How to test prompts across multiple models at once

Picking a model by reputation is guesswork. The only reliable way to know which model handles your prompt best is to run the same input through several of them and compare the outputs side by side. The problem is usually logistics: separate API keys, separate SDKs, separate billing dashboards, and request formats that never quite match. This guide shows how to collapse all of that into a single workflow.

Why test across models at all

Models have genuinely different strengths. A reasoning-heavy model might nail a tricky extraction task but cost 5x more and run slower. A cheaper model might be perfectly adequate for summarization. Output style, instruction-following, JSON reliability, and refusal behavior all vary. Testing turns a vague "which is best?" into measurable answers for the exact task you care about.

Things worth comparing:

• Quality — does the output actually solve the task?
• Format adherence — valid JSON, correct schema, no extra prose?
• Latency — time to first token and total time.
• Cost — tokens in/out at each model's price.
• Consistency — run the prompt 3-5 times; does the answer hold?

The setup that makes it easy

The friction in multi-model testing is almost always the plumbing. AnyModel removes it by exposing one OpenAI-compatible endpoint that reaches GPT, Claude, Gemini, DeepSeek, GLM, Kimi, Qwen, and Grok. One base URL, one API key. Switching models means changing the model id string — nothing else in your code changes.

That means a loop over a list of model names is your test harness.

for m in gpt-5 claude-opus-4-8 gemini-2.5-pro deepseek-chat; do
  echo "=== $m ==="
  curl -s https://anymodel.org/v1/chat/completions \
    -H "Authorization: Bearer $ANYMODEL_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"model\":\"$m\",\"messages\":[{\"role\":\"user\",\"content\":\"Summarize this in one sentence: ...\"}]}" \
    | jq -r '.choices[0].message.content'
done

Because the request shape is identical for every provider, the only variable is the model id. That is the whole point — you are testing models, not fighting APIs.

A repeatable testing method

1. Fix the prompt. Write the exact system + user message you'll ship. Don't tweak it between models or you're not comparing fairly.
2. Pick a small model set. Start with 3-4 candidates across price tiers rather than all of them.
3. Use the same parameters. Keep temperature, max_tokens, and any tools identical.
4. Run multiple samples. One generation can be a fluke. Three to five per model surfaces consistency.
5. Score against a rubric. Define pass/fail criteria up front (valid JSON, key facts present, tone). Subjective "feels better" is hard to act on later.
6. Record cost and latency. The cheapest acceptable model usually wins in production.

If you want a head start on which models tend to suit which tasks, the model comparison page is a useful reference before you spend tokens.

Testing inside your real tools

Side-by-side curl is great for quick checks, but you'll often want to test inside the tool you actually work in. For supported CLIs (codex, claude, opencode, hermes) one line points them at AnyModel:

bash <(curl -fsSL "https://anymodel.org/i?tool=codex") <YOUR_API_KEY>

Use tool=claude for Claude Code. For editors and clients like Cursor, Windsurf, Zed, Cline, Aider, Continue, or Gemini CLI, do a manual OpenAI-compatible setup: set the base URL to https://anymodel.org/v1 and paste your key. Then flip the model id in that tool's settings to A/B test in your real environment.

Cost and privacy while you experiment

Testing burns tokens, so the economics matter. AnyModel gives you 1,000,000 free tokens on signup, and 6,000,000 total if you link Telegram — no credit card. After that it's pay-per-token with no subscription and no minimums, so an idle account costs nothing.

If your test prompts contain sensitive material, enable Ghost Mode — opt-in, zero-retention keys where prompts and responses aren't stored on our side (only a token counter). Note this is honest, not magic: the underlying model provider still receives the prompt, so it isn't "100% private."

Start comparing

Multi-model testing stops being a chore the moment every model lives behind one endpoint and one key. Define your prompt, loop over a few model ids, score the results, and let the data choose. More walkthroughs live on the blog.

Create a free account and run your first comparison in minutes.