{"uid":"cap_MF_pZoO4NdoG9eSyXlW45","slug":"agents-zerogpu-ai-zerogpu-chat-completions-openai-compatible-23d2db4f","name":"ZeroGPU Chat Completions (OpenAI-compatible)","description":"OpenAI-style Chat Completions endpoint, authenticated by verifying the\n`zc-signature` HMAC over the raw request bytes. The request is routed by\n`model` using model metadata, and the same orchestration as\n`/v1/chat/completions` runs behind it.\n\nOn success, actual token usage is settled with ZeroClick and reported\nvia the `zc-usage` response header.\n","url":"https://agents.zerogpu.ai/zeroclick/v1/chat/completions?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","required":["model","messages"],"properties":{"model":{"type":"string","example":"LFM2.5-1.2B-Instruct","description":"Model id; routes the request via model metadata. Kebab-cased into a ZeroClick service slug for metering."},"messages":{"type":"array","items":{"type":"object","required":["role","content"],"properties":{"role":{"enum":["system","user","assistant","tool","developer"],"type":"string","description":"A `developer` role is normalized to `system`; unrecognized roles are treated as `user`."},"content":{"type":"string"}}},"minItems":1},"metadata":{"type":"object","description":"Task-specific options. For GLiNER models this carries `usecase`,\n`schema`, `labels`, `threshold`, `mask`, and `replacement_char`. For\nmoderation, `threshold`; for signal-extract, `max_keywords`.\n","additionalProperties":true},"categories":{"type":"array","items":{"type":"string"},"description":"Candidate labels for text-classification models. May also be supplied via a system-role message."},"multilingual":{"type":"boolean","description":"Forwarded to the IAB cloud inference API only when the resolved model supports it."}},"additionalProperties":true},"responseSchema":{"type":"object","properties":{"id":{"type":"string"},"model":{"type":"string"},"usage":{"type":"object","properties":{"total_tokens":{"type":"integer"},"prompt_tokens":{"type":"integer"},"completion_tokens":{"type":"integer"},"prompt_tokens_details":{"type":"object","additionalProperties":true},"completion_tokens_details":{"type":"object","additionalProperties":true}}},"object":{"type":"string","const":"chat.completion"},"choices":{"type":"array","items":{"type":"object","properties":{"index":{"type":"integer"},"message":{"type":"object","properties":{"role":{"type":"string","const":"assistant"},"content":{"type":"string"},"annotations":{"type":"array","items":{}}}},"finish_reason":{"type":"string"}}}},"created":{"type":"integer"}},"description":"OpenAI Chat Completions response. For classification models, `choices[0].message.content` is a JSON-stringified classification result.","additionalProperties":true},"example":null,"exampleRequest":null,"tags":[],"displayCostAmount":"unknown","displayCostAsset":"USD","priceDynamic":true,"priceHint":null,"priceStatus":"unknown_unparsed","priceSource":"registry","requiresHandshake":false,"reviewCount":2,"rating":{"score":"0.67","successRate":"0.83","reviews":2,"stars":"3.7","state":"rated"},"availabilityStatus":"healthy","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"unknown","summary":"price unknown","primary":{"kind":"dynamic","protocol":"mpp","network":"tempo","amountUsd":null,"per":"call","confidence":"unknown"},"accepted":[{"kind":"dynamic","protocol":"mpp","network":"tempo","amountUsd":null,"per":"call","confidence":"unknown"}]},"paymentMethods":[{"uid":"pm_YCXkAVpswL73Rx9J5l_SG","protocol":"mpp","methodType":"crypto","chain":"tempo","mode":"charge","costAmount":"unknown","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":"agents.zerogpu.ai","brandSlug":"agents-zerogpu-ai-x402","brandBaseUrl":"https://agents.zerogpu.ai","brandDocsUrl":null,"whatItDoes":"Generates chat completions via an OpenAI-compatible API across multiple hosted models including DeepSeek-V4-Flash, GLM-5.2, Qwen3-30B, LFM2.5, and Llama-3.1 with per-token billing.","exampleAgentPrompt":"Using the llama-3.1-8b-instruct-fast model, summarize the following article into three bullet points: 'Quantum computing has advanced significantly this year, with IBM releasing a 1000-qubit processor that promises to solve optimization problems previously intractable for classical machines...'","exampleUseCases":[{"title":"Long-context document reasoning","prompt":"I need to analyze this 800,000-token legal contract and identify any clauses that expose us to liability — use DeepSeek-V4-Flash since it supports that 1M token window."},{"title":"Multilingual customer support drafting","prompt":"Using Qwen3-30B-A3B, draft a polite response in Japanese to this customer complaint about a delayed shipment: 'Your delivery was supposed to arrive last week and I still haven't received it.'"},{"title":"High-volume article summarization pipeline","prompt":"For each of these 50 news articles, use llama-3.1-8b-instruct-fast to generate a one-paragraph summary — I need this done fast and cheaply at scale."}],"resultDescription":"Returns an OpenAI-format chat completions response object containing choices[0].message.content with the model's reply, along with token usage breakdown (prompt_tokens, completion_tokens, total_tokens), finish_reason, model name, and a unique completion ID. For classification models, the content field contains a JSON-stringified result.","failureModes":["Invalid model ID returns validation error — must be one of the five supported enum values","Malformed messages array (missing role or content) returns 400 bad request","Exceeding the model's context window returns a context length error","Insufficient payment balance returns 402 payment required","Rate limiting or capacity issues may return 429 or 503 under high load","Empty messages array returns a schema validation error"],"whenToPreferThis":"Choose this endpoint when you need an OpenAI-compatible chat completions interface with model variety and per-token billing — especially when you need a 1M token context window (DeepSeek-V4-Flash or GLM-5.2), multilingual reasoning with function calling (Qwen3-30B-A3B), fast lightweight instruction following (LFM2.5-1.2B-Instruct), or high-volume summarization (llama-3.1-8b-instruct-fast). Prefer this over generic OpenAI when you want transparent pay-per-use pricing, agent-native payment handling, or access to these specific alternative models not available on standard OpenAI.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":39,"lastUsedAt":"2026-10-02T01:11:55.539Z","lastSuccessfullyRanAt":"2026-10-02T01:11:38.038Z","lastHealthCheckAt":"2026-10-12T18:30:02.437Z","isFirstParty":false,"canonicalSlug":"agents-zerogpu-ai-zerogpu-chat-completions-openai-compatible-23d2db4f"}