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OpenAI-compatible Chat Completion (Llama 4 Scout / Llama 3.3 70B / gpt-oss-120b / Qwen3)
OpenAI-compatible Chat Completion (Llama 4 Scout / Llama 3.3 70B / gpt-oss-120b / Qwen3) is a paid API for AI agents from agent-data-tools.revmesh2074.workers.dev, paid per call via x402, $0.002/call (dynamic — varies by input), status unknown (last checked 2026-11-04).
Accepts an OpenAI-shaped chat completion request and returns an assistant reply from open models (Llama 4 Scout default at $0.002, or Llama 3.3 70B / gpt-oss-120b at $0.01), paid per-call via x402 or prepaid credits.
Price / call$0.002varies by input
Success—
StatusUnknown
How this service works
OpenAI chat completions request shape, answered by the same models and at the same prices as /ai/chat ($0.002, default) and /ai/chat-large ($0.01; pick it with model "llama-3.3-70b" or "gpt-oss-120b"): send messages (system, user, assistant), get one assistant message back. No streaming. Point an OpenAI SDK at https://agent-data-tools.revmesh2074.workers.dev/v1 with an x402 or MPP fetch wrapper; the unpaid request returns the 402 quote and the paid retry returns the completion. Failed calls are never charged.
Output
Returns an OpenAI-shaped chat completion object with a choices array containing the assistant message (content and optional tool_calls), finish_reason, model name, completion id, created timestamp, and a usage block with prompt_tokens, completion_tokens, total_tokens, and an estimated flag.
Example use cases
Structured JSON extraction from text
“Use the Llama 4 Scout model to extract the company name, founding year, and CEO from this paragraph and return the result as a JSON object: 'Acme Corp was founded in 2010 by Jane Doe, who still serves as CEO today.'”
Function-calling agent step
“Send these messages to Llama 4 Scout with tool use enabled — system: 'You are a data assistant', user: 'What is the weather in Paris?' — and return any tool_calls it emits so I can execute them.”
Large-model reasoning with gpt-oss-120b
“Using the gpt-oss-120b model, give me a step-by-step analysis of the pros and cons of a microservices vs monolith architecture for a startup, max 800 tokens.”
Technical details
Endpoint
GEThttps://agent-data-tools.revmesh2074.workers.dev/v1/chat/completions?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you already have an OpenAI SDK or toolchain and want to point it at open models (Llama 4 Scout, Llama 3.3 70B, gpt-oss-120b, Qwen3) with strict pay-per-call pricing (from $0.002), no subscription, no account, and crypto (x402/USDC) or card payment. It is ideal for agents that need drop-in OpenAI API compatibility with open-weight models, function calling, JSON mode, or structured output at low per-call cost. Prefer the larger model tier (llama-3.3-70b or gpt-oss-120b) for complex reasoning tasks; use the default Llama 4 Scout for cost-sensitive, high-volume inference.
Side effects
- 402 Payment Required returned on first unpaid call — includes x402 quote; retry with payment header to get the completion
- model name not recognized — defaults silently to Llama 4 Scout or may return a 400
- messages array missing or empty — returns 400 validation error
- max_tokens exceeds 1024 — capped or rejected
- temperature outside 0–1.5 range — rejected with validation error
- network timeout on slow inference — no charge on failure
- streaming (stream: true) may not be supported by all callers despite being in schema
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| model | string | @cf/meta/llama-4-scout-17b-16e-instruct (default, $0.002), or llama-3.3-70b / gpt-oss-120b ($0.01) |
| tools | array | OpenAI function tools (Llama 4 Scout, the default model): the answer carries tool_calls |
| stream | boolean | true answers OpenAI server-sent events ending in [DONE] |
| messagesrequired | array | |
| max_tokens | integer | Maximum output tokens (default 400) |
| temperature | number | Default 0.6 |
| tool_choice | — | auto, none, required or one named function |
| response_format | object | {"type":"json_object"} or a json_schema |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| id | string | |
| model | string | |
| usage | object | |
| object | string | |
| choices | array | |
| created | integer |
Reviews
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