AnyModelRate limits explained, and how a proxy smooths them out
If you have ever shipped an AI feature only to watch it return 429 Too Many Requests during a demo, you have met rate limits. They are not a bug — they are how providers protect shared infrastructure. But they can quietly cap your throughput, break batch jobs, and turn a smooth product into a flaky one. Here is what they actually are, and how routing through a proxy gateway helps you live with them.
What a rate limit actually counts
Most LLM APIs enforce several limits at once. Hit any single one and you get throttled, even if you are nowhere near the others.
| Limit | What it counts | Typical unit |
|---|---|---|
| RPM | Requests per minute | calls/min |
| TPM | Tokens per minute (input + output) | tokens/min |
| Concurrency | In-flight requests at once | parallel calls |
| RPD / daily caps | Requests or tokens per day | per 24h |
The painful part is that limits are per provider, per account, and often per model tier. Your OpenAI quota does nothing for your Anthropic calls. A new account on one provider may start with tight TPM, while another gives you headroom. Managing this by hand means juggling several dashboards, keys, and retry strategies.
Why you hit them earlier than expected
Three things make limits bite sooner than the numbers suggest:
- Bursty traffic. Real usage is spiky. A limit of 60 RPM does not mean "one request per second" — ten requests arriving in the same second can trip it.
- Token math. TPM counts both prompt and completion tokens. A long system prompt or a big RAG context burns your TPM budget fast, even with few requests.
- Retries that backfire. Naive retry loops re-send failed calls immediately, adding load right when you are already over the line, which extends the throttling window.
How a proxy smooths the curve
A gateway sits between your app and the model providers. Instead of one client hammering one provider's quota, requests flow through a layer that can queue, balance, and fall back. With AnyModel, you point everything at a single OpenAI-compatible endpoint and switch models by changing one string:
curl https://anymodel.org/v1/chat/completions \
-H "Authorization: Bearer $ANYMODEL_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"claude-sonnet","messages":[{"role":"user","content":"hi"}]}'
Change "model" to a GPT, Gemini, DeepSeek, GLM, Kimi, Qwen, or Grok id and nothing else in your code moves. That single-endpoint design is what makes smoothing practical:
1. Spread load across models
When one model's tier is saturated, you can route the same request to a comparable model from a different provider. Because the request shape is identical, failover is a config change, not a rewrite. The model comparison page helps you pick sensible substitutes by capability and cost.
2. One key, one budget
Instead of tracking RPM and TPM across many provider dashboards, you have one API key reaching all models and one token balance to watch. Less context-switching means fewer surprises.
3. Backpressure instead of hard failures
A proxy can absorb short bursts and pace them out, so a momentary spike does not become a wall of 429s for your users.
A realistic note
A proxy smooths limits — it does not delete them. The underlying providers still meter usage, and a model you fall back to has its own quota. The win is operational: one integration, graceful degradation across providers, and a single place to reason about throughput instead of five.
Getting started in one line
If you use Codex, Claude Code, OpenCode, or Hermes, drop in the endpoint with one command:
bash <(curl -fsSL "https://anymodel.org/i?tool=codex") <YOUR_API_KEY>
Use tool=claude for Claude Code. For Cursor, Windsurf, Zed, Cline, Aider, Continue, or Gemini CLI, set the base URL to https://anymodel.org/v1 and paste your key into the OpenAI-compatible provider field.
You start with 1,000,000 free tokens, rising to 6,000,000 if you link Telegram — no credit card. After that it is pay-per-token with no subscription and no minimums. Privacy-sensitive workloads can enable Ghost Mode, our opt-in zero-retention setting where we store only a token counter, not your prompts or responses (the model provider still receives the prompt, so it is not absolute privacy). For more guides, see the blog.
Stop fighting 429s across five dashboards. Create a free account and route everything through one endpoint.