{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cerebras-api","slug":"cerebras-api","name":"Cerebras API","type":"api","url":"https://cerebras.ai","page_url":"https://unfragile.ai/cerebras-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cerebras-api__cap_0","uri":"capability://text.generation.language.wafer.scale.inference.acceleration.for.llm.token.generation","name":"wafer-scale inference acceleration for llm token generation","description":"Executes LLM inference on custom wafer-scale silicon chips that eliminate memory bottlenecks inherent in GPU-based systems. The architecture achieves 2000+ tokens/second throughput by distributing computation across a single monolithic die rather than relying on discrete GPU memory hierarchies. Supports streaming token generation for real-time applications, with claimed 20x faster inference than cloud GPU providers for equivalent model sizes.","intents":["I need to generate text completions at the lowest possible latency for real-time user-facing applications","I want to reduce infrastructure costs by using more efficient hardware than GPU clouds","I need to serve high-throughput inference workloads without memory bandwidth constraints","I'm building conversational AI that requires sub-second response times"],"best_for":["teams building latency-sensitive LLM applications (chatbots, real-time code generation, voice AI)","companies with high-volume inference workloads seeking cost-per-token optimization","developers migrating from GPU-based inference to custom silicon solutions"],"limitations":["Performance claims (2000+ tokens/sec, 20x faster) are unverified and include disclaimers that results vary by workload, configuration, and testing methodology","No documented context window limits or maximum input token constraints","Throughput advantage may not materialize for small batch sizes or latency-insensitive workloads","Custom hardware lock-in — cannot easily migrate to alternative providers without code changes"],"requires":["API key from Cerebras (obtained via free tier signup or paid subscription)","Network connectivity to Cerebras inference endpoints (regional availability unknown)","OpenAI-compatible client library (e.g., OpenAI Python SDK) or direct HTTP/REST client"],"input_types":["text prompts (format and maximum length undocumented)","conversation history (multi-turn format compatible with OpenAI API)"],"output_types":["text completions (streaming or non-streaming, format undocumented)","token counts and usage metrics"],"categories":["text-generation-language","hardware-acceleration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_1","uri":"capability://tool.use.integration.openai.compatible.api.endpoint.for.drop.in.model.substitution","name":"openai-compatible api endpoint for drop-in model substitution","description":"Exposes Cerebras inference as an OpenAI-compatible REST API, allowing developers to swap Cerebras as a backend provider without modifying application code. Implements the same request/response schemas, authentication patterns, and error handling conventions as OpenAI's API, enabling use of existing OpenAI client libraries (Python, Node.js, etc.) against Cerebras infrastructure. Endpoint structure, specific HTTP methods, and payload schemas are not documented.","intents":["I want to use Cerebras inference without rewriting my OpenAI-integrated application code","I need to compare Cerebras performance against OpenAI by swapping API endpoints","I'm building a multi-provider LLM abstraction layer and need OpenAI-compatible backends","I want to migrate from OpenAI to Cerebras with minimal code changes"],"best_for":["developers with existing OpenAI integrations seeking to evaluate Cerebras performance","teams building provider-agnostic LLM applications with pluggable backends","companies looking to reduce costs by switching from OpenAI to Cerebras"],"limitations":["API endpoint URLs, HTTP method specifications, and request/response schemas are not documented — compatibility is claimed but not formally specified","No documentation on which OpenAI API features are supported (streaming, function calling, vision, etc.)","Error response formats and error codes are undocumented, making error handling uncertain","No SDK provided by Cerebras — relies entirely on third-party OpenAI client libraries"],"requires":["OpenAI Python SDK (pip install openai) or equivalent Node.js/other language client","Cerebras API key (obtained from free tier or paid subscription)","Knowledge of OpenAI API conventions (request format, authentication header structure)"],"input_types":["JSON request bodies compatible with OpenAI chat completions API","text prompts and conversation histories"],"output_types":["JSON response bodies compatible with OpenAI API schema","streaming token responses (if supported)"],"categories":["tool-use-integration","api-compatibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_2","uri":"capability://text.generation.language.multi.model.inference.routing.across.open.source.llm.families","name":"multi-model inference routing across open-source llm families","description":"Provides access to multiple open-source LLM families (Llama, GLM, Qwen, GPT-OSS) deployed on Cerebras hardware, allowing developers to select models by family and size. Routing logic