How to pick the right model for each task and save money

Using one expensive frontier model for everything is the most common way teams overpay for AI. A model that excels at multi-step refactoring is overkill for classifying a support ticket — and you pay for that overkill on every call. The fix is simple: match the model to the job. Here is how to do that without rewriting your stack.

Start with the task, not the model

Before reaching for the "best" model, describe what the task actually needs:

• Latency — does a user wait on this response?
• Reasoning depth — is it lookup/formatting, or genuine multi-step logic?
• Context length — a tweet, or a 200-page contract?
• Volume — ten calls a day, or ten thousand?
• Tolerance for error — a draft a human reviews, or an autonomous action?

High volume plus low reasoning depth is the classic case for a small, cheap model. Low volume plus high stakes justifies a frontier model. Most workloads are a mix, which is exactly why one model rarely fits all of them.

A rough task-to-tier map

Treat this as a starting point, not gospel. Models change monthly, so check current capabilities on /models and put two candidates head-to-head on /compare before committing.

The trick: switching models should cost nothing

Most people stick to one model because trying another means new SDKs, new keys, and new billing. That friction is what keeps the expensive default in place.

AnyModel removes it. You get one OpenAI-compatible endpoint and one API key that reaches every major family — GPT, Claude, Gemini, DeepSeek, GLM, Kimi, Qwen, and Grok. To switch models you change the model id and nothing else:

curl https://anymodel.org/v1/chat/completions \
  -H "Authorization: Bearer $ANYMODEL_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash",
    "messages": [{"role": "user", "content": "Classify this ticket: ..."}]
  }'

Send the cheap classification call to a small model, then point the hard reasoning step at claude-opus or a GPT model — same key, same base URL, same code path. That is what makes per-task routing practical instead of theoretical.

A simple routing pattern

You do not need a fancy framework. A tiny dispatcher gets most of the savings:

1. Default to cheap. Route every request to a small model first.
2. Escalate on signal. Bump to a mid or frontier model only when the input is long, the task is flagged complex, or the cheap model returns low confidence.
3. Cache and dedupe. Identical or near-identical prompts should not hit the API twice.

Even a crude version of this — small model for 80% of traffic, frontier for the 20% that needs it — often cuts spend by half or more versus sending everything to the top tier.

Try it where you already work

If you use a CLI, wire it up in one line (works for codex, claude, opencode, and hermes):

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 the Gemini CLI, do a manual OpenAI-compatible setup: set the base URL to https://anymodel.org/v1 and paste your key.

Handling sensitive prompts? Turn on Ghost Mode for zero-retention keys — we keep only a token counter, not your prompts or responses. (The model provider still receives the prompt, so this is not absolute privacy, but nothing is stored on our side.)

Bottom line

Saving money on AI is mostly a routing problem, not a discount problem. Pick the smallest model that clears the bar for each task, escalate only when you must, and keep switching cheap. For more workflow ideas, browse the blog.

You can test all of this for free: create an account for 1,000,000 tokens on signup — 6,000,000 total if you link Telegram — with no credit card, no subscription, and no minimums. After that it is pay-per-token.