Build global applications supporting multiple languages from a single model endpoint"],"best_for":["Teams building international products requiring multilingual support without model duplication","Developers serving Chinese-speaking markets where Baidu models may have regional advantages","Organizations needing cost-effective multilingual inference without maintaining separate language-specific models"],"limitations":["Multilingual training may dilute performance in any single language compared to language-specific models","Language detection is implicit — the model may misidentify language intent if prompts are ambiguous, leading to code-switching artifacts","Non-Latin scripts (Chinese, Arabic, etc.) may have different token efficiency, affecting cost predictability across languages","No explicit language preference mechanism — cannot force output in a specific language if the model infers a different language from context"],"requires":["UTF-8 encoding support for non-Latin scripts","Awareness of token counting differences across languages (Chinese typically requires more tokens per semantic unit than English)","Optional: language detection preprocessing if strict language enforcement is required"],"input_types":["text in any supported language (Chinese, English, etc.)","code-switched prompts mixing multiple languages","language-tagged prompts (e.g., '[Chinese]' prefix) for explicit language hints"],"output_types":["text in the inferred or specified language","code-switched responses reflecting input language mixing"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.completion.modes","name":"api-based inference with streaming and batch completion modes","description":"ERNIE-4.5-300B-A47B is accessed exclusively via OpenRouter or Baidu's API, supporting both streaming (token-by-token output for real-time UI) and batch (full completion returned at once) inference modes. The API abstracts away model deployment complexity, handling load balancing, rate limiting, and multi-user concurrency server-side, while clients manage request formatting and response parsing.","intents":["Integrate ERNIE-4.5 into web applications with real-time streaming UI updates without managing GPU infrastructure","Process batch inference jobs (e.g., summarizing 1000 documents) via API without provisioning dedicated compute","Build multi-tenant SaaS applications where API rate limiting and usage tracking are handled by the provider"],"best_for":["Startups and small teams avoiding infrastructure overhead by using managed APIs","Developers prototyping AI features quickly without ML ops expertise","Organizations in regions where Baidu APIs have lower latency or better compliance (China, Asia-Pacific)"],"limitations":["API latency is unpredictable — depends on provider load, network conditions, and request queuing (typically 500ms-2s per request)","Streaming mode requires persistent HTTP/2 connections, incompatible with some legacy proxies or firewalls","No local inference option — all requests traverse the internet, raising privacy concerns for sensitive data","Rate limiting and quota management are provider-controlled — no fine-grained control over throughput or priority","Vendor lock-in — switching to alternative models requires code changes to request/response parsing"],"requires":["API key from OpenRouter or Baidu (paid subscription required)","HTTP client library supporting streaming (e.g., requests, httpx, fetch API)","Network connectivity and firewall rules allowing outbound HTTPS to OpenRouter/Baidu endpoints","Error handling for rate limits (429), timeouts, and service unavailability"],"input_types":["JSON request bodies with messages array and optional parameters (temperature, max_tokens, etc.)","HTTP headers with Authorization token"],"output_types":["streaming: Server-Sent Events (SSE) with JSON chunks containing partial tokens","batch: JSON response with complete completion text and usage metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_5","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control.for.output.diversity","name":"temperature and sampling parameter control for output diversity","description":"ERNIE-4.5-300B-A47B exposes temperature, top-p (nucleus sampling), and top-k parameters allowing fine-grained control over output randomness and diversity. Lower temperatures (0.0-0.5) produce deterministic, focused outputs suitable for factual tasks; higher temperatures (0.7-1.0+) increase creativity and diversity for open-ended generation. The model implements standard softmax temperature scaling and nucleus sampling, enabling developers to tune the probability distribution over tokens without retraining.","intents":["Generate deterministic outputs for factual tasks (Q&A, summarization) by setting low temperature","Create diverse creative content (brainstorming, storytelling) by increasing temperature and top-p","Balance consistency and novelty for domain-specific applications (customer support vs. creative writing)"],"best_for":["Developers building applications requiring tunable output behavior without model switching","Teams A/B testing different temperature settings to optimize user satisfaction metrics","Researchers studying the relationship between sampling parameters and output quality"],"limitations":["Temperature scaling is applied uniformly across all tokens — no per-token or per-layer control","Extreme temperatures (>1.5) may produce incoherent outputs or repeated tokens due to probability distribution collapse","No adaptive temperature — the model cannot adjust sampling dynamically based on context or confidence","Sampling parameters are request-level only — cannot be changed mid-generation without restarting"],"requires":["Understanding