determines which model executes on the wafer-scale infrastructure based on request parameters. Specific model versions, context windows, training data, and capability differences are not documented. Default model selection behavior is unknown.","intents":["I need to choose between different open-source models (Llama, Qwen, GLM) for my use case","I want to compare model quality and speed tradeoffs on the same hardware","I'm building an application that needs to switch models based on input complexity or cost constraints","I need access to specific model families without managing separate infrastructure"],"best_for":["developers evaluating open-source models without local GPU infrastructure","teams building multi-model applications with dynamic model selection logic","researchers comparing model performance across Llama, Qwen, and GLM families"],"limitations":["No documentation on model versions, training data, or capability differences — only family names listed (Llama, GLM-4.7, GPT-OSS 120B, QWEN3 Instruct, Codex-Spark)","Context window limits per model are undocumented, making it impossible to determine which models suit long-context applications","No information on model-specific performance characteristics or latency profiles","Model availability and deprecation policies are undocumented — no guarantee of long-term access to specific versions","No fine-tuning or custom model weight support documented for inference API (available only in Enterprise tier)"],"requires":["Cerebras API key","Knowledge of available model names and identifiers (not formally documented)","OpenAI-compatible client library with model parameter support"],"input_types":["text prompts","model identifier string (e.g., 'llama-2-70b', 'qwen3-instruct')"],"output_types":["text completions from selected model","model metadata (if available via API)"],"categories":["text-generation-language","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_3","uri":"capability://tool.use.integration.tier.based.rate.limiting.with.relative.performance.guarantees","name":"tier-based rate limiting with relative performance guarantees","description":"Implements three-tier rate limiting (Free, Developer, Enterprise) with relative performance differentiation but no absolute rate limit numbers documented. Free tier provides baseline access to all models with unspecified rate limits. Developer tier ($10+ minimum) offers 10x higher rate limits than free tier (absolute numbers unknown). Enterprise tier provides custom rate limits negotiated with sales. Specific tokens-per-second or requests-per-minute limits are not published, making capacity planning difficult.","intents":["I need to understand what rate limits apply to my tier before building production applications","I want to upgrade from free to developer tier and need to know the performance improvement","I'm planning enterprise deployment and need to negotiate custom rate limits","I need to estimate how many concurrent users my tier can support"],"best_for":["developers prototyping with free tier who need to understand upgrade paths","small teams evaluating Cerebras with developer tier subscriptions","enterprises with custom workload requirements"],"limitations":["Rate limits are expressed only as relative multipliers (10x higher for Developer vs Free) with no absolute numbers, making capacity planning impossible without contacting support","No documentation on rate limit reset windows, burst allowances, or throttling behavior","Free tier rate limits are completely unspecified — no guidance on whether free tier is suitable for production or testing only","No per-model rate limits documented — unclear if all models share a single quota or have separate limits","No documentation on how rate limits interact with token throughput (2000+ tokens/sec claim) — unclear if this is per-request or aggregate throughput"],"requires":["Cerebras account (free signup or paid subscription)","API key associated with specific tier","Direct communication with Cerebras support for absolute rate limit numbers"],"input_types":["API requests to inference endpoints"],"output_types":["HTTP 429 (Too Many Requests) responses when rate limits exceeded","Rate limit headers in responses (format undocumented)"],"categories":["tool-use-integration","rate-limiting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_4","uri":"capability://code.generation.editing.subscription.based.token.quota.management.for.code.generation.workloads","name":"subscription-based token quota management for code generation workloads","description":"Offers Cerebras Code product as separate subscription tiers (Pro: $50/month for 24M tokens/day, Max: $200/month for 120M tokens/day) with fixed daily token allowances. Quota resets daily and applies specifically to code generation tasks. Pricing is presented as subscription cost per month rather than per-token, simplifying budgeting but reducing flexibility for variable workloads. Pro tier is marked 'sold out' on pricing page.","intents":["I need predictable monthly costs for code generation without per-token billing uncertainty","I want to estimate daily code generation capacity (e.g., 24M tokens/day for Pro tier)","I'm choosing between Pro ($50/month) and Max ($200/month) tiers based on my team's code generation volume","I need to