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Yolo-Auto

Flat-rate, unmetered OpenAI-compatible API serving Qwen3.6-35B, priced by concurrency instead of tokens

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paidAll plans are flat monthly with unlimited Qwen3.6-35B-A3B tokens and no daily request cap; what scales is concurrency and context. Starter is $6/month with 1 concurrent unit and a 128K context window. Builder is $10/month, marked best value, with 2 concurrent units per subscription. A Pro tier above that adds further concurrent units and a larger context window. Every plan is cancel-anytime with no per-token overage and no long contract, and the vendor states no prompt or response storage by default. There is no free tier — paid plans start at $6/month.View full pricing →

Visit Yolo-Auto

https://yolo-auto.com/

About 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

OpenAI-compatible /v1/chat/completions endpoint — drop-in for existing tooling
Flat monthly pricing with no per-token metering or overage
Concurrency-unit based plans rather than token quotas
128K context window on the entry tier
No routine prompt or response retention
Verified compatibility with Cursor, Claude Code, LangChain, LlamaIndex and the OpenAI SDK

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

Tags

llm-apiopenai-compatibleqwenflat-rateagentsinference
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