Yolo-Auto
Flat-rate, unmetered OpenAI-compatible API serving Qwen3.6-35B, priced by concurrency instead of tokens
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yolo-auto.comAbout Yolo-Auto
Yolo-Auto sells one idea: an OpenAI-compatible LLM endpoint with no token meter. You point any tool that already speaks the /v1/chat/completions shape at yolo-auto.com, use a Yolo API key, and pay a flat monthly figure instead of watching a per-token counter. The model behind it is Qwen3.6-35B-A3B, a mixture-of-experts open-weights model, and the constraint that replaces token billing is concurrency — each plan buys a number of concurrent units and a context-window ceiling rather than a quantity of tokens. That trade is aimed squarely at agentic workloads, where a coding agent or an autonomous loop can burn an unpredictable number of tokens overnight and produce an invoice nobody budgeted for; a flat plan converts that risk into a fixed line item at the cost of throughput during bursts. The vendor also makes a privacy claim that is unusual for a cheap inference reseller: no routine retention of prompts or responses. Documented compatibility covers Cursor, LangChain, Claude Code, LlamaIndex, Hermes, OpenClaw and the plain OpenAI SDK, which is really just a restatement that anything OpenAI-shaped works. Published counters on the site claim 36.2 billion tokens served across 1 million requests. Single-model availability is the obvious limitation — there is no frontier-model fallback if Qwen3.6 is the wrong tool for a task.
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Key Features
Yolo-Auto Pros & Cons
✅ Pros
- +Removes token-bill anxiety from long-running agent loops for $6–10/month
- +Zero migration cost for anything already using the OpenAI SDK shape
- +Explicit no-retention stance, rare at this price point
⚠️ Cons
- −One model only — no frontier-model fallback when Qwen3.6 is the wrong fit
- −Concurrency, not tokens, becomes the bottleneck; 1 unit on Starter is genuinely serial
- −No free tier to evaluate quality before paying
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