understand if my daily code generation workload fits within the tier quota"],"best_for":["development teams with predictable daily code generation volumes","companies seeking fixed monthly budgets for AI-assisted coding","teams using Cerebras Code IDE integrations (VS Code, JetBrains, etc.)"],"limitations":["Pro tier ($50/month, 24M tokens/day) is marked 'sold out' — availability is uncertain","Quota is daily, not monthly — if a team uses 25M tokens on day 1, they exceed the Pro tier limit and must upgrade or wait until next day","No documentation on quota rollover, carryover, or burst allowances — unclear if unused daily quota is lost","Pricing is presented as 'value' ($48/day for Pro tier) but this is marketing math, not actual per-token cost — actual per-token rate is undocumented","Separate from inference API pricing — unclear if Code tier tokens count against inference API quotas or are isolated","No documentation on which models are available under Code subscriptions vs inference API"],"requires":["Cerebras Code subscription (Pro or Max tier)","IDE integration (VS Code, JetBrains, or other supported editor)","API key associated with Code subscription"],"input_types":["code context from IDE","code completion requests"],"output_types":["code suggestions and completions","token usage tracking"],"categories":["code-generation-editing","pricing-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_5","uri":"capability://text.generation.language.voice.response.generation.with.streaming.audio.output","name":"voice response generation with streaming audio output","description":"Enables LLM inference to generate voice responses in real-time, supporting conversational AI applications that require audio output. The documentation claims 'instant, accurate voice responses' and 'conversations that flow,' suggesting streaming audio generation with low latency. Implementation details (text-to-speech engine, supported languages, audio formats, streaming protocol) are not documented.","intents":["I'm building a voice assistant that needs to respond to user queries with natural-sounding speech","I need real-time audio streaming for conversational AI without buffering delays","I want to combine LLM inference and voice synthesis in a single API call","I'm creating a voice-first application that requires sub-second response times"],"best_for":["developers building voice assistants and conversational AI","teams creating voice-first interfaces for accessibility","applications requiring real-time audio streaming (e.g., live customer support bots)"],"limitations":["No documentation on supported languages, accents, or voice options","Audio format specifications are undocumented (MP3, WAV, Opus, etc.)","Streaming protocol is undocumented — unclear if WebSocket, Server-Sent Events, or HTTP chunked encoding is used","No information on latency profile for voice generation — 'instant' is marketing language without concrete millisecond targets","No documentation on voice customization, speaker identity, or prosody control","Unclear if voice generation is included in inference API or requires separate Cerebras Code subscription"],"requires":["Cerebras API key","Audio playback capability on client side","Support for streaming audio protocol (format undocumented)"],"input_types":["text prompts or conversation history"],"output_types":["streaming audio (format and codec undocumented)","text transcription (if applicable)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_6","uri":"capability://planning.reasoning.multi.agent.orchestration.for.complex.reasoning.workflows","name":"multi-agent orchestration for complex reasoning workflows","description":"Supports multi-agent systems and complex reasoning tasks, with claims of 'complex reasoning in under a second.' The capability appears to enable chaining multiple LLM calls or agent interactions on Cerebras hardware. Implementation details (agent framework, state management, inter-agent communication protocol, reasoning patterns) are not documented. Unclear whether this is a native Cerebras feature or compatibility with external agent frameworks.","intents":["I need to build multi-step reasoning workflows where agents collaborate to solve complex problems","I want to run multi-agent systems with sub-second latency for interactive applications","I'm implementing chain-of-thought reasoning that requires multiple LLM calls in sequence","I need to coordinate multiple specialized agents (e.g., planner, executor, validator) efficiently"],"best_for":["developers building complex AI systems with multiple reasoning steps","teams implementing agent-based automation workflows","applications requiring real-time multi-step decision making"],"limitations":["No documentation on agent framework compatibility (LangChain, AutoGen, CrewAI, etc.)","No specification of how multi-agent state is managed or persisted","No documentation on inter-agent communication protocol or message format","'Complex reasoning in under a second' is unverified marketing claim without concrete benchmarks or workload definitions","Unclear if multi-agent capability is included in standard inference API or requires enterprise tier","No documentation on failure handling, agent timeouts, or recovery mechanisms"],"requires":["Cerebras API key","Agent framework (if external) or Cerebras-native agent SDK (undocumented)","Understanding of multi-agent architecture and reasoning patterns"],"input_types":["task descriptions or problem statements","agent configurations and role definitions"],"output_types":["final reasoning output","intermediate agent responses (if available)","reasoning trace or execution log (format undocumented)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_7","uri":"capability://tool.use.integration.integration.with.cloud.deployment.platforms.and.model.hubs","name":"integration with cloud deployment platforms and model hubs","description":"Cerebras inference is available through third-party integrations including AWS Marketplace (reseller), OpenRouter (unified API aggregator), Hugging Face Hub (model access), and Vercel (deployment platform). These integrations allow developers to access Cerebras without direct API integration, using existing platform workflows. Integration depth, feature parity, and pricing through each platform are not documented.","intents":["I want to use Cerebras inference through AWS Marketplace without managing separate API keys","I'm using OpenRouter to abstract multiple LLM providers and want to include Cerebras","I need to deploy Cerebras-powered applications on Vercel without custom backend code","I'm browsing models on Hugging Face and want to run them on Cerebras hardware"],"best_for":["AWS customers seeking to purchase Cerebras through existing AWS accounts","developers using OpenRouter for multi-provider LLM abstraction","Vercel users building serverless applications with Cerebras inference","Hugging Face community members exploring model deployment options"],"limitations":["No documentation on feature parity between direct Cerebras API and platform integrations — unclear which capabilities (streaming, voice, multi-agent) are available through each platform","AWS Marketplace pricing and terms are separate from Cerebras direct pricing — cost comparison is undocumented","OpenRouter integration may add latency or cost overhead compared to direct API calls","Vercel integration details are undocumented — unclear if it's a native integration or requires custom code","Hugging Face Hub integration scope is undocumented — unclear which models are available and how inference is routed","No documentation on support channels or SLAs for platform-mediated integrations"],"requires":["Account on relevant platform (AWS, OpenRouter, Vercel, Hugging Face)","Platform-specific authentication and configuration","Cerebras API key (if required by platform)"],"input_types":["platform-specific request formats (varies by integration)"],"output_types":["platform-specific response formats (varies by integration)"],"categories":["tool-use-integration","platform-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_8","uri":"capability://code.generation.editing.ide.integrated.code.completion.with.context.awareness","name":"ide-integrated code completion with context awareness","description":"Provides code completion suggestions directly within development environments (VS Code, JetBrains IDEs, etc.) through Cerebras Code product. The capability integrates with IDE context (current file, project structure, cursor position) to generate contextually relevant code suggestions. Specific context window size, supported languages, and suggestion ranking algorithms are not documented. Integration is available through IDE extensions or plugins.","intents":["I want AI-powered code completion in my IDE without switching to a web interface","I need code suggestions that understand my project context and coding style","I'm using VS Code or JetBrains and want to evaluate Cerebras Code completion quality","I need to generate code snippets quickly while maintaining IDE workflow"],"best_for":["individual developers and small teams using VS Code or JetBrains","teams with Cerebras Code subscriptions (Pro or Max tier)","developers seeking code completion alternatives to GitHub Copilot"],"limitations":["IDE support is limited to VS Code and JetBrains — no documentation on other editors (Vim, Emacs, Sublime, etc.)","Context window size for IDE integration is undocumented — unclear how much project context is sent to Cerebras","Supported programming languages are not listed — unclear if all languages are supported or only popular ones","No documentation on suggestion ranking, filtering, or user preference learning","Pro tier ($50/month) is marked 'sold out' — availability is uncertain","Latency profile for IDE integration is undocumented — unclear if suggestions appear instantly or with noticeable delay"],"requires":["Cerebras Code subscription (Pro or Max tier)","VS Code or JetBrains IDE","IDE extension for Cerebras Code (installation method undocumented)","API key associated with Code subscription"],"input_types":["current file content","cursor position","project context (scope undocumented)"],"output_types":["code completion suggestions","inline code snippets"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__cap_9","uri":"capability://tool.use.integration.cost.optimized.inference.with.claimed.infrastructure.savings","name":"cost-optimized inference with claimed infrastructure savings","description":"Positions Cerebras as a cost-effective alternative to GPU cloud providers, with