of temperature semantics (0.0 = deterministic, 1.0 = standard softmax, >1.0 = more random)","Experimentation to find optimal parameters for specific use cases","Client-side parameter validation to prevent invalid values (e.g., negative temperature)"],"input_types":["temperature: float (0.0 to 2.0+, typical range 0.0-1.0)","top_p: float (0.0 to 1.0, nucleus sampling threshold)","top_k: integer (1 to vocab_size, top-k sampling limit)"],"output_types":["text with varying diversity/creativity based on parameter settings"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_6","uri":"capability://text.generation.language.maximum.token.length.configuration.for.context.window.management","name":"maximum token length configuration for context window management","description":"ERNIE-4.5-300B-A47B allows clients to specify max_tokens parameter, controlling the maximum length of generated completions. This enables developers to enforce output length constraints without post-processing, useful for fitting responses into UI constraints or limiting API costs. The model respects the max_tokens limit during generation, stopping early if the limit is reached before natural completion.","intents":["Limit API costs by capping output tokens per request","Fit model outputs into fixed UI containers (e.g., Twitter-like character limits, mobile screens)","Implement safety guardrails preventing runaway generation or token exhaustion attacks"],"best_for":["Cost-conscious teams needing predictable token budgets per request","Frontend developers building UIs with fixed space constraints","Security teams implementing rate limiting and abuse prevention"],"limitations":["Hard cutoff at max_tokens may truncate responses mid-sentence or mid-thought, reducing coherence","No graceful degradation — the model stops generation abruptly rather than summarizing or concluding naturally","max_tokens is a hard limit on output only; input tokens (context) are not affected, so large prompts still consume quota","No token counting API — developers must estimate token usage manually or use external tokenizers, introducing discrepancies"],"requires":["Understanding of token counting (rough estimate: 1 token ≈ 4 characters in English)","Client-side logic to handle truncated responses gracefully","Optional: external tokenizer (e.g., tiktoken) for accurate token counting before API calls"],"input_types":["max_tokens: integer (1 to model's max context, typical 2000-4000)"],"output_types":["text completion truncated to max_tokens length"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-300b-a47b__cap_7","uri":"capability://text.generation.language.stop.sequence.configuration.for.controlled.generation.termination","name":"stop sequence configuration for controlled generation termination","description":"ERNIE-4.5-300B-A47B supports stop_sequences parameter allowing developers to specify custom tokens or strings that trigger generation termination. When the model generates a stop sequence, output is immediately halted and returned, enabling natural conversation boundaries (e.g., stopping at newlines for single-line outputs) or domain-specific delimiters without post-processing.","intents":["Implement turn-taking in multi-agent conversations by stopping at specific delimiters (e.g., '[END]')","Generate single-line outputs (function names, variable names) by stopping at newline characters","Extract structured data by stopping at closing delimiters (e.g., '}' for JSON objects)"],"best_for":["Developers building structured output systems where natural boundaries are known in advance","Teams implementing multi-agent dialogue requiring explicit turn delimiters","Researchers studying controlled generation and output structure"],"limitations":["Stop sequences are exact string matches — no regex or pattern matching support","Multiple stop sequences may conflict if one is a prefix of another, causing unexpected early termination","Stop sequences are applied after token generation, so the model may generate partial tokens before stopping","No priority ordering — if multiple stop sequences appear in output, the first encountered is used"],"requires":["Knowledge of model's tokenization to predict when stop sequences will be generated","Testing to ensure stop sequences don't accidentally match common output patterns","Client-side handling for cases where stop sequences are never encountered (timeout/max_tokens reached first)"],"input_types":["stop_sequences: array of strings (e.g., ['\\n', '[END]', '}'])"],"output_types":["text completion terminated at the first matching stop sequence"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or direct Baidu API credentials","HTTP/2 capable client library (async recommended for production throughput)","Context window 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pruning or fine-tuning individual experts without full model retraining","Context window is finite — conversations exceeding ~4K-8K tokens require manual summarization or truncation","No native conversation persistence — each API call must include full history, increasing payload size and latency for long conversations","Role-based message handling may not generalize to non-English languages with different grammatical role structures","No built-in conversation branching or hypothetical reasoning ('what if' scenarios require explicit prompt engineering)","Instruction-following quality degrades with ambiguous or contradictory prompts — no built-in conflict resolution","Complex multi-step instructions may require explicit chain-of-thought prompting to achieve reliable execution","builder identity is not verified yet","no observed match outcomes 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