An LLM API service that replaces the usage-based APIs of OpenAI and Anthropic with a fixed monthly subscription. It exposes an OpenAI-compatible interface, so you can move existing coding agents and automation scripts over by changing just three things: the base URL, the model name, and the API key. Smart routing sends each request to an appropriate model and provider, and execution is scheduled through a shared pool ─ the combination is what lets you keep agents running for hours without watching per-token pricing. Launched in November 2025.
Key Features
- Flat monthly pricing instead of per-token billing: No per-token fees on any plan. There is no need to track “how much have I spent this month” in a dashboard; the compute budget is fixed at the start of the month
- A three-line swap thanks to the OpenAI-compatible API: Point the base URL at
api.stdcmpt.com/v1, set the model parameter tostandardcompute, and use a key issued from the dashboard. Tools built on the OpenAI SDK need essentially no code changes - Works with major coding agents: Usable from agents that speak OpenAI-compatible SDKs, including Claude Code, Cursor, Cline, GitHub Copilot, Windsurf, Aider, and Continue
- Frontier models from several labs in one place: Anthropic’s Claude Opus 5, OpenAI’s GPT-5.6 Sol, and Google’s Gemini 3.1 Pro, plus open models such as GLM 5.2, Kimi K3, and MiniMax M3. New releases are reviewed and added within days of availability
- Smart routing and provider optimization: Lightweight models handle routine work while frontier models take demanding tasks, and even for the same model each request goes to whichever provider is fastest or cheapest. This is the mechanism behind “more compute per dollar”
- Pacing to avoid running dry: When the monthly compute budget is close to being exhausted, an optional pacing mode eases response speed instead of halting requests
- Prompts and outputs are not stored: The company states that neither it nor the providers it routes to store, log, or train on prompts and outputs, and that providers which train on API traffic are excluded from the network
Pricing
| Plan | Monthly price | Highlights |
|---|---|---|
| Economy | $39 | Shared execution pool, standard execution speed, smart routing to all models, 1 API key |
| Standard | $89 | Priority scheduling, higher-capacity pool, faster execution, 1 API key |
| Max | $249 | Highest-priority scheduling, maximum execution speed, built for continuous agent fleets, 1 API key |
| Business | Contact sales | Negotiated flat rate sized to team volume, multiple API keys, consolidated invoicing |
No plan charges per token, and every plan reaches the same set of models. The difference between tiers comes down to speed and scheduling priority. A seven-day refund window applies after the first subscription, and cancellation is available from the dashboard at any time.
Pricing is current as of August 2026. Check the official site for the latest information.
Pros & Cons
✅ Pros
- Costs do not spike when agents run for long stretches, so the budget stays predictable
- Being OpenAI-compatible, existing toolchains migrate with almost no rewriting
- One subscription reaches frontier models from several labs, avoiding separate keys and invoices per provider
- Routing is automatic, so you do not have to build your own per-task model-selection logic
- A clear statement that prompts and outputs are not retained
⚠️ Cons
- Once the monthly compute budget is used up, requests stop until renewal unless pacing is enabled
- “Unlimited” here means priority control on top of a shared pool; response speed under load depends on your tier
- For light workloads that would cost only a few dollars a month, usage-based billing is cheaper
- The model name is abstracted to
standardcompute, which fits poorly with requirements that pin a specific model - Tools without an OpenAI-compatible interface cannot connect
Comparison with Similar Services
| Criteria | Standard Compute | OpenRouter | OpenAI API (direct) | Anthropic API (direct) |
|---|---|---|---|---|
| Billing | Flat monthly | Usage-based (credits) | Usage-based | Usage-based |
| Models | Top-tier models from several labs | A wide catalog across providers | OpenAI models only | Anthropic models only |
| Model selection | Mostly automatic routing | Chosen by the caller | Chosen by the caller | Chosen by the caller |
| Interface | OpenAI-compatible | OpenAI-compatible | OpenAI | Anthropic (compatibility layer available) |
| Best suited for | Long-running agents | Trying and comparing many models | Production built around OpenAI | Production built around Anthropic |
Who Is It For
- Developers who run coding agents all day and cannot predict the API bill at the end of the month
- Solo developers and small teams that want compute costs booked as a fixed expense
- People who want to use models from several labs but keep contracts and invoicing consolidated
- Anyone whose automation already runs on OpenAI-compatible SDKs and wants to swap only the endpoint to try it
- Conversely, it is a poor fit if your monthly API usage stays at a few dollars ─ usage-based billing remains cheaper
Summary
Standard Compute aims to replace “using agents while watching the per-token meter” with a fixed monthly budget. Because it is OpenAI-compatible, the cost of trying it is low and a migration experiment is easy to run. At the same time, it is worth understanding that this is not a truly limitless service: it rests on shared-pool priority and a monthly compute budget. A practical approach is to start on the Economy plan, confirm that your workload fits inside the budget, and move up a tier if the speed is not enough.