marketing claims of 'slash AI infrastructure costs' and 'leading price-performance.' The value proposition is based on wafer-scale hardware efficiency reducing per-token costs compared to GPU clusters. Specific cost comparisons, per-token pricing, and infrastructure cost breakdowns are not documented. Pricing is presented through subscription tiers (Free, Developer, Enterprise) rather than transparent per-token rates.","intents":["I need to reduce my AI infrastructure costs compared to OpenAI or GPU cloud providers","I want to understand the total cost of ownership for Cerebras vs alternatives","I'm evaluating whether Cerebras offers better price-performance than my current provider","I need to estimate monthly costs for my inference workload"],"best_for":["cost-conscious teams with high-volume inference workloads","companies evaluating alternatives to OpenAI or Anthropic for cost reduction","teams with predictable inference patterns suitable for subscription-based pricing"],"limitations":["No per-token pricing published — only subscription tiers (Free, Developer, Enterprise) and Code subscriptions (Pro/Max), making cost comparison difficult","Cost comparison claims ('slash costs', '20x faster') are unverified and lack specific benchmarks or workload definitions","No documentation on how costs scale with model size, context length, or throughput","Free tier rate limits are unspecified — unclear if free tier is suitable for production or testing only","Enterprise pricing requires contacting sales — no transparency on volume discounts or custom pricing","No documentation on hidden costs (data transfer, storage, support) beyond API usage"],"requires":["Cerebras account (free or paid)","Ability to estimate your inference workload (tokens/day or requests/day)","Direct communication with Cerebras sales for enterprise pricing"],"input_types":["workload specifications (tokens/day, model size, throughput requirements)"],"output_types":["estimated monthly cost (requires manual calculation or sales consultation)"],"categories":["tool-use-integration","pricing-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebras-api__headline","uri":"capability://tool.use.integration.high.performance.llm.inference.api","name":"high-performance llm inference api","description":"The Cerebras API provides the fastest LLM inference powered by custom wafer-scale chips, serving models like Llama at over 2000 tokens/second, ideal for developers needing rapid AI model responses.","intents":["best LLM inference API","fastest API for AI model inference","LLM API for real-time applications","high-speed inference for machine learning models","API for serving Llama and OpenAI models"],"best_for":["real-time AI applications","high-throughput inference tasks"],"limitations":[],"requires":["API key for access"],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from Cerebras (obtained via free tier signup or paid subscription)","Network connectivity to Cerebras inference endpoints (regional availability unknown)","OpenAI-compatible client library (e.g., OpenAI Python SDK) or direct HTTP/REST client","OpenAI Python SDK (pip install openai) or equivalent Node.js/other language client","Cerebras API key (obtained from free tier or paid subscription)","Knowledge of OpenAI API conventions (request format, authentication header structure)","Cerebras API key","Knowledge of available model names and identifiers (not formally documented)","OpenAI-compatible client library with model parameter support","Cerebras account (free signup or paid subscription)"],"failure_modes":["Performance claims (2000+ tokens/sec, 20x faster) are unverified and include disclaimers that results vary by workload, configuration, and testing methodology","No documented context window limits or maximum input token constraints","Throughput advantage may not materialize for small batch sizes or latency-insensitive workloads","Custom hardware lock-in — cannot easily migrate to alternative providers without code changes","API endpoint URLs, HTTP method specifications, and request/response schemas are not documented — compatibility is claimed but not formally specified","No documentation on which OpenAI API features are supported (streaming, function calling, vision, etc.)","Error response formats and error codes are undocumented, making error handling uncertain","No SDK provided by Cerebras — relies entirely on third-party OpenAI client libraries","No documentation on model versions, training data, or capability differences — only family names listed (Llama, GLM-4.7, GPT-OSS 120B, QWEN3 Instruct, Codex-Spark)","Context window limits per model are undocumented, making it impossible to determine which models suit long-context applications","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.547Z","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=cerebras-api","compare_url":"https://unfragile.ai/compare?artifact=cerebras-api"}},"signature":"d+BcU0eEgVdzrV0zauTi/7tqvZUZMVdb+Bb73+yvH0vO7JPAAX43l0X5KciIei+lBvv4OaesZIHSt3Dzb3RvCg==","signedAt":"2026-06-21T20:54:08.819Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cerebras-api","artifact":"https://unfragile.ai/cerebras-api","verify":"https://unfragile.ai/api/v1/verify?slug=cerebras-api","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"}}